From cdce4eba5b9fcf3c1544a42524112d1f633a8a9d Mon Sep 17 00:00:00 2001 From: "zhangkun9038@dingtalk.com" Date: Thu, 1 May 2025 12:33:24 +0800 Subject: [PATCH] run.sh --- #### **3. 检查目标变量** | 6 + .gitignore | 2 + config_examples/basic.json | 126 + debug.log | 2334 +++++++++++++ docker-compose.yml | 3 +- e6402d0.diff | 407 +++ filtered_output.log | 888 +++++ freqtrade.log | 3066 +++++++++++++++++ freqtrade/abc.py | 336 ++ freqtrade/new.py | 295 ++ freqtrade/old.py | 336 ++ freqtrade/templates/MyDynamicStrategy.py | 185 + freqtrade/templates/optima.py | 69 + freqtrade_utf8.log | 3066 +++++++++++++++++ output.log | 1232 +++++++ output_filted.log | 450 +-- .../backtest-result-2025-05-01_04-27-37.json | 1 + ...ktest-result-2025-05-01_04-27-37.meta.json | 1 + .../backtest-result-2025-05-01_04-27-37.zip | Bin 0 -> 159024 bytes ...-05-01_04-27-37_FreqaiExampleStrategy.json | 32 + ...25-05-01_04-27-37_FreqaiExampleStrategy.py | 336 ++ ...est-result-2025-05-01_04-27-37_config.json | 1 + ...-2025-05-01_04-27-37_market_change.feather | Bin 0 -> 180842 bytes run.sh | 23 + temp_validation.log | 2202 ++++++++++++ your_output_file.log | 3066 +++++++++++++++++ 26 files changed, 18262 insertions(+), 201 deletions(-) create mode 100644 #### **3. 检查目标变量** create mode 100644 config_examples/basic.json create mode 100644 debug.log create mode 100644 e6402d0.diff create mode 100644 filtered_output.log create mode 100644 freqtrade.log create mode 100644 freqtrade/abc.py create mode 100644 freqtrade/new.py create mode 100644 freqtrade/old.py create mode 100644 freqtrade/templates/MyDynamicStrategy.py create mode 100644 freqtrade/templates/optima.py create mode 100644 freqtrade_utf8.log create mode 100644 output.log create mode 100644 result/backtest-result-2025-05-01_04-27-37.json create mode 100644 result/backtest-result-2025-05-01_04-27-37.meta.json create mode 100644 result/backtest-result-2025-05-01_04-27-37.zip create mode 100644 result/backtest-result-2025-05-01_04-27-37_FreqaiExampleStrategy.json create mode 100644 result/backtest-result-2025-05-01_04-27-37_FreqaiExampleStrategy.py create mode 100644 result/backtest-result-2025-05-01_04-27-37_config.json create mode 100644 result/backtest-result-2025-05-01_04-27-37_market_change.feather create mode 100755 run.sh create mode 100644 temp_validation.log create mode 100644 your_output_file.log diff --git a/#### **3. 检查目标变量** b/#### **3. 检查目标变量** new file mode 100644 index 00000000..63f98aa1 --- /dev/null +++ b/#### **3. 检查目标变量** @@ -0,0 +1,6 @@ + +<<<<<<< HEAD +======= +dataframe['target'] = np.where(short_ma > long_ma, 2, + np.where(short_ma < long_ma, 0, 1)) +>>>>>>> Snippet diff --git a/.gitignore b/.gitignore index 2693995d..824ef289 100644 --- a/.gitignore +++ b/.gitignore @@ -120,3 +120,5 @@ target/ docker-compose-*.yml data/ + +!result/ diff --git a/config_examples/basic.json b/config_examples/basic.json new file mode 100644 index 00000000..0b12b7b9 --- /dev/null +++ b/config_examples/basic.json @@ -0,0 +1,126 @@ +{ + "$schema": "https://schema.freqtrade.io/schema.json", + "trading_mode": "spot", + "margin_mode": "isolated", + "max_open_trades": 4, + "stake_currency": "USDT", + "stake_amount": 150, + "startup_candle_count": 30, + "tradable_balance_ratio": 1, + "fiat_display_currency": "USD", + "dry_run": true, + "timeframe": "3m", + "dry_run_wallet": 1000, + "cancel_open_orders_on_exit": true, + "stoploss": -0.05, + "unfilledtimeout": { + "entry": 5, + "exit": 15 + }, + "exchange": { + "name": "okx", + "key": "eca767d4-fda5-4a1b-bb28-49ae18093307", + "secret": "8CA3628A556ED137977DB298D37BC7F3", + "enable_ws": false, + "ccxt_config": { + "enableRateLimit": true, + "rateLimit": 500, + "options": { + "defaultType": "spot" + } + }, + "ccxt_async_config": { + "enableRateLimit": true, + "rateLimit": 500, + "timeout": 20000 + }, + "pair_whitelist": [ + "BTC/USDT", + "SOL/USDT" + ], + "pair_blacklist": [] + }, + "entry_pricing": { + "price_side": "same", + "use_order_book": true, + "order_book_top": 1, + "price_last_balance": 0.0, + "check_depth_of_market": { + "enabled": false, + "bids_to_ask_delta": 1 + } + }, + "exit_pricing": { + "price_side": "other", + "use_order_book": true, + "order_book_top": 1 + }, + "pairlists": [ + { + "method": "StaticPairList" + } + ], + "freqai": { + "enabled": true, + "data_kitchen": { + "fillna": "ffill", + "feature_parameters": { + "DI_threshold": 0.9, + "weight_factor": 0.9 + } + }, + "freqaimodel": "XGBoostRegressor", + "purge_old_models": 2, + "identifier": "test175", + "train_period_days": 30, + "backtest_period_days": 10, + "live_retrain_hours": 0, + "feature_selection": { + "method": "recursive_elimination", + "threshold": 0.01 + }, + "feature_parameters": { + "include_timeframes": ["5m", "1h"], + "include_corr_pairlist": ["BTC/USDT"], + "label_period_candles": 10, + "use_SVM_to_remove_outliers": true, + "principal_component_analysis": true + }, + "model": "XGBoostRegressor", + "data_split_parameters": { + "test_size": 0.2, + "shuffle": true, + "random_state": 42 + }, + "model_training_parameters": { + "n_estimators": 200, + "learning_rate": 0.05, + "max_depth": 5, + "subsample": 0.8, + "colsample_bytree": 0.8, + "objective": "reg:squarederror", + "eval_metric": "rmse", + "early_stopping_rounds": 50 + } + }, + "api_server": { + "enabled": true, + "listen_ip_address": "0.0.0.0", + "listen_port": 8080, + "verbosity": "error", + "enable_openapi": false, + "jwt_secret_key": "6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca", + "ws_token": "6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ", + "CORS_origins": [], + "username": "freqAdmin", + "password": "admin" + }, + "bot_name": "freqtrade", + "initial_state": "running", + "force_entry_enable": false, + "internals": { + "process_throttle_secs": 5, + "heartbeat_interval": 20, + "loglevel": "DEBUG" + } +} diff --git a/debug.log b/debug.log new file mode 100644 index 00000000..c574f5c8 --- /dev/null +++ b/debug.log @@ -0,0 +1,2334 @@ +2025-04-29 01:22:03,335 - freqtrade - INFO - freqtrade 2025.3 +2025-04-29 01:22:03,551 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-04-29 01:22:04,957 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-04-29 01:22:04,958 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ... +2025-04-29 01:22:04,960 - freqtrade.loggers - INFO - Enabling colorized output. +2025-04-29 01:22:04,960 - root - INFO - Logfile configured +2025-04-29 01:22:04,960 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-04-29 01:22:04,961 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-04-29 01:22:04,961 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-04-29 01:22:04,961 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250101-20250420 ... +2025-04-29 01:22:04,998 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-04-29 01:22:04,999 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-04-29 01:22:05,000 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-04-29 01:22:05,000 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250101-20250420 +2025-04-29 01:22:05,001 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-04-29 01:22:05,002 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-04-29 01:22:05,008 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-04-29 01:22:05,008 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-04-29 01:22:05,009 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:22:05,011 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-04-29 01:22:05,012 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-04-29 01:22:05,012 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-04-29 01:22:05,012 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-04-29 01:22:05,017 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-04-29 01:22:05,023 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-04-29 01:22:07,636 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-04-29 01:22:07,657 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-04-29 01:22:07,657 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-04-29 01:22:07,658 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-04-29 01:22:07,658 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-04-29 01:22:07,659 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-04-29 01:22:07,659 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-04-29 01:22:07,659 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-04-29 01:22:07,660 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +'minutes'}. +2025-04-29 01:22:07,660 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-04-29 01:22:07,661 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-04-29 01:22:07,661 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-04-29 01:22:07,661 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-04-29 01:22:07,662 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-04-29 01:22:07,662 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-04-29 01:22:07,662 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-04-29 01:22:07,663 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-04-29 01:22:07,663 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-04-29 01:22:07,663 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-04-29 01:22:07,664 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +'stoploss_on_exchange_interval': 60} +2025-04-29 01:22:07,664 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-04-29 01:22:07,664 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-04-29 01:22:07,664 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-04-29 01:22:07,665 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-04-29 01:22:07,665 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-04-29 01:22:07,665 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-04-29 01:22:07,666 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-04-29 01:22:07,666 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-04-29 01:22:07,666 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-04-29 01:22:07,666 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-04-29 01:22:07,667 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-04-29 01:22:07,667 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-04-29 01:22:07,667 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-04-29 01:22:07,667 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-04-29 01:22:07,668 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:22:07,671 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-04-29 01:22:07,677 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-04-29 01:22:07,678 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:22:07,678 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-04-29 01:22:07,842 - freqtrade.optimize.backtesting - INFO - Loading data from 2024-12-01 21:30:00 up to 2025-04-20 00:00:00 (139 days). +2025-04-29 01:22:07,843 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-04-29 01:22:07,843 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-04-29 01:22:09,440 - matplotlib.font_manager - INFO - generated new fontManager +2025-04-29 01:22:09,642 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-04-29 01:22:09,643 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-04-29 01:22:09,643 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-04-29 01:22:09,643 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-04-29 01:22:09,644 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-04-29 01:22:09,644 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-04-29 01:22:09,645 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-04-29 01:22:09,650 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-04-29 01:22:09,652 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:22:09,654 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:22:09,654 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1735689600_prediction.feather +2025-04-29 01:22:09,754 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:22:09,755 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:22:09,858 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:22:09,859 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:22:09,960 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:22:09,960 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:22:10,084 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:22:10,084 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:22:10,182 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:22:10,182 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:22:10,260 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:10,265 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:22:10,267 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.152831 +1 0.003285 50.152831 +2 0.001898 50.152831 +3 0.000484 50.152831 +4 0.001688 50.152831 +2025-04-29 01:22:10,270 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:10,275 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:22:10,277 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:22:10,281 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1735689600/cb_btc_1735689600 +2025-04-29 01:22:10,281 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:10,297 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:10,298 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:22:15,331 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:15,332 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24624 validation_1-rmse:0.26036 +[1] validation_0-rmse:0.24176 validation_1-rmse:0.25460 +[2] validation_0-rmse:0.23782 validation_1-rmse:0.24904 +[3] validation_0-rmse:0.23408 validation_1-rmse:0.24381 +[4] validation_0-rmse:0.23057 validation_1-rmse:0.23882 +[5] validation_0-rmse:0.22701 validation_1-rmse:0.23409 +[6] validation_0-rmse:0.22400 validation_1-rmse:0.22962 +[7] validation_0-rmse:0.22088 validation_1-rmse:0.22533 +[8] validation_0-rmse:0.21817 validation_1-rmse:0.22130 +[9] validation_0-rmse:0.21491 validation_1-rmse:0.21740 +[10] validation_0-rmse:0.21265 validation_1-rmse:0.21347 +[11] validation_0-rmse:0.20982 validation_1-rmse:0.20978 +[12] validation_0-rmse:0.20747 validation_1-rmse:0.20640 +[13] validation_0-rmse:0.20512 validation_1-rmse:0.20299 +[14] validation_0-rmse:0.20280 validation_1-rmse:0.19966 +[15] validation_0-rmse:0.20012 validation_1-rmse:0.19656 +[16] validation_0-rmse:0.19785 validation_1-rmse:0.19346 +[17] validation_0-rmse:0.19572 validation_1-rmse:0.19054 +[18] validation_0-rmse:0.19400 validation_1-rmse:0.18759 +[19] validation_0-rmse:0.19164 validation_1-rmse:0.18488 +[20] validation_0-rmse:0.18956 validation_1-rmse:0.18205 +[21] validation_0-rmse:0.18746 validation_1-rmse:0.17951 +[22] validation_0-rmse:0.18593 validation_1-rmse:0.17696 +[23] validation_0-rmse:0.18395 validation_1-rmse:0.17465 +[24] validation_0-rmse:0.18249 validation_1-rmse:0.17217 +[25] validation_0-rmse:0.18084 validation_1-rmse:0.16993 +[26] validation_0-rmse:0.17928 validation_1-rmse:0.16771 +[27] validation_0-rmse:0.17776 validation_1-rmse:0.16571 +[28] validation_0-rmse:0.17652 validation_1-rmse:0.16356 +[29] validation_0-rmse:0.17499 validation_1-rmse:0.16166 +[30] validation_0-rmse:0.17371 validation_1-rmse:0.15983 +[31] validation_0-rmse:0.17243 validation_1-rmse:0.15792 +[32] validation_0-rmse:0.17110 validation_1-rmse:0.15628 +[33] validation_0-rmse:0.16996 validation_1-rmse:0.15433 +[34] validation_0-rmse:0.16884 validation_1-rmse:0.15277 +[35] validation_0-rmse:0.16785 validation_1-rmse:0.15090 +[36] validation_0-rmse:0.16682 validation_1-rmse:0.14942 +[37] validation_0-rmse:0.16559 validation_1-rmse:0.14774 +[38] validation_0-rmse:0.16459 validation_1-rmse:0.14628 +[39] validation_0-rmse:0.16356 validation_1-rmse:0.14466 +[40] validation_0-rmse:0.16250 validation_1-rmse:0.14330 +[41] validation_0-rmse:0.16153 validation_1-rmse:0.14201 +[42] validation_0-rmse:0.16059 validation_1-rmse:0.14075 +[43] validation_0-rmse:0.15986 validation_1-rmse:0.13938 +[44] validation_0-rmse:0.15908 validation_1-rmse:0.13822 +[45] validation_0-rmse:0.15810 validation_1-rmse:0.13687 +[46] validation_0-rmse:0.15733 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.15655 validation_1-rmse:0.13458 +[48] validation_0-rmse:0.15580 validation_1-rmse:0.13355 +[49] validation_0-rmse:0.15512 validation_1-rmse:0.13228 +[50] validation_0-rmse:0.15434 validation_1-rmse:0.13121 +[51] validation_0-rmse:0.15363 validation_1-rmse:0.13030 +[52] validation_0-rmse:0.15294 validation_1-rmse:0.12937 +[53] validation_0-rmse:0.15243 validation_1-rmse:0.12818 +[54] validation_0-rmse:0.15170 validation_1-rmse:0.12720 +[55] validation_0-rmse:0.15096 validation_1-rmse:0.12632 +[56] validation_0-rmse:0.15035 validation_1-rmse:0.12538 +[57] validation_0-rmse:0.14977 validation_1-rmse:0.12453 +[58] validation_0-rmse:0.14914 validation_1-rmse:0.12363 +[59] validation_0-rmse:0.14867 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.14819 validation_1-rmse:0.12183 +[61] validation_0-rmse:0.14763 validation_1-rmse:0.12108 +[62] validation_0-rmse:0.14706 validation_1-rmse:0.12035 +[63] validation_0-rmse:0.14648 validation_1-rmse:0.11946 +[64] validation_0-rmse:0.14601 validation_1-rmse:0.11876 +[65] validation_0-rmse:0.14553 validation_1-rmse:0.11808 +[66] validation_0-rmse:0.14506 validation_1-rmse:0.11742 +[67] validation_0-rmse:0.14469 validation_1-rmse:0.11671 +[68] validation_0-rmse:0.14422 validation_1-rmse:0.11604 +[69] validation_0-rmse:0.14381 validation_1-rmse:0.11543 +[70] validation_0-rmse:0.14337 validation_1-rmse:0.11485 +[71] validation_0-rmse:0.14294 validation_1-rmse:0.11398 +[72] validation_0-rmse:0.14260 validation_1-rmse:0.11335 +[73] validation_0-rmse:0.14223 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14190 validation_1-rmse:0.11225 +[75] validation_0-rmse:0.14144 validation_1-rmse:0.11143 +[76] validation_0-rmse:0.14098 validation_1-rmse:0.11052 +[77] validation_0-rmse:0.14062 validation_1-rmse:0.10998 +[78] validation_0-rmse:0.14029 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.13993 validation_1-rmse:0.10888 +[80] validation_0-rmse:0.13958 validation_1-rmse:0.10839 +[81] validation_0-rmse:0.13918 validation_1-rmse:0.10767 +[82] validation_0-rmse:0.13897 validation_1-rmse:0.10720 +[83] validation_0-rmse:0.13864 validation_1-rmse:0.10669 +[84] validation_0-rmse:0.13836 validation_1-rmse:0.10620 +[85] validation_0-rmse:0.13810 validation_1-rmse:0.10573 +[86] validation_0-rmse:0.13782 validation_1-rmse:0.10526 +[87] validation_0-rmse:0.13756 validation_1-rmse:0.10458 +[88] validation_0-rmse:0.13736 validation_1-rmse:0.10420 +[89] validation_0-rmse:0.13708 validation_1-rmse:0.10383 +[90] validation_0-rmse:0.13685 validation_1-rmse:0.10343 +[91] validation_0-rmse:0.13658 validation_1-rmse:0.10298 +[92] validation_0-rmse:0.13646 validation_1-rmse:0.10231 +[93] validation_0-rmse:0.13615 validation_1-rmse:0.10190 +[94] validation_0-rmse:0.13589 validation_1-rmse:0.10154 +[95] validation_0-rmse:0.13572 validation_1-rmse:0.10095 +[96] validation_0-rmse:0.13550 validation_1-rmse:0.10058 +[97] validation_0-rmse:0.13530 validation_1-rmse:0.10026 +[98] validation_0-rmse:0.13513 validation_1-rmse:0.09995 +[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950 +2025-04-29 01:22:16,022 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.74 secs) -------------------- +2025-04-29 01:22:16,023 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:16,682 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:22:16,683 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1736553600_prediction.feather +2025-04-29 01:22:16,685 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:16,691 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:22:16,693 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:22:16,697 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:16,703 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:22:16,704 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:22:16,708 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1736553600/cb_btc_1736553600 +2025-04-29 01:22:16,709 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:16,726 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:16,726 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:22:21,650 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:22:21,652 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:21,652 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26037 validation_1-rmse:0.25324 +[1] validation_0-rmse:0.25572 validation_1-rmse:0.24787 +[2] validation_0-rmse:0.25117 validation_1-rmse:0.24281 +[3] validation_0-rmse:0.24697 validation_1-rmse:0.23802 +[4] validation_0-rmse:0.24328 validation_1-rmse:0.23332 +[5] validation_0-rmse:0.23939 validation_1-rmse:0.22905 +[6] validation_0-rmse:0.23522 validation_1-rmse:0.22484 +[7] validation_0-rmse:0.23148 validation_1-rmse:0.22085 +[8] validation_0-rmse:0.22873 validation_1-rmse:0.21697 +[9] validation_0-rmse:0.22519 validation_1-rmse:0.21317 +[10] validation_0-rmse:0.22206 validation_1-rmse:0.20963 +[11] validation_0-rmse:0.21866 validation_1-rmse:0.20626 +[12] validation_0-rmse:0.21563 validation_1-rmse:0.20296 +[13] validation_0-rmse:0.21313 validation_1-rmse:0.19956 +[14] validation_0-rmse:0.21062 validation_1-rmse:0.19636 +[15] validation_0-rmse:0.20808 validation_1-rmse:0.19339 +[16] validation_0-rmse:0.20570 validation_1-rmse:0.19058 +[17] validation_0-rmse:0.20318 validation_1-rmse:0.18781 +[18] validation_0-rmse:0.20113 validation_1-rmse:0.18518 +[19] validation_0-rmse:0.19934 validation_1-rmse:0.18248 +[20] validation_0-rmse:0.19735 validation_1-rmse:0.18006 +[21] validation_0-rmse:0.19541 validation_1-rmse:0.17744 +[22] validation_0-rmse:0.19336 validation_1-rmse:0.17517 +[23] validation_0-rmse:0.19145 validation_1-rmse:0.17301 +[24] validation_0-rmse:0.18989 validation_1-rmse:0.17058 +[25] validation_0-rmse:0.18782 validation_1-rmse:0.16854 +[26] validation_0-rmse:0.18634 validation_1-rmse:0.16625 +[27] validation_0-rmse:0.18471 validation_1-rmse:0.16430 +[28] validation_0-rmse:0.18312 validation_1-rmse:0.16236 +[29] validation_0-rmse:0.18157 validation_1-rmse:0.16053 +[30] validation_0-rmse:0.17991 validation_1-rmse:0.15849 +[31] validation_0-rmse:0.17839 validation_1-rmse:0.15677 +[32] validation_0-rmse:0.17693 validation_1-rmse:0.15498 +[33] validation_0-rmse:0.17574 validation_1-rmse:0.15336 +[34] validation_0-rmse:0.17469 validation_1-rmse:0.15168 +[35] validation_0-rmse:0.17352 validation_1-rmse:0.15015 +[36] validation_0-rmse:0.17228 validation_1-rmse:0.14868 +[37] validation_0-rmse:0.17127 validation_1-rmse:0.14692 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.14553 +[39] validation_0-rmse:0.16926 validation_1-rmse:0.14420 +[40] validation_0-rmse:0.16821 validation_1-rmse:0.14297 +[41] validation_0-rmse:0.16740 validation_1-rmse:0.14144 +[42] validation_0-rmse:0.16647 validation_1-rmse:0.14020 +[43] validation_0-rmse:0.16548 validation_1-rmse:0.13903 +[44] validation_0-rmse:0.16440 validation_1-rmse:0.13765 +[45] validation_0-rmse:0.16353 validation_1-rmse:0.13652 +[46] validation_0-rmse:0.16269 validation_1-rmse:0.13522 +[47] validation_0-rmse:0.16193 validation_1-rmse:0.13419 +[48] validation_0-rmse:0.16114 validation_1-rmse:0.13311 +[49] validation_0-rmse:0.16043 validation_1-rmse:0.13214 +[50] validation_0-rmse:0.15971 validation_1-rmse:0.13090 +[51] validation_0-rmse:0.15909 validation_1-rmse:0.12992 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.12899 +[53] validation_0-rmse:0.15763 validation_1-rmse:0.12809 +[54] validation_0-rmse:0.15697 validation_1-rmse:0.12724 +[55] validation_0-rmse:0.15631 validation_1-rmse:0.12637 +[56] validation_0-rmse:0.15553 validation_1-rmse:0.12535 +[57] validation_0-rmse:0.15494 validation_1-rmse:0.12456 +[58] validation_0-rmse:0.15452 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.15396 validation_1-rmse:0.12273 +[60] validation_0-rmse:0.15334 validation_1-rmse:0.12196 +[61] validation_0-rmse:0.15274 validation_1-rmse:0.12123 +[62] validation_0-rmse:0.15221 validation_1-rmse:0.12048 +[63] validation_0-rmse:0.15176 validation_1-rmse:0.11953 +[64] validation_0-rmse:0.15133 validation_1-rmse:0.11887 +[65] validation_0-rmse:0.15080 validation_1-rmse:0.11796 +[66] validation_0-rmse:0.15035 validation_1-rmse:0.11734 +[67] validation_0-rmse:0.14995 validation_1-rmse:0.11667 +[68] validation_0-rmse:0.14954 validation_1-rmse:0.11616 +[69] validation_0-rmse:0.14916 validation_1-rmse:0.11535 +[70] validation_0-rmse:0.14887 validation_1-rmse:0.11469 +[71] validation_0-rmse:0.14854 validation_1-rmse:0.11408 +[72] validation_0-rmse:0.14811 validation_1-rmse:0.11334 +[73] validation_0-rmse:0.14766 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14738 validation_1-rmse:0.11231 +[75] validation_0-rmse:0.14697 validation_1-rmse:0.11184 +[76] validation_0-rmse:0.14663 validation_1-rmse:0.11108 +[77] validation_0-rmse:0.14635 validation_1-rmse:0.11058 +[78] validation_0-rmse:0.14591 validation_1-rmse:0.10984 +[79] validation_0-rmse:0.14561 validation_1-rmse:0.10929 +[80] validation_0-rmse:0.14529 validation_1-rmse:0.10875 +[81] validation_0-rmse:0.14510 validation_1-rmse:0.10826 +[82] validation_0-rmse:0.14471 validation_1-rmse:0.10772 +[83] validation_0-rmse:0.14444 validation_1-rmse:0.10725 +[84] validation_0-rmse:0.14420 validation_1-rmse:0.10652 +[85] validation_0-rmse:0.14393 validation_1-rmse:0.10608 +[86] validation_0-rmse:0.14371 validation_1-rmse:0.10567 +[87] validation_0-rmse:0.14342 validation_1-rmse:0.10528 +[88] validation_0-rmse:0.14314 validation_1-rmse:0.10483 +[89] validation_0-rmse:0.14307 validation_1-rmse:0.10439 +[90] validation_0-rmse:0.14273 validation_1-rmse:0.10395 +[91] validation_0-rmse:0.14237 validation_1-rmse:0.10353 +[92] validation_0-rmse:0.14210 validation_1-rmse:0.10318 +[93] validation_0-rmse:0.14186 validation_1-rmse:0.10279 +[94] validation_0-rmse:0.14175 validation_1-rmse:0.10234 +[95] validation_0-rmse:0.14153 validation_1-rmse:0.10204 +[96] validation_0-rmse:0.14142 validation_1-rmse:0.10160 +[97] validation_0-rmse:0.14124 validation_1-rmse:0.10126 +[98] validation_0-rmse:0.14102 validation_1-rmse:0.10068 +[99] validation_0-rmse:0.14079 validation_1-rmse:0.10036 +2025-04-29 01:22:22,365 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.66 secs) -------------------- +2025-04-29 01:22:22,366 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:22,973 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:22:22,973 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1737417600_prediction.feather +2025-04-29 01:22:22,978 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:22,983 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:22:22,985 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:22:22,989 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:22,995 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:22:22,996 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:22:23,001 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1737417600/cb_btc_1737417600 +2025-04-29 01:22:23,001 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:23,018 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:23,019 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:22:27,947 - datasieve.pipeline - INFO - DI tossed 1622 predictions for being too far from training data. +2025-04-29 01:22:27,950 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:27,951 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25769 validation_1-rmse:0.25549 +[1] validation_0-rmse:0.25314 validation_1-rmse:0.24986 +[2] validation_0-rmse:0.24864 validation_1-rmse:0.24456 +[3] validation_0-rmse:0.24486 validation_1-rmse:0.23955 +[4] validation_0-rmse:0.24144 validation_1-rmse:0.23480 +[5] validation_0-rmse:0.23803 validation_1-rmse:0.23024 +[6] validation_0-rmse:0.23468 validation_1-rmse:0.22599 +[7] validation_0-rmse:0.23134 validation_1-rmse:0.22162 +[8] validation_0-rmse:0.22843 validation_1-rmse:0.21773 +[9] validation_0-rmse:0.22560 validation_1-rmse:0.21396 +[10] validation_0-rmse:0.22402 validation_1-rmse:0.21023 +[11] validation_0-rmse:0.22155 validation_1-rmse:0.20680 +[12] validation_0-rmse:0.21899 validation_1-rmse:0.20342 +[13] validation_0-rmse:0.21654 validation_1-rmse:0.20029 +[14] validation_0-rmse:0.21431 validation_1-rmse:0.19719 +[15] validation_0-rmse:0.21282 validation_1-rmse:0.19411 +[16] validation_0-rmse:0.21076 validation_1-rmse:0.19117 +[17] validation_0-rmse:0.20882 validation_1-rmse:0.18835 +[18] validation_0-rmse:0.20695 validation_1-rmse:0.18547 +[19] validation_0-rmse:0.20538 validation_1-rmse:0.18292 +[20] validation_0-rmse:0.20345 validation_1-rmse:0.18038 +[21] validation_0-rmse:0.20148 validation_1-rmse:0.17799 +[22] validation_0-rmse:0.19991 validation_1-rmse:0.17569 +[23] validation_0-rmse:0.19832 validation_1-rmse:0.17350 +[24] validation_0-rmse:0.19658 validation_1-rmse:0.17096 +[25] validation_0-rmse:0.19474 validation_1-rmse:0.16879 +[26] validation_0-rmse:0.19292 validation_1-rmse:0.16665 +[27] validation_0-rmse:0.19134 validation_1-rmse:0.16470 +[28] validation_0-rmse:0.19034 validation_1-rmse:0.16253 +[29] validation_0-rmse:0.18882 validation_1-rmse:0.16068 +[30] validation_0-rmse:0.18736 validation_1-rmse:0.15892 +[31] validation_0-rmse:0.18605 validation_1-rmse:0.15690 +[32] validation_0-rmse:0.18481 validation_1-rmse:0.15521 +[33] validation_0-rmse:0.18346 validation_1-rmse:0.15356 +[34] validation_0-rmse:0.18222 validation_1-rmse:0.15188 +[35] validation_0-rmse:0.18095 validation_1-rmse:0.15028 +[36] validation_0-rmse:0.18015 validation_1-rmse:0.14857 +[37] validation_0-rmse:0.17915 validation_1-rmse:0.14713 +[38] validation_0-rmse:0.17817 validation_1-rmse:0.14573 +[39] validation_0-rmse:0.17723 validation_1-rmse:0.14437 +[40] validation_0-rmse:0.17619 validation_1-rmse:0.14308 +[41] validation_0-rmse:0.17509 validation_1-rmse:0.14176 +[42] validation_0-rmse:0.17407 validation_1-rmse:0.14047 +[43] validation_0-rmse:0.17340 validation_1-rmse:0.13921 +[44] validation_0-rmse:0.17245 validation_1-rmse:0.13806 +[45] validation_0-rmse:0.17212 validation_1-rmse:0.13685 +[46] validation_0-rmse:0.17133 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.17064 validation_1-rmse:0.13451 +[48] validation_0-rmse:0.17004 validation_1-rmse:0.13331 +[49] validation_0-rmse:0.16941 validation_1-rmse:0.13222 +[50] validation_0-rmse:0.16858 validation_1-rmse:0.13123 +[51] validation_0-rmse:0.16786 validation_1-rmse:0.13007 +[52] validation_0-rmse:0.16718 validation_1-rmse:0.12912 +[53] validation_0-rmse:0.16651 validation_1-rmse:0.12806 +[54] validation_0-rmse:0.16592 validation_1-rmse:0.12709 +[55] validation_0-rmse:0.16542 validation_1-rmse:0.12604 +[56] validation_0-rmse:0.16479 validation_1-rmse:0.12523 +[57] validation_0-rmse:0.16426 validation_1-rmse:0.12439 +[58] validation_0-rmse:0.16363 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.16325 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.16289 validation_1-rmse:0.12173 +[61] validation_0-rmse:0.16226 validation_1-rmse:0.12099 +[62] validation_0-rmse:0.16176 validation_1-rmse:0.12010 +[63] validation_0-rmse:0.16144 validation_1-rmse:0.11936 +[64] validation_0-rmse:0.16088 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.16030 validation_1-rmse:0.11786 +[66] validation_0-rmse:0.15991 validation_1-rmse:0.11714 +[67] validation_0-rmse:0.15947 validation_1-rmse:0.11640 +[68] validation_0-rmse:0.15912 validation_1-rmse:0.11574 +[69] validation_0-rmse:0.15874 validation_1-rmse:0.11507 +[70] validation_0-rmse:0.15837 validation_1-rmse:0.11430 +[71] validation_0-rmse:0.15798 validation_1-rmse:0.11365 +[72] validation_0-rmse:0.15763 validation_1-rmse:0.11305 +[73] validation_0-rmse:0.15713 validation_1-rmse:0.11250 +[74] validation_0-rmse:0.15648 validation_1-rmse:0.11177 +[75] validation_0-rmse:0.15619 validation_1-rmse:0.11122 +[76] validation_0-rmse:0.15593 validation_1-rmse:0.11066 +[77] validation_0-rmse:0.15562 validation_1-rmse:0.11007 +[78] validation_0-rmse:0.15519 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.15500 validation_1-rmse:0.10883 +[80] validation_0-rmse:0.15461 validation_1-rmse:0.10835 +[81] validation_0-rmse:0.15417 validation_1-rmse:0.10780 +[82] validation_0-rmse:0.15393 validation_1-rmse:0.10742 +[83] validation_0-rmse:0.15395 validation_1-rmse:0.10634 +[84] validation_0-rmse:0.15359 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.15315 validation_1-rmse:0.10539 +[86] validation_0-rmse:0.15315 validation_1-rmse:0.10440 +[87] validation_0-rmse:0.15278 validation_1-rmse:0.10400 +[88] validation_0-rmse:0.15239 validation_1-rmse:0.10353 +[89] validation_0-rmse:0.15200 validation_1-rmse:0.10310 +[90] validation_0-rmse:0.15182 validation_1-rmse:0.10245 +[91] validation_0-rmse:0.15175 validation_1-rmse:0.10182 +[92] validation_0-rmse:0.15139 validation_1-rmse:0.10138 +[93] validation_0-rmse:0.15105 validation_1-rmse:0.10095 +[94] validation_0-rmse:0.15091 validation_1-rmse:0.10056 +[95] validation_0-rmse:0.15088 validation_1-rmse:0.09964 +[96] validation_0-rmse:0.15065 validation_1-rmse:0.09927 +[97] validation_0-rmse:0.15036 validation_1-rmse:0.09888 +[98] validation_0-rmse:0.15021 validation_1-rmse:0.09852 +[99] validation_0-rmse:0.15004 validation_1-rmse:0.09815 +2025-04-29 01:22:28,711 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.71 secs) -------------------- +2025-04-29 01:22:28,712 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:29,220 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:22:29,221 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1738281600_prediction.feather +2025-04-29 01:22:29,226 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:29,231 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:22:29,233 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:22:29,239 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:29,245 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:22:29,246 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:22:29,251 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1738281600/cb_btc_1738281600 +2025-04-29 01:22:29,251 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:29,267 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:29,268 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:22:34,207 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:34,208 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25046 validation_1-rmse:0.26128 +[1] validation_0-rmse:0.24588 validation_1-rmse:0.25570 +[2] validation_0-rmse:0.24156 validation_1-rmse:0.25047 +[3] validation_0-rmse:0.23757 validation_1-rmse:0.24551 +[4] validation_0-rmse:0.23411 validation_1-rmse:0.24075 +[5] validation_0-rmse:0.23029 validation_1-rmse:0.23637 +[6] validation_0-rmse:0.22707 validation_1-rmse:0.23199 +[7] validation_0-rmse:0.22405 validation_1-rmse:0.22801 +[8] validation_0-rmse:0.22083 validation_1-rmse:0.22420 +[9] validation_0-rmse:0.21768 validation_1-rmse:0.22038 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.21674 +[11] validation_0-rmse:0.21187 validation_1-rmse:0.21322 +[12] validation_0-rmse:0.20911 validation_1-rmse:0.20996 +[13] validation_0-rmse:0.20669 validation_1-rmse:0.20679 +[14] validation_0-rmse:0.20441 validation_1-rmse:0.20366 +[15] validation_0-rmse:0.20250 validation_1-rmse:0.20054 +[16] validation_0-rmse:0.20017 validation_1-rmse:0.19757 +[17] validation_0-rmse:0.19804 validation_1-rmse:0.19490 +[18] validation_0-rmse:0.19618 validation_1-rmse:0.19221 +[19] validation_0-rmse:0.19404 validation_1-rmse:0.18954 +[20] validation_0-rmse:0.19209 validation_1-rmse:0.18666 +[21] validation_0-rmse:0.19014 validation_1-rmse:0.18430 +[22] validation_0-rmse:0.18845 validation_1-rmse:0.18197 +[23] validation_0-rmse:0.18653 validation_1-rmse:0.17972 +[24] validation_0-rmse:0.18468 validation_1-rmse:0.17722 +[25] validation_0-rmse:0.18325 validation_1-rmse:0.17491 +[26] validation_0-rmse:0.18152 validation_1-rmse:0.17284 +[27] validation_0-rmse:0.17999 validation_1-rmse:0.17092 +[28] validation_0-rmse:0.17846 validation_1-rmse:0.16892 +[29] validation_0-rmse:0.17696 validation_1-rmse:0.16709 +[30] validation_0-rmse:0.17558 validation_1-rmse:0.16510 +[31] validation_0-rmse:0.17418 validation_1-rmse:0.16335 +[32] validation_0-rmse:0.17293 validation_1-rmse:0.16161 +[33] validation_0-rmse:0.17159 validation_1-rmse:0.16003 +[34] validation_0-rmse:0.17030 validation_1-rmse:0.15831 +[35] validation_0-rmse:0.16907 validation_1-rmse:0.15681 +[36] validation_0-rmse:0.16796 validation_1-rmse:0.15513 +[37] validation_0-rmse:0.16690 validation_1-rmse:0.15349 +[38] validation_0-rmse:0.16580 validation_1-rmse:0.15204 +[39] validation_0-rmse:0.16492 validation_1-rmse:0.15050 +[40] validation_0-rmse:0.16383 validation_1-rmse:0.14918 +[41] validation_0-rmse:0.16281 validation_1-rmse:0.14788 +[42] validation_0-rmse:0.16176 validation_1-rmse:0.14660 +[43] validation_0-rmse:0.16082 validation_1-rmse:0.14516 +[44] validation_0-rmse:0.15990 validation_1-rmse:0.14395 +[45] validation_0-rmse:0.15891 validation_1-rmse:0.14281 +[46] validation_0-rmse:0.15797 validation_1-rmse:0.14168 +[47] validation_0-rmse:0.15712 validation_1-rmse:0.14040 +[48] validation_0-rmse:0.15632 validation_1-rmse:0.13933 +[49] validation_0-rmse:0.15542 validation_1-rmse:0.13821 +[50] validation_0-rmse:0.15458 validation_1-rmse:0.13705 +[51] validation_0-rmse:0.15404 validation_1-rmse:0.13583 +[52] validation_0-rmse:0.15334 validation_1-rmse:0.13483 +[53] validation_0-rmse:0.15256 validation_1-rmse:0.13387 +[54] validation_0-rmse:0.15190 validation_1-rmse:0.13290 +[55] validation_0-rmse:0.15122 validation_1-rmse:0.13174 +[56] validation_0-rmse:0.15065 validation_1-rmse:0.13080 +[57] validation_0-rmse:0.15006 validation_1-rmse:0.12993 +[58] validation_0-rmse:0.14955 validation_1-rmse:0.12897 +[59] validation_0-rmse:0.14893 validation_1-rmse:0.12814 +[60] validation_0-rmse:0.14843 validation_1-rmse:0.12735 +[61] validation_0-rmse:0.14789 validation_1-rmse:0.12642 +[62] validation_0-rmse:0.14718 validation_1-rmse:0.12561 +[63] validation_0-rmse:0.14659 validation_1-rmse:0.12486 +[64] validation_0-rmse:0.14600 validation_1-rmse:0.12397 +[65] validation_0-rmse:0.14547 validation_1-rmse:0.12324 +[66] validation_0-rmse:0.14499 validation_1-rmse:0.12255 +[67] validation_0-rmse:0.14451 validation_1-rmse:0.12188 +[68] validation_0-rmse:0.14393 validation_1-rmse:0.12114 +[69] validation_0-rmse:0.14346 validation_1-rmse:0.12048 +[70] validation_0-rmse:0.14293 validation_1-rmse:0.11974 +[71] validation_0-rmse:0.14256 validation_1-rmse:0.11893 +[72] validation_0-rmse:0.14212 validation_1-rmse:0.11830 +[73] validation_0-rmse:0.14177 validation_1-rmse:0.11748 +[74] validation_0-rmse:0.14134 validation_1-rmse:0.11686 +[75] validation_0-rmse:0.14101 validation_1-rmse:0.11609 +[76] validation_0-rmse:0.14060 validation_1-rmse:0.11536 +[77] validation_0-rmse:0.14020 validation_1-rmse:0.11484 +[78] validation_0-rmse:0.13983 validation_1-rmse:0.11412 +[79] validation_0-rmse:0.13951 validation_1-rmse:0.11357 +[80] validation_0-rmse:0.13928 validation_1-rmse:0.11273 +[81] validation_0-rmse:0.13889 validation_1-rmse:0.11221 +[82] validation_0-rmse:0.13855 validation_1-rmse:0.11166 +[83] validation_0-rmse:0.13824 validation_1-rmse:0.11114 +[84] validation_0-rmse:0.13808 validation_1-rmse:0.11050 +[85] validation_0-rmse:0.13767 validation_1-rmse:0.10998 +[86] validation_0-rmse:0.13731 validation_1-rmse:0.10947 +[87] validation_0-rmse:0.13716 validation_1-rmse:0.10876 +[88] validation_0-rmse:0.13678 validation_1-rmse:0.10832 +[89] validation_0-rmse:0.13659 validation_1-rmse:0.10782 +[90] validation_0-rmse:0.13629 validation_1-rmse:0.10736 +[91] validation_0-rmse:0.13600 validation_1-rmse:0.10662 +[92] validation_0-rmse:0.13577 validation_1-rmse:0.10613 +[93] validation_0-rmse:0.13541 validation_1-rmse:0.10565 +[94] validation_0-rmse:0.13534 validation_1-rmse:0.10501 +[95] validation_0-rmse:0.13511 validation_1-rmse:0.10453 +[96] validation_0-rmse:0.13483 validation_1-rmse:0.10401 +[97] validation_0-rmse:0.13455 validation_1-rmse:0.10362 +[98] validation_0-rmse:0.13425 validation_1-rmse:0.10323 +[99] validation_0-rmse:0.13402 validation_1-rmse:0.10289 +2025-04-29 01:22:35,089 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.84 secs) -------------------- +2025-04-29 01:22:35,090 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:35,631 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:22:35,632 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1739145600_prediction.feather +2025-04-29 01:22:35,636 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:35,642 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:22:35,644 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:22:35,648 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:35,654 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:22:35,656 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:22:35,660 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1739145600/cb_btc_1739145600 +2025-04-29 01:22:35,660 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:35,677 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:35,677 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:22:40,681 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:40,682 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26428 validation_1-rmse:0.27464 +[1] validation_0-rmse:0.25911 validation_1-rmse:0.26865 +[2] validation_0-rmse:0.25427 validation_1-rmse:0.26296 +[3] validation_0-rmse:0.24970 validation_1-rmse:0.25748 +[4] validation_0-rmse:0.24525 validation_1-rmse:0.25222 +[5] validation_0-rmse:0.24140 validation_1-rmse:0.24725 +[6] validation_0-rmse:0.23748 validation_1-rmse:0.24264 +[7] validation_0-rmse:0.23368 validation_1-rmse:0.23792 +[8] validation_0-rmse:0.23022 validation_1-rmse:0.23363 +[9] validation_0-rmse:0.22695 validation_1-rmse:0.22945 +[10] validation_0-rmse:0.22381 validation_1-rmse:0.22543 +[11] validation_0-rmse:0.22105 validation_1-rmse:0.22154 +[12] validation_0-rmse:0.21818 validation_1-rmse:0.21797 +[13] validation_0-rmse:0.21526 validation_1-rmse:0.21430 +[14] validation_0-rmse:0.21284 validation_1-rmse:0.21101 +[15] validation_0-rmse:0.21034 validation_1-rmse:0.20769 +[16] validation_0-rmse:0.20802 validation_1-rmse:0.20438 +[17] validation_0-rmse:0.20590 validation_1-rmse:0.20136 +[18] validation_0-rmse:0.20386 validation_1-rmse:0.19837 +[19] validation_0-rmse:0.20219 validation_1-rmse:0.19549 +[20] validation_0-rmse:0.20037 validation_1-rmse:0.19283 +[21] validation_0-rmse:0.19826 validation_1-rmse:0.19005 +[22] validation_0-rmse:0.19657 validation_1-rmse:0.18750 +[23] validation_0-rmse:0.19525 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.19373 validation_1-rmse:0.18267 +[25] validation_0-rmse:0.19197 validation_1-rmse:0.18037 +[26] validation_0-rmse:0.19063 validation_1-rmse:0.17799 +[27] validation_0-rmse:0.18897 validation_1-rmse:0.17587 +[28] validation_0-rmse:0.18765 validation_1-rmse:0.17382 +[29] validation_0-rmse:0.18608 validation_1-rmse:0.17185 +[30] validation_0-rmse:0.18456 validation_1-rmse:0.16992 +[31] validation_0-rmse:0.18340 validation_1-rmse:0.16793 +[32] validation_0-rmse:0.18206 validation_1-rmse:0.16616 +[33] validation_0-rmse:0.18077 validation_1-rmse:0.16437 +[34] validation_0-rmse:0.17960 validation_1-rmse:0.16270 +[35] validation_0-rmse:0.17857 validation_1-rmse:0.16105 +[36] validation_0-rmse:0.17748 validation_1-rmse:0.15925 +[37] validation_0-rmse:0.17649 validation_1-rmse:0.15762 +[38] validation_0-rmse:0.17540 validation_1-rmse:0.15611 +[39] validation_0-rmse:0.17427 validation_1-rmse:0.15469 +[40] validation_0-rmse:0.17312 validation_1-rmse:0.15301 +[41] validation_0-rmse:0.17217 validation_1-rmse:0.15169 +[42] validation_0-rmse:0.17119 validation_1-rmse:0.15037 +[43] validation_0-rmse:0.17030 validation_1-rmse:0.14910 +[44] validation_0-rmse:0.16939 validation_1-rmse:0.14786 +[45] validation_0-rmse:0.16851 validation_1-rmse:0.14660 +[46] validation_0-rmse:0.16793 validation_1-rmse:0.14518 +[47] validation_0-rmse:0.16760 validation_1-rmse:0.14365 +[48] validation_0-rmse:0.16674 validation_1-rmse:0.14258 +[49] validation_0-rmse:0.16588 validation_1-rmse:0.14152 +[50] validation_0-rmse:0.16505 validation_1-rmse:0.14051 +[51] validation_0-rmse:0.16437 validation_1-rmse:0.13919 +[52] validation_0-rmse:0.16361 validation_1-rmse:0.13818 +[53] validation_0-rmse:0.16290 validation_1-rmse:0.13715 +[54] validation_0-rmse:0.16217 validation_1-rmse:0.13621 +[55] validation_0-rmse:0.16207 validation_1-rmse:0.13493 +[56] validation_0-rmse:0.16153 validation_1-rmse:0.13395 +[57] validation_0-rmse:0.16077 validation_1-rmse:0.13302 +[58] validation_0-rmse:0.16021 validation_1-rmse:0.13218 +[59] validation_0-rmse:0.15972 validation_1-rmse:0.13117 +[60] validation_0-rmse:0.15954 validation_1-rmse:0.13003 +[61] validation_0-rmse:0.15896 validation_1-rmse:0.12926 +[62] validation_0-rmse:0.15849 validation_1-rmse:0.12848 +[63] validation_0-rmse:0.15801 validation_1-rmse:0.12770 +[64] validation_0-rmse:0.15737 validation_1-rmse:0.12678 +[65] validation_0-rmse:0.15736 validation_1-rmse:0.12578 +[66] validation_0-rmse:0.15684 validation_1-rmse:0.12506 +[67] validation_0-rmse:0.15638 validation_1-rmse:0.12437 +[68] validation_0-rmse:0.15618 validation_1-rmse:0.12336 +[69] validation_0-rmse:0.15581 validation_1-rmse:0.12269 +[70] validation_0-rmse:0.15537 validation_1-rmse:0.12205 +[71] validation_0-rmse:0.15534 validation_1-rmse:0.12117 +[72] validation_0-rmse:0.15485 validation_1-rmse:0.12049 +[73] validation_0-rmse:0.15465 validation_1-rmse:0.11968 +[74] validation_0-rmse:0.15430 validation_1-rmse:0.11906 +[75] validation_0-rmse:0.15386 validation_1-rmse:0.11840 +[76] validation_0-rmse:0.15353 validation_1-rmse:0.11781 +[77] validation_0-rmse:0.15354 validation_1-rmse:0.11697 +[78] validation_0-rmse:0.15325 validation_1-rmse:0.11630 +[79] validation_0-rmse:0.15282 validation_1-rmse:0.11572 +[80] validation_0-rmse:0.15239 validation_1-rmse:0.11514 +[81] validation_0-rmse:0.15226 validation_1-rmse:0.11431 +[82] validation_0-rmse:0.15189 validation_1-rmse:0.11381 +[83] validation_0-rmse:0.15171 validation_1-rmse:0.11316 +[84] validation_0-rmse:0.15136 validation_1-rmse:0.11270 +[85] validation_0-rmse:0.15112 validation_1-rmse:0.11212 +[86] validation_0-rmse:0.15112 validation_1-rmse:0.11140 +[87] validation_0-rmse:0.15074 validation_1-rmse:0.11094 +[88] validation_0-rmse:0.15048 validation_1-rmse:0.11035 +[89] validation_0-rmse:0.15026 validation_1-rmse:0.10983 +[90] validation_0-rmse:0.14989 validation_1-rmse:0.10938 +[91] validation_0-rmse:0.14955 validation_1-rmse:0.10893 +[92] validation_0-rmse:0.14955 validation_1-rmse:0.10815 +[93] validation_0-rmse:0.14933 validation_1-rmse:0.10765 +[94] validation_0-rmse:0.14908 validation_1-rmse:0.10711 +[95] validation_0-rmse:0.14889 validation_1-rmse:0.10668 +[96] validation_0-rmse:0.14853 validation_1-rmse:0.10627 +[97] validation_0-rmse:0.14853 validation_1-rmse:0.10553 +[98] validation_0-rmse:0.14835 validation_1-rmse:0.10513 +[99] validation_0-rmse:0.14818 validation_1-rmse:0.10475 +2025-04-29 01:22:41,413 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.75 secs) -------------------- +2025-04-29 01:22:41,414 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:41,936 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:22:41,937 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740009600_prediction.feather +2025-04-29 01:22:41,941 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:41,948 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:22:41,949 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:22:41,954 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:41,961 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:22:41,962 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:22:41,967 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740009600/cb_btc_1740009600 +2025-04-29 01:22:41,967 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:41,983 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:41,984 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:22:47,004 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:47,005 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27166 validation_1-rmse:0.27726 +[1] validation_0-rmse:0.26708 validation_1-rmse:0.27112 +[2] validation_0-rmse:0.26297 validation_1-rmse:0.26523 +[3] validation_0-rmse:0.25865 validation_1-rmse:0.25959 +[4] validation_0-rmse:0.25494 validation_1-rmse:0.25419 +[5] validation_0-rmse:0.25100 validation_1-rmse:0.24913 +[6] validation_0-rmse:0.24763 validation_1-rmse:0.24437 +[7] validation_0-rmse:0.24441 validation_1-rmse:0.23970 +[8] validation_0-rmse:0.24110 validation_1-rmse:0.23527 +[9] validation_0-rmse:0.23801 validation_1-rmse:0.23102 +[10] validation_0-rmse:0.23492 validation_1-rmse:0.22691 +[11] validation_0-rmse:0.23229 validation_1-rmse:0.22297 +[12] validation_0-rmse:0.22956 validation_1-rmse:0.21923 +[13] validation_0-rmse:0.22707 validation_1-rmse:0.21564 +[14] validation_0-rmse:0.22482 validation_1-rmse:0.21221 +[15] validation_0-rmse:0.22237 validation_1-rmse:0.20891 +[16] validation_0-rmse:0.22030 validation_1-rmse:0.20557 +[17] validation_0-rmse:0.21784 validation_1-rmse:0.20243 +[18] validation_0-rmse:0.21591 validation_1-rmse:0.19949 +[19] validation_0-rmse:0.21399 validation_1-rmse:0.19664 +[20] validation_0-rmse:0.21182 validation_1-rmse:0.19378 +[21] validation_0-rmse:0.20992 validation_1-rmse:0.19110 +[22] validation_0-rmse:0.20821 validation_1-rmse:0.18850 +[23] validation_0-rmse:0.20621 validation_1-rmse:0.18597 +[24] validation_0-rmse:0.20490 validation_1-rmse:0.18353 +[25] validation_0-rmse:0.20318 validation_1-rmse:0.18126 +[26] validation_0-rmse:0.20168 validation_1-rmse:0.17896 +[27] validation_0-rmse:0.19992 validation_1-rmse:0.17679 +[28] validation_0-rmse:0.19865 validation_1-rmse:0.17458 +[29] validation_0-rmse:0.19722 validation_1-rmse:0.17257 +[30] validation_0-rmse:0.19571 validation_1-rmse:0.17039 +[31] validation_0-rmse:0.19429 validation_1-rmse:0.16855 +[32] validation_0-rmse:0.19285 validation_1-rmse:0.16664 +[33] validation_0-rmse:0.19141 validation_1-rmse:0.16488 +[34] validation_0-rmse:0.19022 validation_1-rmse:0.16312 +[35] validation_0-rmse:0.18904 validation_1-rmse:0.16145 +[36] validation_0-rmse:0.18832 validation_1-rmse:0.15973 +[37] validation_0-rmse:0.18723 validation_1-rmse:0.15815 +[38] validation_0-rmse:0.18610 validation_1-rmse:0.15653 +[39] validation_0-rmse:0.18504 validation_1-rmse:0.15503 +[40] validation_0-rmse:0.18402 validation_1-rmse:0.15358 +[41] validation_0-rmse:0.18333 validation_1-rmse:0.15193 +[42] validation_0-rmse:0.18213 validation_1-rmse:0.15058 +[43] validation_0-rmse:0.18176 validation_1-rmse:0.14922 +[44] validation_0-rmse:0.18093 validation_1-rmse:0.14792 +[45] validation_0-rmse:0.18017 validation_1-rmse:0.14667 +[46] validation_0-rmse:0.17928 validation_1-rmse:0.14537 +[47] validation_0-rmse:0.17858 validation_1-rmse:0.14420 +[48] validation_0-rmse:0.17770 validation_1-rmse:0.14306 +[49] validation_0-rmse:0.17695 validation_1-rmse:0.14199 +[50] validation_0-rmse:0.17613 validation_1-rmse:0.14094 +[51] validation_0-rmse:0.17545 validation_1-rmse:0.13979 +[52] validation_0-rmse:0.17490 validation_1-rmse:0.13874 +[53] validation_0-rmse:0.17452 validation_1-rmse:0.13755 +[54] validation_0-rmse:0.17383 validation_1-rmse:0.13663 +[55] validation_0-rmse:0.17327 validation_1-rmse:0.13568 +[56] validation_0-rmse:0.17255 validation_1-rmse:0.13477 +[57] validation_0-rmse:0.17192 validation_1-rmse:0.13382 +[58] validation_0-rmse:0.17138 validation_1-rmse:0.13277 +[59] validation_0-rmse:0.17074 validation_1-rmse:0.13188 +[60] validation_0-rmse:0.17026 validation_1-rmse:0.13089 +[61] validation_0-rmse:0.16969 validation_1-rmse:0.13010 +[62] validation_0-rmse:0.16932 validation_1-rmse:0.12904 +[63] validation_0-rmse:0.16888 validation_1-rmse:0.12818 +[64] validation_0-rmse:0.16849 validation_1-rmse:0.12745 +[65] validation_0-rmse:0.16802 validation_1-rmse:0.12639 +[66] validation_0-rmse:0.16747 validation_1-rmse:0.12567 +[67] validation_0-rmse:0.16710 validation_1-rmse:0.12496 +[68] validation_0-rmse:0.16672 validation_1-rmse:0.12426 +[69] validation_0-rmse:0.16635 validation_1-rmse:0.12331 +[70] validation_0-rmse:0.16597 validation_1-rmse:0.12267 +[71] validation_0-rmse:0.16554 validation_1-rmse:0.12196 +[72] validation_0-rmse:0.16522 validation_1-rmse:0.12121 +[73] validation_0-rmse:0.16481 validation_1-rmse:0.12054 +[74] validation_0-rmse:0.16442 validation_1-rmse:0.11996 +[75] validation_0-rmse:0.16409 validation_1-rmse:0.11939 +[76] validation_0-rmse:0.16375 validation_1-rmse:0.11878 +[77] validation_0-rmse:0.16275 validation_1-rmse:0.11753 +[78] validation_0-rmse:0.16248 validation_1-rmse:0.11692 +[79] validation_0-rmse:0.16215 validation_1-rmse:0.11619 +[80] validation_0-rmse:0.16187 validation_1-rmse:0.11564 +[81] validation_0-rmse:0.16150 validation_1-rmse:0.11493 +[82] validation_0-rmse:0.16123 validation_1-rmse:0.11438 +[83] validation_0-rmse:0.16109 validation_1-rmse:0.11358 +[84] validation_0-rmse:0.16065 validation_1-rmse:0.11304 +[85] validation_0-rmse:0.16038 validation_1-rmse:0.11256 +[86] validation_0-rmse:0.16022 validation_1-rmse:0.11205 +[87] validation_0-rmse:0.16007 validation_1-rmse:0.11158 +[88] validation_0-rmse:0.15945 validation_1-rmse:0.11054 +[89] validation_0-rmse:0.15912 validation_1-rmse:0.11008 +[90] validation_0-rmse:0.15894 validation_1-rmse:0.10937 +[91] validation_0-rmse:0.15868 validation_1-rmse:0.10886 +[92] validation_0-rmse:0.15845 validation_1-rmse:0.10844 +[93] validation_0-rmse:0.15817 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.15789 validation_1-rmse:0.10758 +[95] validation_0-rmse:0.15772 validation_1-rmse:0.10721 +[96] validation_0-rmse:0.15763 validation_1-rmse:0.10676 +[97] validation_0-rmse:0.15751 validation_1-rmse:0.10609 +[98] validation_0-rmse:0.15731 validation_1-rmse:0.10574 +[99] validation_0-rmse:0.15738 validation_1-rmse:0.10531 +2025-04-29 01:22:47,782 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.81 secs) -------------------- +2025-04-29 01:22:47,783 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:48,337 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:22:48,337 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740873600_prediction.feather +2025-04-29 01:22:48,342 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:48,349 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:22:48,350 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:22:48,356 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:48,362 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:22:48,364 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:22:48,368 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740873600/cb_btc_1740873600 +2025-04-29 01:22:48,369 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:48,384 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:48,385 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:22:53,444 - datasieve.pipeline - INFO - DI tossed 2275 predictions for being too far from training data. +2025-04-29 01:22:53,447 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:53,448 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27618 validation_1-rmse:0.28955 +[1] validation_0-rmse:0.27005 validation_1-rmse:0.28323 +[2] validation_0-rmse:0.26414 validation_1-rmse:0.27722 +[3] validation_0-rmse:0.25897 validation_1-rmse:0.27161 +[4] validation_0-rmse:0.25425 validation_1-rmse:0.26622 +[5] validation_0-rmse:0.24886 validation_1-rmse:0.26100 +[6] validation_0-rmse:0.24522 validation_1-rmse:0.25606 +[7] validation_0-rmse:0.24137 validation_1-rmse:0.25132 +[8] validation_0-rmse:0.23765 validation_1-rmse:0.24687 +[9] validation_0-rmse:0.23323 validation_1-rmse:0.24254 +[10] validation_0-rmse:0.22900 validation_1-rmse:0.23827 +[11] validation_0-rmse:0.22588 validation_1-rmse:0.23450 +[12] validation_0-rmse:0.22228 validation_1-rmse:0.23055 +[13] validation_0-rmse:0.21872 validation_1-rmse:0.22698 +[14] validation_0-rmse:0.21492 validation_1-rmse:0.22348 +[15] validation_0-rmse:0.21329 validation_1-rmse:0.22011 +[16] validation_0-rmse:0.21024 validation_1-rmse:0.21686 +[17] validation_0-rmse:0.20823 validation_1-rmse:0.21380 +[18] validation_0-rmse:0.20544 validation_1-rmse:0.21075 +[19] validation_0-rmse:0.20415 validation_1-rmse:0.20787 +[20] validation_0-rmse:0.20143 validation_1-rmse:0.20515 +[21] validation_0-rmse:0.19917 validation_1-rmse:0.20247 +[22] validation_0-rmse:0.19745 validation_1-rmse:0.19994 +[23] validation_0-rmse:0.19508 validation_1-rmse:0.19746 +[24] validation_0-rmse:0.19300 validation_1-rmse:0.19490 +[25] validation_0-rmse:0.19085 validation_1-rmse:0.19254 +[26] validation_0-rmse:0.18898 validation_1-rmse:0.19031 +[27] validation_0-rmse:0.18720 validation_1-rmse:0.18794 +[28] validation_0-rmse:0.18503 validation_1-rmse:0.18584 +[29] validation_0-rmse:0.18314 validation_1-rmse:0.18382 +[30] validation_0-rmse:0.18132 validation_1-rmse:0.18164 +[31] validation_0-rmse:0.17984 validation_1-rmse:0.17967 +[32] validation_0-rmse:0.17818 validation_1-rmse:0.17779 +[33] validation_0-rmse:0.17637 validation_1-rmse:0.17572 +[34] validation_0-rmse:0.17473 validation_1-rmse:0.17399 +[35] validation_0-rmse:0.17338 validation_1-rmse:0.17229 +[36] validation_0-rmse:0.17253 validation_1-rmse:0.17055 +[37] validation_0-rmse:0.17149 validation_1-rmse:0.16883 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.16730 +[39] validation_0-rmse:0.16950 validation_1-rmse:0.16556 +[40] validation_0-rmse:0.16815 validation_1-rmse:0.16412 +[41] validation_0-rmse:0.16704 validation_1-rmse:0.16268 +[42] validation_0-rmse:0.16617 validation_1-rmse:0.16128 +[43] validation_0-rmse:0.16542 validation_1-rmse:0.15970 +[44] validation_0-rmse:0.16438 validation_1-rmse:0.15840 +[45] validation_0-rmse:0.16356 validation_1-rmse:0.15692 +[46] validation_0-rmse:0.16239 validation_1-rmse:0.15574 +[47] validation_0-rmse:0.16153 validation_1-rmse:0.15456 +[48] validation_0-rmse:0.16076 validation_1-rmse:0.15314 +[49] validation_0-rmse:0.15998 validation_1-rmse:0.15201 +[50] validation_0-rmse:0.15946 validation_1-rmse:0.15084 +[51] validation_0-rmse:0.15891 validation_1-rmse:0.14954 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.14847 +[53] validation_0-rmse:0.15764 validation_1-rmse:0.14722 +[54] validation_0-rmse:0.15707 validation_1-rmse:0.14623 +[55] validation_0-rmse:0.15653 validation_1-rmse:0.14527 +[56] validation_0-rmse:0.15583 validation_1-rmse:0.14434 +[57] validation_0-rmse:0.15549 validation_1-rmse:0.14329 +[58] validation_0-rmse:0.15507 validation_1-rmse:0.14241 +[59] validation_0-rmse:0.15468 validation_1-rmse:0.14053 +[60] validation_0-rmse:0.15398 validation_1-rmse:0.13968 +[61] validation_0-rmse:0.15390 validation_1-rmse:0.13864 +[62] validation_0-rmse:0.15360 validation_1-rmse:0.13783 +[63] validation_0-rmse:0.15368 validation_1-rmse:0.13704 +[64] validation_0-rmse:0.15338 validation_1-rmse:0.13624 +[65] validation_0-rmse:0.15273 validation_1-rmse:0.13551 +[66] validation_0-rmse:0.15238 validation_1-rmse:0.13451 +[67] validation_0-rmse:0.15212 validation_1-rmse:0.13290 +[68] validation_0-rmse:0.15191 validation_1-rmse:0.13217 +[69] validation_0-rmse:0.15138 validation_1-rmse:0.13143 +[70] validation_0-rmse:0.15090 validation_1-rmse:0.13071 +[71] validation_0-rmse:0.15082 validation_1-rmse:0.13001 +[72] validation_0-rmse:0.14988 validation_1-rmse:0.12847 +[73] validation_0-rmse:0.14953 validation_1-rmse:0.12783 +[74] validation_0-rmse:0.14924 validation_1-rmse:0.12709 +[75] validation_0-rmse:0.14926 validation_1-rmse:0.12578 +[76] validation_0-rmse:0.14903 validation_1-rmse:0.12499 +[77] validation_0-rmse:0.14851 validation_1-rmse:0.12435 +[78] validation_0-rmse:0.14808 validation_1-rmse:0.12368 +[79] validation_0-rmse:0.14768 validation_1-rmse:0.12305 +[80] validation_0-rmse:0.14741 validation_1-rmse:0.12217 +[81] validation_0-rmse:0.14712 validation_1-rmse:0.12165 +[82] validation_0-rmse:0.14696 validation_1-rmse:0.12110 +[83] validation_0-rmse:0.14686 validation_1-rmse:0.12045 +[84] validation_0-rmse:0.14648 validation_1-rmse:0.11984 +[85] validation_0-rmse:0.14623 validation_1-rmse:0.11923 +[86] validation_0-rmse:0.14606 validation_1-rmse:0.11869 +[87] validation_0-rmse:0.14583 validation_1-rmse:0.11754 +[88] validation_0-rmse:0.14572 validation_1-rmse:0.11710 +[89] validation_0-rmse:0.14537 validation_1-rmse:0.11660 +[90] validation_0-rmse:0.14510 validation_1-rmse:0.11614 +[91] validation_0-rmse:0.14516 validation_1-rmse:0.11514 +[92] validation_0-rmse:0.14480 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.14475 validation_1-rmse:0.11414 +[94] validation_0-rmse:0.14443 validation_1-rmse:0.11374 +[95] validation_0-rmse:0.14409 validation_1-rmse:0.11331 +[96] validation_0-rmse:0.14391 validation_1-rmse:0.11240 +[97] validation_0-rmse:0.14303 validation_1-rmse:0.11154 +[98] validation_0-rmse:0.14274 validation_1-rmse:0.11114 +[99] validation_0-rmse:0.14246 validation_1-rmse:0.11071 +2025-04-29 01:22:54,188 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.82 secs) -------------------- +2025-04-29 01:22:54,189 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:22:54,678 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:22:54,678 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1741737600_prediction.feather +2025-04-29 01:22:54,685 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:54,691 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:22:54,693 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:22:54,699 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:22:54,706 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:22:54,707 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:22:54,712 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1741737600/cb_btc_1741737600 +2025-04-29 01:22:54,712 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:22:54,728 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:22:54,729 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:22:59,740 - datasieve.pipeline - INFO - DI tossed 18 predictions for being too far from training data. +2025-04-29 01:22:59,743 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:22:59,743 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26738 validation_1-rmse:0.26816 +[1] validation_0-rmse:0.26268 validation_1-rmse:0.26258 +[2] validation_0-rmse:0.25808 validation_1-rmse:0.25725 +[3] validation_0-rmse:0.25395 validation_1-rmse:0.25212 +[4] validation_0-rmse:0.24987 validation_1-rmse:0.24723 +[5] validation_0-rmse:0.24633 validation_1-rmse:0.24263 +[6] validation_0-rmse:0.24308 validation_1-rmse:0.23814 +[7] validation_0-rmse:0.23959 validation_1-rmse:0.23402 +[8] validation_0-rmse:0.23612 validation_1-rmse:0.22977 +[9] validation_0-rmse:0.23322 validation_1-rmse:0.22577 +[10] validation_0-rmse:0.23012 validation_1-rmse:0.22207 +[11] validation_0-rmse:0.22730 validation_1-rmse:0.21843 +[12] validation_0-rmse:0.22453 validation_1-rmse:0.21489 +[13] validation_0-rmse:0.22236 validation_1-rmse:0.21145 +[14] validation_0-rmse:0.22000 validation_1-rmse:0.20841 +[15] validation_0-rmse:0.21744 validation_1-rmse:0.20529 +[16] validation_0-rmse:0.21556 validation_1-rmse:0.20225 +[17] validation_0-rmse:0.21331 validation_1-rmse:0.19932 +[18] validation_0-rmse:0.21171 validation_1-rmse:0.19643 +[19] validation_0-rmse:0.21051 validation_1-rmse:0.19382 +[20] validation_0-rmse:0.20880 validation_1-rmse:0.19128 +[21] validation_0-rmse:0.20711 validation_1-rmse:0.18854 +[22] validation_0-rmse:0.20538 validation_1-rmse:0.18612 +[23] validation_0-rmse:0.20350 validation_1-rmse:0.18381 +[24] validation_0-rmse:0.20234 validation_1-rmse:0.18144 +[25] validation_0-rmse:0.20081 validation_1-rmse:0.17917 +[26] validation_0-rmse:0.19918 validation_1-rmse:0.17714 +[27] validation_0-rmse:0.19804 validation_1-rmse:0.17496 +[28] validation_0-rmse:0.19662 validation_1-rmse:0.17304 +[29] validation_0-rmse:0.19580 validation_1-rmse:0.17082 +[30] validation_0-rmse:0.19454 validation_1-rmse:0.16901 +[31] validation_0-rmse:0.19331 validation_1-rmse:0.16691 +[32] validation_0-rmse:0.19234 validation_1-rmse:0.16517 +[33] validation_0-rmse:0.19118 validation_1-rmse:0.16354 +[34] validation_0-rmse:0.19024 validation_1-rmse:0.16175 +[35] validation_0-rmse:0.18915 validation_1-rmse:0.16020 +[36] validation_0-rmse:0.18823 validation_1-rmse:0.15865 +[37] validation_0-rmse:0.18756 validation_1-rmse:0.15712 +[38] validation_0-rmse:0.18698 validation_1-rmse:0.15541 +[39] validation_0-rmse:0.18643 validation_1-rmse:0.15395 +[40] validation_0-rmse:0.18562 validation_1-rmse:0.15265 +[41] validation_0-rmse:0.18516 validation_1-rmse:0.15124 +[42] validation_0-rmse:0.18421 validation_1-rmse:0.14979 +[43] validation_0-rmse:0.18360 validation_1-rmse:0.14850 +[44] validation_0-rmse:0.18275 validation_1-rmse:0.14733 +[45] validation_0-rmse:0.18253 validation_1-rmse:0.14597 +[46] validation_0-rmse:0.18183 validation_1-rmse:0.14470 +[47] validation_0-rmse:0.18111 validation_1-rmse:0.14361 +[48] validation_0-rmse:0.18060 validation_1-rmse:0.14243 +[49] validation_0-rmse:0.18001 validation_1-rmse:0.14134 +[50] validation_0-rmse:0.17953 validation_1-rmse:0.14030 +[51] validation_0-rmse:0.17899 validation_1-rmse:0.13927 +[52] validation_0-rmse:0.17830 validation_1-rmse:0.13817 +[53] validation_0-rmse:0.17770 validation_1-rmse:0.13720 +[54] validation_0-rmse:0.17702 validation_1-rmse:0.13629 +[55] validation_0-rmse:0.17650 validation_1-rmse:0.13531 +[56] validation_0-rmse:0.17625 validation_1-rmse:0.13440 +[57] validation_0-rmse:0.17580 validation_1-rmse:0.13352 +[58] validation_0-rmse:0.17530 validation_1-rmse:0.13268 +[59] validation_0-rmse:0.17486 validation_1-rmse:0.13166 +[60] validation_0-rmse:0.17438 validation_1-rmse:0.13071 +[61] validation_0-rmse:0.17387 validation_1-rmse:0.12991 +[62] validation_0-rmse:0.17356 validation_1-rmse:0.12914 +[63] validation_0-rmse:0.17311 validation_1-rmse:0.12839 +[64] validation_0-rmse:0.17265 validation_1-rmse:0.12767 +[65] validation_0-rmse:0.17209 validation_1-rmse:0.12682 +[66] validation_0-rmse:0.17197 validation_1-rmse:0.12595 +[67] validation_0-rmse:0.17157 validation_1-rmse:0.12506 +[68] validation_0-rmse:0.17131 validation_1-rmse:0.12439 +[69] validation_0-rmse:0.17088 validation_1-rmse:0.12371 +[70] validation_0-rmse:0.17038 validation_1-rmse:0.12298 +[71] validation_0-rmse:0.17009 validation_1-rmse:0.12235 +[72] validation_0-rmse:0.16979 validation_1-rmse:0.12172 +[73] validation_0-rmse:0.16934 validation_1-rmse:0.12118 +[74] validation_0-rmse:0.16902 validation_1-rmse:0.12050 +[75] validation_0-rmse:0.16881 validation_1-rmse:0.11988 +[76] validation_0-rmse:0.16846 validation_1-rmse:0.11928 +[77] validation_0-rmse:0.16809 validation_1-rmse:0.11846 +[78] validation_0-rmse:0.16774 validation_1-rmse:0.11791 +[79] validation_0-rmse:0.16745 validation_1-rmse:0.11738 +[80] validation_0-rmse:0.16717 validation_1-rmse:0.11683 +[81] validation_0-rmse:0.16702 validation_1-rmse:0.11599 +[82] validation_0-rmse:0.16677 validation_1-rmse:0.11535 +[83] validation_0-rmse:0.16649 validation_1-rmse:0.11468 +[84] validation_0-rmse:0.16605 validation_1-rmse:0.11415 +[85] validation_0-rmse:0.16591 validation_1-rmse:0.11350 +[86] validation_0-rmse:0.16560 validation_1-rmse:0.11303 +[87] validation_0-rmse:0.16531 validation_1-rmse:0.11259 +[88] validation_0-rmse:0.16504 validation_1-rmse:0.11185 +[89] validation_0-rmse:0.16485 validation_1-rmse:0.11134 +[90] validation_0-rmse:0.16463 validation_1-rmse:0.11083 +[91] validation_0-rmse:0.16436 validation_1-rmse:0.11041 +[92] validation_0-rmse:0.16412 validation_1-rmse:0.10988 +[93] validation_0-rmse:0.16388 validation_1-rmse:0.10942 +[94] validation_0-rmse:0.16391 validation_1-rmse:0.10881 +[95] validation_0-rmse:0.16357 validation_1-rmse:0.10838 +[96] validation_0-rmse:0.16358 validation_1-rmse:0.10796 +[97] validation_0-rmse:0.16338 validation_1-rmse:0.10756 +[98] validation_0-rmse:0.16339 validation_1-rmse:0.10688 +[99] validation_0-rmse:0.16321 validation_1-rmse:0.10649 +2025-04-29 01:23:00,451 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.74 secs) -------------------- +2025-04-29 01:23:00,452 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:01,085 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:23:01,086 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1742601600_prediction.feather +2025-04-29 01:23:01,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:23:01,101 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:23:01,103 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:23:01,113 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:23:01,120 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:23:01,122 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:23:01,127 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1742601600/cb_btc_1742601600 +2025-04-29 01:23:01,128 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:23:01,152 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:01,155 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:23:06,424 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:23:06,425 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26992 validation_1-rmse:0.26756 +[1] validation_0-rmse:0.26551 validation_1-rmse:0.26201 +[2] validation_0-rmse:0.26111 validation_1-rmse:0.25656 +[3] validation_0-rmse:0.25690 validation_1-rmse:0.25154 +[4] validation_0-rmse:0.25291 validation_1-rmse:0.24683 +[5] validation_0-rmse:0.24933 validation_1-rmse:0.24228 +[6] validation_0-rmse:0.24598 validation_1-rmse:0.23796 +[7] validation_0-rmse:0.24252 validation_1-rmse:0.23392 +[8] validation_0-rmse:0.23953 validation_1-rmse:0.22978 +[9] validation_0-rmse:0.23634 validation_1-rmse:0.22592 +[10] validation_0-rmse:0.23330 validation_1-rmse:0.22229 +[11] validation_0-rmse:0.23059 validation_1-rmse:0.21875 +[12] validation_0-rmse:0.22799 validation_1-rmse:0.21546 +[13] validation_0-rmse:0.22565 validation_1-rmse:0.21212 +[14] validation_0-rmse:0.22329 validation_1-rmse:0.20904 +[15] validation_0-rmse:0.22111 validation_1-rmse:0.20604 +[16] validation_0-rmse:0.21894 validation_1-rmse:0.20318 +[17] validation_0-rmse:0.21715 validation_1-rmse:0.20021 +[18] validation_0-rmse:0.21499 validation_1-rmse:0.19735 +[19] validation_0-rmse:0.21283 validation_1-rmse:0.19480 +[20] validation_0-rmse:0.21109 validation_1-rmse:0.19209 +[21] validation_0-rmse:0.20904 validation_1-rmse:0.18969 +[22] validation_0-rmse:0.20762 validation_1-rmse:0.18718 +[23] validation_0-rmse:0.20580 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.20434 validation_1-rmse:0.18262 +[25] validation_0-rmse:0.20267 validation_1-rmse:0.18048 +[26] validation_0-rmse:0.20106 validation_1-rmse:0.17844 +[27] validation_0-rmse:0.19945 validation_1-rmse:0.17647 +[28] validation_0-rmse:0.19813 validation_1-rmse:0.17443 +[29] validation_0-rmse:0.19669 validation_1-rmse:0.17264 +[30] validation_0-rmse:0.19541 validation_1-rmse:0.17054 +[31] validation_0-rmse:0.19401 validation_1-rmse:0.16881 +[32] validation_0-rmse:0.19263 validation_1-rmse:0.16719 +[33] validation_0-rmse:0.19134 validation_1-rmse:0.16560 +[34] validation_0-rmse:0.18996 validation_1-rmse:0.16365 +[35] validation_0-rmse:0.18864 validation_1-rmse:0.16211 +[36] validation_0-rmse:0.18752 validation_1-rmse:0.16069 +[37] validation_0-rmse:0.18652 validation_1-rmse:0.15898 +[38] validation_0-rmse:0.18540 validation_1-rmse:0.15751 +[39] validation_0-rmse:0.18429 validation_1-rmse:0.15616 +[40] validation_0-rmse:0.18317 validation_1-rmse:0.15475 +[41] validation_0-rmse:0.18215 validation_1-rmse:0.15324 +[42] validation_0-rmse:0.18119 validation_1-rmse:0.15199 +[43] validation_0-rmse:0.18008 validation_1-rmse:0.15057 +[44] validation_0-rmse:0.17926 validation_1-rmse:0.14942 +[45] validation_0-rmse:0.17841 validation_1-rmse:0.14813 +[46] validation_0-rmse:0.17755 validation_1-rmse:0.14700 +[47] validation_0-rmse:0.17672 validation_1-rmse:0.14572 +[48] validation_0-rmse:0.17586 validation_1-rmse:0.14466 +[49] validation_0-rmse:0.17511 validation_1-rmse:0.14354 +[50] validation_0-rmse:0.17440 validation_1-rmse:0.14236 +[51] validation_0-rmse:0.17354 validation_1-rmse:0.14130 +[52] validation_0-rmse:0.17281 validation_1-rmse:0.14035 +[53] validation_0-rmse:0.17210 validation_1-rmse:0.13942 +[54] validation_0-rmse:0.17136 validation_1-rmse:0.13843 +[55] validation_0-rmse:0.17045 validation_1-rmse:0.13715 +[56] validation_0-rmse:0.16971 validation_1-rmse:0.13629 +[57] validation_0-rmse:0.16900 validation_1-rmse:0.13511 +[58] validation_0-rmse:0.16834 validation_1-rmse:0.13426 +[59] validation_0-rmse:0.16763 validation_1-rmse:0.13323 +[60] validation_0-rmse:0.16702 validation_1-rmse:0.13242 +[61] validation_0-rmse:0.16639 validation_1-rmse:0.13164 +[62] validation_0-rmse:0.16586 validation_1-rmse:0.13079 +[63] validation_0-rmse:0.16527 validation_1-rmse:0.13006 +[64] validation_0-rmse:0.16458 validation_1-rmse:0.12914 +[65] validation_0-rmse:0.16396 validation_1-rmse:0.12841 +[66] validation_0-rmse:0.16332 validation_1-rmse:0.12742 +[67] validation_0-rmse:0.16290 validation_1-rmse:0.12665 +[68] validation_0-rmse:0.16248 validation_1-rmse:0.12584 +[69] validation_0-rmse:0.16192 validation_1-rmse:0.12503 +[70] validation_0-rmse:0.16128 validation_1-rmse:0.12435 +[71] validation_0-rmse:0.16078 validation_1-rmse:0.12371 +[72] validation_0-rmse:0.16032 validation_1-rmse:0.12311 +[73] validation_0-rmse:0.15998 validation_1-rmse:0.12241 +[74] validation_0-rmse:0.15959 validation_1-rmse:0.12184 +[75] validation_0-rmse:0.15922 validation_1-rmse:0.12121 +[76] validation_0-rmse:0.15877 validation_1-rmse:0.12064 +[77] validation_0-rmse:0.15830 validation_1-rmse:0.11981 +[78] validation_0-rmse:0.15791 validation_1-rmse:0.11927 +[79] validation_0-rmse:0.15751 validation_1-rmse:0.11859 +[80] validation_0-rmse:0.15716 validation_1-rmse:0.11795 +[81] validation_0-rmse:0.15680 validation_1-rmse:0.11740 +[82] validation_0-rmse:0.15624 validation_1-rmse:0.11683 +[83] validation_0-rmse:0.15578 validation_1-rmse:0.11632 +[84] validation_0-rmse:0.15553 validation_1-rmse:0.11586 +[85] validation_0-rmse:0.15471 validation_1-rmse:0.11513 +[86] validation_0-rmse:0.15444 validation_1-rmse:0.11465 +[87] validation_0-rmse:0.15417 validation_1-rmse:0.11406 +[88] validation_0-rmse:0.15387 validation_1-rmse:0.11359 +[89] validation_0-rmse:0.15359 validation_1-rmse:0.11319 +[90] validation_0-rmse:0.15332 validation_1-rmse:0.11269 +[91] validation_0-rmse:0.15301 validation_1-rmse:0.11221 +[92] validation_0-rmse:0.15258 validation_1-rmse:0.11176 +[93] validation_0-rmse:0.15231 validation_1-rmse:0.11135 +[94] validation_0-rmse:0.15202 validation_1-rmse:0.11093 +[95] validation_0-rmse:0.15185 validation_1-rmse:0.11041 +[96] validation_0-rmse:0.15173 validation_1-rmse:0.11000 +[97] validation_0-rmse:0.15150 validation_1-rmse:0.10961 +[98] validation_0-rmse:0.15114 validation_1-rmse:0.10917 +[99] validation_0-rmse:0.15096 validation_1-rmse:0.10882 +2025-04-29 01:23:07,093 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.96 secs) -------------------- +2025-04-29 01:23:07,094 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:07,615 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:23:07,615 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather +2025-04-29 01:23:07,624 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:23:07,631 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:23:07,633 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:23:07,640 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:23:07,647 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:23:07,649 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:23:07,654 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-04-29 01:23:07,654 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:23:07,670 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:07,671 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:23:12,541 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:23:12,541 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.28468 validation_1-rmse:0.28491 +[1] validation_0-rmse:0.27961 validation_1-rmse:0.27914 +[2] validation_0-rmse:0.27524 validation_1-rmse:0.27370 +[3] validation_0-rmse:0.27054 validation_1-rmse:0.26858 +[4] validation_0-rmse:0.26601 validation_1-rmse:0.26362 +[5] validation_0-rmse:0.26219 validation_1-rmse:0.25896 +[6] validation_0-rmse:0.25814 validation_1-rmse:0.25450 +[7] validation_0-rmse:0.25500 validation_1-rmse:0.25026 +[8] validation_0-rmse:0.25145 validation_1-rmse:0.24602 +[9] validation_0-rmse:0.24843 validation_1-rmse:0.24213 +[10] validation_0-rmse:0.24527 validation_1-rmse:0.23824 +[11] validation_0-rmse:0.24238 validation_1-rmse:0.23440 +[12] validation_0-rmse:0.23940 validation_1-rmse:0.23096 +[13] validation_0-rmse:0.23630 validation_1-rmse:0.22764 +[14] validation_0-rmse:0.23385 validation_1-rmse:0.22440 +[15] validation_0-rmse:0.23099 validation_1-rmse:0.22128 +[16] validation_0-rmse:0.22865 validation_1-rmse:0.21801 +[17] validation_0-rmse:0.22620 validation_1-rmse:0.21515 +[18] validation_0-rmse:0.22375 validation_1-rmse:0.21210 +[19] validation_0-rmse:0.22142 validation_1-rmse:0.20925 +[20] validation_0-rmse:0.21927 validation_1-rmse:0.20663 +[21] validation_0-rmse:0.21720 validation_1-rmse:0.20416 +[22] validation_0-rmse:0.21528 validation_1-rmse:0.20170 +[23] validation_0-rmse:0.21330 validation_1-rmse:0.19913 +[24] validation_0-rmse:0.21136 validation_1-rmse:0.19693 +[25] validation_0-rmse:0.21002 validation_1-rmse:0.19438 +[26] validation_0-rmse:0.20807 validation_1-rmse:0.19222 +[27] validation_0-rmse:0.20636 validation_1-rmse:0.19016 +[28] validation_0-rmse:0.20439 validation_1-rmse:0.18763 +[29] validation_0-rmse:0.20276 validation_1-rmse:0.18559 +[30] validation_0-rmse:0.20114 validation_1-rmse:0.18380 +[31] validation_0-rmse:0.19965 validation_1-rmse:0.18163 +[32] validation_0-rmse:0.19833 validation_1-rmse:0.17955 +[33] validation_0-rmse:0.19688 validation_1-rmse:0.17782 +[34] validation_0-rmse:0.19558 validation_1-rmse:0.17614 +[35] validation_0-rmse:0.19420 validation_1-rmse:0.17451 +[36] validation_0-rmse:0.19297 validation_1-rmse:0.17293 +[37] validation_0-rmse:0.19169 validation_1-rmse:0.17111 +[38] validation_0-rmse:0.19038 validation_1-rmse:0.16943 +[39] validation_0-rmse:0.18941 validation_1-rmse:0.16798 +[40] validation_0-rmse:0.18828 validation_1-rmse:0.16657 +[41] validation_0-rmse:0.18724 validation_1-rmse:0.16485 +[42] validation_0-rmse:0.18620 validation_1-rmse:0.16347 +[43] validation_0-rmse:0.18525 validation_1-rmse:0.16204 +[44] validation_0-rmse:0.18429 validation_1-rmse:0.16073 +[45] validation_0-rmse:0.18324 validation_1-rmse:0.15951 +[46] validation_0-rmse:0.18250 validation_1-rmse:0.15797 +[47] validation_0-rmse:0.18157 validation_1-rmse:0.15682 +[48] validation_0-rmse:0.18069 validation_1-rmse:0.15566 +[49] validation_0-rmse:0.18002 validation_1-rmse:0.15440 +[50] validation_0-rmse:0.17914 validation_1-rmse:0.15322 +[51] validation_0-rmse:0.17842 validation_1-rmse:0.15220 +[52] validation_0-rmse:0.17756 validation_1-rmse:0.15107 +[53] validation_0-rmse:0.17668 validation_1-rmse:0.15007 +[54] validation_0-rmse:0.17596 validation_1-rmse:0.14866 +[55] validation_0-rmse:0.17525 validation_1-rmse:0.14775 +[56] validation_0-rmse:0.17467 validation_1-rmse:0.14653 +[57] validation_0-rmse:0.17390 validation_1-rmse:0.14564 +[58] validation_0-rmse:0.17326 validation_1-rmse:0.14478 +[59] validation_0-rmse:0.17273 validation_1-rmse:0.14356 +[60] validation_0-rmse:0.17218 validation_1-rmse:0.14269 +[61] validation_0-rmse:0.17157 validation_1-rmse:0.14186 +[62] validation_0-rmse:0.17120 validation_1-rmse:0.14083 +[63] validation_0-rmse:0.17069 validation_1-rmse:0.14002 +[64] validation_0-rmse:0.17012 validation_1-rmse:0.13912 +[65] validation_0-rmse:0.16942 validation_1-rmse:0.13834 +[66] validation_0-rmse:0.16914 validation_1-rmse:0.13720 +[67] validation_0-rmse:0.16856 validation_1-rmse:0.13648 +[68] validation_0-rmse:0.16800 validation_1-rmse:0.13569 +[69] validation_0-rmse:0.16796 validation_1-rmse:0.13472 +[70] validation_0-rmse:0.16737 validation_1-rmse:0.13405 +[71] validation_0-rmse:0.16686 validation_1-rmse:0.13342 +[72] validation_0-rmse:0.16639 validation_1-rmse:0.13270 +[73] validation_0-rmse:0.16648 validation_1-rmse:0.13149 +[74] validation_0-rmse:0.16609 validation_1-rmse:0.13086 +[75] validation_0-rmse:0.16560 validation_1-rmse:0.13025 +[76] validation_0-rmse:0.16530 validation_1-rmse:0.12925 +[77] validation_0-rmse:0.16492 validation_1-rmse:0.12824 +[78] validation_0-rmse:0.16451 validation_1-rmse:0.12770 +[79] validation_0-rmse:0.16414 validation_1-rmse:0.12710 +[80] validation_0-rmse:0.16377 validation_1-rmse:0.12654 +[81] validation_0-rmse:0.16338 validation_1-rmse:0.12595 +[82] validation_0-rmse:0.16317 validation_1-rmse:0.12490 +[83] validation_0-rmse:0.16249 validation_1-rmse:0.12361 +[84] validation_0-rmse:0.16217 validation_1-rmse:0.12307 +[85] validation_0-rmse:0.16178 validation_1-rmse:0.12255 +[86] validation_0-rmse:0.16149 validation_1-rmse:0.12206 +[87] validation_0-rmse:0.16113 validation_1-rmse:0.12155 +[88] validation_0-rmse:0.16049 validation_1-rmse:0.12061 +[89] validation_0-rmse:0.16008 validation_1-rmse:0.11990 +[90] validation_0-rmse:0.15955 validation_1-rmse:0.11882 +[91] validation_0-rmse:0.15927 validation_1-rmse:0.11842 +[92] validation_0-rmse:0.15891 validation_1-rmse:0.11796 +[93] validation_0-rmse:0.15880 validation_1-rmse:0.11730 +[94] validation_0-rmse:0.15829 validation_1-rmse:0.11631 +[95] validation_0-rmse:0.15809 validation_1-rmse:0.11584 +[96] validation_0-rmse:0.15778 validation_1-rmse:0.11544 +[97] validation_0-rmse:0.15763 validation_1-rmse:0.11504 +[98] validation_0-rmse:0.15724 validation_1-rmse:0.11438 +[99] validation_0-rmse:0.15694 validation_1-rmse:0.11396 +2025-04-29 01:23:13,172 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.52 secs) -------------------- +2025-04-29 01:23:13,173 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:13,698 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:23:13,699 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather +2025-04-29 01:23:13,709 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:23:13,717 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:23:13,719 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:23:13,729 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:23:13,736 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:23:13,738 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.093162 +1 0.003285 50.093162 +2 0.001898 50.093162 +3 0.000484 50.093162 +4 0.001688 50.093162 +2025-04-29 01:23:13,742 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-04-29 01:23:13,743 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:23:13,761 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:13,761 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:23:18,729 - datasieve.pipeline - INFO - DI tossed 2001 predictions for being too far from training data. +2025-04-29 01:23:18,732 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:23:18,732 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.32950 validation_1-rmse:0.29220 +[1] validation_0-rmse:0.32402 validation_1-rmse:0.28580 +[2] validation_0-rmse:0.31922 validation_1-rmse:0.27974 +[3] validation_0-rmse:0.31450 validation_1-rmse:0.27409 +[4] validation_0-rmse:0.30969 validation_1-rmse:0.26866 +[5] validation_0-rmse:0.30585 validation_1-rmse:0.26346 +[6] validation_0-rmse:0.30202 validation_1-rmse:0.25855 +[7] validation_0-rmse:0.29888 validation_1-rmse:0.25375 +[8] validation_0-rmse:0.29520 validation_1-rmse:0.24919 +[9] validation_0-rmse:0.29164 validation_1-rmse:0.24487 +[10] validation_0-rmse:0.28843 validation_1-rmse:0.24072 +[11] validation_0-rmse:0.28514 validation_1-rmse:0.23667 +[12] validation_0-rmse:0.28114 validation_1-rmse:0.23279 +[13] validation_0-rmse:0.27740 validation_1-rmse:0.22909 +[14] validation_0-rmse:0.27421 validation_1-rmse:0.22543 +[15] validation_0-rmse:0.27115 validation_1-rmse:0.22210 +[16] validation_0-rmse:0.26820 validation_1-rmse:0.21859 +[17] validation_0-rmse:0.26549 validation_1-rmse:0.21528 +[18] validation_0-rmse:0.26254 validation_1-rmse:0.21226 +[19] validation_0-rmse:0.25967 validation_1-rmse:0.20927 +[20] validation_0-rmse:0.25735 validation_1-rmse:0.20641 +[21] validation_0-rmse:0.25470 validation_1-rmse:0.20366 +[22] validation_0-rmse:0.25265 validation_1-rmse:0.20073 +[23] validation_0-rmse:0.25054 validation_1-rmse:0.19819 +[24] validation_0-rmse:0.24806 validation_1-rmse:0.19573 +[25] validation_0-rmse:0.24570 validation_1-rmse:0.19304 +[26] validation_0-rmse:0.24361 validation_1-rmse:0.19076 +[27] validation_0-rmse:0.24148 validation_1-rmse:0.18853 +[28] validation_0-rmse:0.24014 validation_1-rmse:0.18621 +[29] validation_0-rmse:0.23792 validation_1-rmse:0.18410 +[30] validation_0-rmse:0.23603 validation_1-rmse:0.18203 +[31] validation_0-rmse:0.23421 validation_1-rmse:0.17990 +[32] validation_0-rmse:0.23264 validation_1-rmse:0.17800 +[33] validation_0-rmse:0.23087 validation_1-rmse:0.17616 +[34] validation_0-rmse:0.22949 validation_1-rmse:0.17427 +[35] validation_0-rmse:0.22857 validation_1-rmse:0.17234 +[36] validation_0-rmse:0.22690 validation_1-rmse:0.17065 +[37] validation_0-rmse:0.22566 validation_1-rmse:0.16898 +[38] validation_0-rmse:0.22462 validation_1-rmse:0.16738 +[39] validation_0-rmse:0.22376 validation_1-rmse:0.16567 +[40] validation_0-rmse:0.22232 validation_1-rmse:0.16410 +[41] validation_0-rmse:0.22105 validation_1-rmse:0.16265 +[42] validation_0-rmse:0.22006 validation_1-rmse:0.16111 +[43] validation_0-rmse:0.21847 validation_1-rmse:0.15976 +[44] validation_0-rmse:0.21782 validation_1-rmse:0.15824 +[45] validation_0-rmse:0.21641 validation_1-rmse:0.15686 +[46] validation_0-rmse:0.21552 validation_1-rmse:0.15554 +[47] validation_0-rmse:0.21459 validation_1-rmse:0.15417 +[48] validation_0-rmse:0.21339 validation_1-rmse:0.15293 +[49] validation_0-rmse:0.21255 validation_1-rmse:0.15176 +[50] validation_0-rmse:0.21192 validation_1-rmse:0.15047 +[51] validation_0-rmse:0.21115 validation_1-rmse:0.14910 +[52] validation_0-rmse:0.21072 validation_1-rmse:0.14774 +[53] validation_0-rmse:0.20992 validation_1-rmse:0.14670 +[54] validation_0-rmse:0.20839 validation_1-rmse:0.14541 +[55] validation_0-rmse:0.20753 validation_1-rmse:0.14442 +[56] validation_0-rmse:0.20648 validation_1-rmse:0.14328 +[57] validation_0-rmse:0.20564 validation_1-rmse:0.14229 +[58] validation_0-rmse:0.20473 validation_1-rmse:0.14137 +[59] validation_0-rmse:0.20418 validation_1-rmse:0.14011 +[60] validation_0-rmse:0.20341 validation_1-rmse:0.13923 +[61] validation_0-rmse:0.20258 validation_1-rmse:0.13839 +[62] validation_0-rmse:0.20230 validation_1-rmse:0.13723 +[63] validation_0-rmse:0.20075 validation_1-rmse:0.13546 +[64] validation_0-rmse:0.20007 validation_1-rmse:0.13467 +[65] validation_0-rmse:0.19937 validation_1-rmse:0.13387 +[66] validation_0-rmse:0.19875 validation_1-rmse:0.13296 +[67] validation_0-rmse:0.19709 validation_1-rmse:0.13137 +[68] validation_0-rmse:0.19675 validation_1-rmse:0.13042 +[69] validation_0-rmse:0.19617 validation_1-rmse:0.12968 +[70] validation_0-rmse:0.19560 validation_1-rmse:0.12900 +[71] validation_0-rmse:0.19492 validation_1-rmse:0.12834 +[72] validation_0-rmse:0.19319 validation_1-rmse:0.12681 +[73] validation_0-rmse:0.19272 validation_1-rmse:0.12612 +[74] validation_0-rmse:0.19230 validation_1-rmse:0.12535 +[75] validation_0-rmse:0.19170 validation_1-rmse:0.12474 +[76] validation_0-rmse:0.19058 validation_1-rmse:0.12338 +[77] validation_0-rmse:0.19010 validation_1-rmse:0.12279 +[78] validation_0-rmse:0.18961 validation_1-rmse:0.12223 +[79] validation_0-rmse:0.18960 validation_1-rmse:0.12156 +[80] validation_0-rmse:0.18882 validation_1-rmse:0.12038 +[81] validation_0-rmse:0.18819 validation_1-rmse:0.11975 +[82] validation_0-rmse:0.18789 validation_1-rmse:0.11916 +[83] validation_0-rmse:0.18738 validation_1-rmse:0.11864 +[84] validation_0-rmse:0.18718 validation_1-rmse:0.11801 +[85] validation_0-rmse:0.18600 validation_1-rmse:0.11698 +[86] validation_0-rmse:0.18572 validation_1-rmse:0.11653 +[87] validation_0-rmse:0.18534 validation_1-rmse:0.11603 +[88] validation_0-rmse:0.18478 validation_1-rmse:0.11508 +[89] validation_0-rmse:0.18430 validation_1-rmse:0.11459 +[90] validation_0-rmse:0.18447 validation_1-rmse:0.11396 +[91] validation_0-rmse:0.18424 validation_1-rmse:0.11352 +[92] validation_0-rmse:0.18367 validation_1-rmse:0.11307 +[93] validation_0-rmse:0.18333 validation_1-rmse:0.11265 +[94] validation_0-rmse:0.18313 validation_1-rmse:0.11216 +[95] validation_0-rmse:0.18275 validation_1-rmse:0.11157 +[96] validation_0-rmse:0.18275 validation_1-rmse:0.11106 +[97] validation_0-rmse:0.18248 validation_1-rmse:0.11068 +[98] validation_0-rmse:0.18220 validation_1-rmse:0.11033 +[99] validation_0-rmse:0.18198 validation_1-rmse:0.10994 +2025-04-29 01:23:19,380 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.64 secs) -------------------- +2025-04-29 01:23:19,381 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:19,963 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:23:19,984 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +1 33535 +0 33236 +2025-04-29 01:23:19,985 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 35773 +1.0 30998 +2025-04-29 01:23:19,989 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-04-29 01:23:19,991 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:23:19,992 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:23:19,992 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1735689600_prediction.feather +2025-04-29 01:23:20,046 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:23:20,047 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:23:20,147 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:23:20,148 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:23:20,246 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:23:20,247 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:23:20,775 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:20,780 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:23:20,781 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.58814 +1 0.003044 49.58814 +2 0.000465 49.58814 +3 -0.000380 49.58814 +4 0.002829 49.58814 +2025-04-29 01:23:20,785 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:20,790 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:23:20,792 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:23:20,799 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1735689600/cb_sol_1735689600 +2025-04-29 01:23:20,799 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:23:20,829 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:20,830 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:23:25,946 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:23:25,947 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30164 validation_1-rmse:0.29585 +[1] validation_0-rmse:0.29609 validation_1-rmse:0.28921 +[2] validation_0-rmse:0.29103 validation_1-rmse:0.28298 +[3] validation_0-rmse:0.28604 validation_1-rmse:0.27706 +[4] validation_0-rmse:0.28108 validation_1-rmse:0.27129 +[5] validation_0-rmse:0.27670 validation_1-rmse:0.26609 +[6] validation_0-rmse:0.27234 validation_1-rmse:0.26092 +[7] validation_0-rmse:0.26874 validation_1-rmse:0.25593 +[8] validation_0-rmse:0.26461 validation_1-rmse:0.25118 +[9] validation_0-rmse:0.26074 validation_1-rmse:0.24677 +[10] validation_0-rmse:0.25745 validation_1-rmse:0.24239 +[11] validation_0-rmse:0.25460 validation_1-rmse:0.23832 +[12] validation_0-rmse:0.25121 validation_1-rmse:0.23441 +[13] validation_0-rmse:0.24825 validation_1-rmse:0.23068 +[14] validation_0-rmse:0.24580 validation_1-rmse:0.22694 +[15] validation_0-rmse:0.24286 validation_1-rmse:0.22346 +[16] validation_0-rmse:0.24051 validation_1-rmse:0.22006 +[17] validation_0-rmse:0.23821 validation_1-rmse:0.21690 +[18] validation_0-rmse:0.23549 validation_1-rmse:0.21383 +[19] validation_0-rmse:0.23335 validation_1-rmse:0.21087 +[20] validation_0-rmse:0.23089 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22918 validation_1-rmse:0.20505 +[22] validation_0-rmse:0.22716 validation_1-rmse:0.20240 +[23] validation_0-rmse:0.22562 validation_1-rmse:0.19981 +[24] validation_0-rmse:0.22385 validation_1-rmse:0.19723 +[25] validation_0-rmse:0.22201 validation_1-rmse:0.19473 +[26] validation_0-rmse:0.22016 validation_1-rmse:0.19245 +[27] validation_0-rmse:0.21834 validation_1-rmse:0.19024 +[28] validation_0-rmse:0.21671 validation_1-rmse:0.18789 +[29] validation_0-rmse:0.21493 validation_1-rmse:0.18579 +[30] validation_0-rmse:0.21385 validation_1-rmse:0.18351 +[31] validation_0-rmse:0.21216 validation_1-rmse:0.18156 +[32] validation_0-rmse:0.21088 validation_1-rmse:0.17941 +[33] validation_0-rmse:0.20953 validation_1-rmse:0.17754 +[34] validation_0-rmse:0.20805 validation_1-rmse:0.17575 +[35] validation_0-rmse:0.20648 validation_1-rmse:0.17399 +[36] validation_0-rmse:0.20515 validation_1-rmse:0.17220 +[37] validation_0-rmse:0.20382 validation_1-rmse:0.17031 +[38] validation_0-rmse:0.20257 validation_1-rmse:0.16871 +[39] validation_0-rmse:0.20125 validation_1-rmse:0.16718 +[40] validation_0-rmse:0.20005 validation_1-rmse:0.16574 +[41] validation_0-rmse:0.19885 validation_1-rmse:0.16415 +[42] validation_0-rmse:0.19789 validation_1-rmse:0.16270 +[43] validation_0-rmse:0.19680 validation_1-rmse:0.16130 +[44] validation_0-rmse:0.19564 validation_1-rmse:0.15993 +[45] validation_0-rmse:0.19480 validation_1-rmse:0.15854 +[46] validation_0-rmse:0.19376 validation_1-rmse:0.15728 +[47] validation_0-rmse:0.19290 validation_1-rmse:0.15568 +[48] validation_0-rmse:0.19223 validation_1-rmse:0.15445 +[49] validation_0-rmse:0.19129 validation_1-rmse:0.15330 +[50] validation_0-rmse:0.19035 validation_1-rmse:0.15194 +[51] validation_0-rmse:0.18948 validation_1-rmse:0.15082 +[52] validation_0-rmse:0.18882 validation_1-rmse:0.14945 +[53] validation_0-rmse:0.18801 validation_1-rmse:0.14840 +[54] validation_0-rmse:0.18707 validation_1-rmse:0.14736 +[55] validation_0-rmse:0.18637 validation_1-rmse:0.14635 +[56] validation_0-rmse:0.18571 validation_1-rmse:0.14542 +[57] validation_0-rmse:0.18497 validation_1-rmse:0.14413 +[58] validation_0-rmse:0.18443 validation_1-rmse:0.14297 +[59] validation_0-rmse:0.18375 validation_1-rmse:0.14203 +[60] validation_0-rmse:0.18319 validation_1-rmse:0.14111 +[61] validation_0-rmse:0.18266 validation_1-rmse:0.14030 +[62] validation_0-rmse:0.18185 validation_1-rmse:0.13914 +[63] validation_0-rmse:0.18145 validation_1-rmse:0.13831 +[64] validation_0-rmse:0.18135 validation_1-rmse:0.13720 +[65] validation_0-rmse:0.18075 validation_1-rmse:0.13643 +[66] validation_0-rmse:0.18020 validation_1-rmse:0.13560 +[67] validation_0-rmse:0.17951 validation_1-rmse:0.13485 +[68] validation_0-rmse:0.17888 validation_1-rmse:0.13414 +[69] validation_0-rmse:0.17850 validation_1-rmse:0.13343 +[70] validation_0-rmse:0.17798 validation_1-rmse:0.13224 +[71] validation_0-rmse:0.17751 validation_1-rmse:0.13133 +[72] validation_0-rmse:0.17711 validation_1-rmse:0.13062 +[73] validation_0-rmse:0.17701 validation_1-rmse:0.12966 +[74] validation_0-rmse:0.17648 validation_1-rmse:0.12872 +[75] validation_0-rmse:0.17611 validation_1-rmse:0.12806 +[76] validation_0-rmse:0.17573 validation_1-rmse:0.12732 +[77] validation_0-rmse:0.17528 validation_1-rmse:0.12664 +[78] validation_0-rmse:0.17478 validation_1-rmse:0.12605 +[79] validation_0-rmse:0.17432 validation_1-rmse:0.12518 +[80] validation_0-rmse:0.17391 validation_1-rmse:0.12466 +[81] validation_0-rmse:0.17358 validation_1-rmse:0.12398 +[82] validation_0-rmse:0.17315 validation_1-rmse:0.12342 +[83] validation_0-rmse:0.17260 validation_1-rmse:0.12276 +[84] validation_0-rmse:0.17220 validation_1-rmse:0.12222 +[85] validation_0-rmse:0.17182 validation_1-rmse:0.12176 +[86] validation_0-rmse:0.17152 validation_1-rmse:0.12124 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12046 +[88] validation_0-rmse:0.17085 validation_1-rmse:0.11974 +[89] validation_0-rmse:0.17053 validation_1-rmse:0.11930 +[90] validation_0-rmse:0.17018 validation_1-rmse:0.11888 +[91] validation_0-rmse:0.17011 validation_1-rmse:0.11810 +[92] validation_0-rmse:0.16980 validation_1-rmse:0.11762 +[93] validation_0-rmse:0.16956 validation_1-rmse:0.11689 +[94] validation_0-rmse:0.16923 validation_1-rmse:0.11641 +[95] validation_0-rmse:0.16912 validation_1-rmse:0.11579 +[96] validation_0-rmse:0.16878 validation_1-rmse:0.11530 +[97] validation_0-rmse:0.16857 validation_1-rmse:0.11489 +[98] validation_0-rmse:0.16824 validation_1-rmse:0.11442 +[99] validation_0-rmse:0.16824 validation_1-rmse:0.11403 +2025-04-29 01:23:27,063 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.26 secs) -------------------- +2025-04-29 01:23:27,064 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:27,614 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:23:27,614 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1736553600_prediction.feather +2025-04-29 01:23:27,618 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:27,624 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:23:27,625 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:23:27,630 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:27,636 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:23:27,638 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:23:27,643 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1736553600/cb_sol_1736553600 +2025-04-29 01:23:27,644 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:23:27,670 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:27,671 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:23:32,840 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:23:32,844 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:23:32,844 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29597 validation_1-rmse:0.29016 +[1] validation_0-rmse:0.29075 validation_1-rmse:0.28391 +[2] validation_0-rmse:0.28602 validation_1-rmse:0.27798 +[3] validation_0-rmse:0.28062 validation_1-rmse:0.27213 +[4] validation_0-rmse:0.27647 validation_1-rmse:0.26682 +[5] validation_0-rmse:0.27188 validation_1-rmse:0.26144 +[6] validation_0-rmse:0.26781 validation_1-rmse:0.25655 +[7] validation_0-rmse:0.26412 validation_1-rmse:0.25180 +[8] validation_0-rmse:0.25994 validation_1-rmse:0.24709 +[9] validation_0-rmse:0.25649 validation_1-rmse:0.24277 +[10] validation_0-rmse:0.25332 validation_1-rmse:0.23850 +[11] validation_0-rmse:0.24999 validation_1-rmse:0.23452 +[12] validation_0-rmse:0.24687 validation_1-rmse:0.23072 +[13] validation_0-rmse:0.24432 validation_1-rmse:0.22694 +[14] validation_0-rmse:0.24128 validation_1-rmse:0.22341 +[15] validation_0-rmse:0.23869 validation_1-rmse:0.21969 +[16] validation_0-rmse:0.23628 validation_1-rmse:0.21635 +[17] validation_0-rmse:0.23354 validation_1-rmse:0.21326 +[18] validation_0-rmse:0.23123 validation_1-rmse:0.21007 +[19] validation_0-rmse:0.22919 validation_1-rmse:0.20707 +[20] validation_0-rmse:0.22705 validation_1-rmse:0.20418 +[21] validation_0-rmse:0.22505 validation_1-rmse:0.20149 +[22] validation_0-rmse:0.22285 validation_1-rmse:0.19887 +[23] validation_0-rmse:0.22084 validation_1-rmse:0.19631 +[24] validation_0-rmse:0.21877 validation_1-rmse:0.19389 +[25] validation_0-rmse:0.21748 validation_1-rmse:0.19133 +[26] validation_0-rmse:0.21557 validation_1-rmse:0.18870 +[27] validation_0-rmse:0.21374 validation_1-rmse:0.18648 +[28] validation_0-rmse:0.21183 validation_1-rmse:0.18432 +[29] validation_0-rmse:0.21047 validation_1-rmse:0.18209 +[30] validation_0-rmse:0.20873 validation_1-rmse:0.17990 +[31] validation_0-rmse:0.20717 validation_1-rmse:0.17795 +[32] validation_0-rmse:0.20564 validation_1-rmse:0.17599 +[33] validation_0-rmse:0.20428 validation_1-rmse:0.17421 +[34] validation_0-rmse:0.20290 validation_1-rmse:0.17229 +[35] validation_0-rmse:0.20161 validation_1-rmse:0.17047 +[36] validation_0-rmse:0.20018 validation_1-rmse:0.16878 +[37] validation_0-rmse:0.19923 validation_1-rmse:0.16688 +[38] validation_0-rmse:0.19796 validation_1-rmse:0.16534 +[39] validation_0-rmse:0.19668 validation_1-rmse:0.16355 +[40] validation_0-rmse:0.19543 validation_1-rmse:0.16204 +[41] validation_0-rmse:0.19441 validation_1-rmse:0.16062 +[42] validation_0-rmse:0.19344 validation_1-rmse:0.15910 +[43] validation_0-rmse:0.19256 validation_1-rmse:0.15759 +[44] validation_0-rmse:0.19154 validation_1-rmse:0.15625 +[45] validation_0-rmse:0.19048 validation_1-rmse:0.15494 +[46] validation_0-rmse:0.18937 validation_1-rmse:0.15366 +[47] validation_0-rmse:0.18865 validation_1-rmse:0.15236 +[48] validation_0-rmse:0.18784 validation_1-rmse:0.15112 +[49] validation_0-rmse:0.18704 validation_1-rmse:0.14998 +[50] validation_0-rmse:0.18625 validation_1-rmse:0.14874 +[51] validation_0-rmse:0.18541 validation_1-rmse:0.14763 +[52] validation_0-rmse:0.18456 validation_1-rmse:0.14659 +[53] validation_0-rmse:0.18383 validation_1-rmse:0.14530 +[54] validation_0-rmse:0.18315 validation_1-rmse:0.14420 +[55] validation_0-rmse:0.18234 validation_1-rmse:0.14321 +[56] validation_0-rmse:0.18181 validation_1-rmse:0.14206 +[57] validation_0-rmse:0.18109 validation_1-rmse:0.14106 +[58] validation_0-rmse:0.18033 validation_1-rmse:0.13996 +[59] validation_0-rmse:0.17964 validation_1-rmse:0.13905 +[60] validation_0-rmse:0.17921 validation_1-rmse:0.13820 +[61] validation_0-rmse:0.17865 validation_1-rmse:0.13731 +[62] validation_0-rmse:0.17795 validation_1-rmse:0.13648 +[63] validation_0-rmse:0.17737 validation_1-rmse:0.13559 +[64] validation_0-rmse:0.17680 validation_1-rmse:0.13483 +[65] validation_0-rmse:0.17628 validation_1-rmse:0.13408 +[66] validation_0-rmse:0.17588 validation_1-rmse:0.13303 +[67] validation_0-rmse:0.17530 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.17478 validation_1-rmse:0.13153 +[69] validation_0-rmse:0.17439 validation_1-rmse:0.13081 +[70] validation_0-rmse:0.17401 validation_1-rmse:0.12991 +[71] validation_0-rmse:0.17347 validation_1-rmse:0.12911 +[72] validation_0-rmse:0.17304 validation_1-rmse:0.12838 +[73] validation_0-rmse:0.17254 validation_1-rmse:0.12774 +[74] validation_0-rmse:0.17207 validation_1-rmse:0.12656 +[75] validation_0-rmse:0.17185 validation_1-rmse:0.12571 +[76] validation_0-rmse:0.17126 validation_1-rmse:0.12512 +[77] validation_0-rmse:0.17096 validation_1-rmse:0.12447 +[78] validation_0-rmse:0.17064 validation_1-rmse:0.12381 +[79] validation_0-rmse:0.17024 validation_1-rmse:0.12300 +[80] validation_0-rmse:0.16989 validation_1-rmse:0.12244 +[81] validation_0-rmse:0.16955 validation_1-rmse:0.12180 +[82] validation_0-rmse:0.16924 validation_1-rmse:0.12129 +[83] validation_0-rmse:0.16931 validation_1-rmse:0.12037 +[84] validation_0-rmse:0.16888 validation_1-rmse:0.11970 +[85] validation_0-rmse:0.16845 validation_1-rmse:0.11914 +[86] validation_0-rmse:0.16809 validation_1-rmse:0.11840 +[87] validation_0-rmse:0.16766 validation_1-rmse:0.11760 +[88] validation_0-rmse:0.16741 validation_1-rmse:0.11714 +[89] validation_0-rmse:0.16707 validation_1-rmse:0.11667 +[90] validation_0-rmse:0.16683 validation_1-rmse:0.11592 +[91] validation_0-rmse:0.16643 validation_1-rmse:0.11537 +[92] validation_0-rmse:0.16621 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.16611 validation_1-rmse:0.11396 +[94] validation_0-rmse:0.16587 validation_1-rmse:0.11350 +[95] validation_0-rmse:0.16563 validation_1-rmse:0.11308 +[96] validation_0-rmse:0.16535 validation_1-rmse:0.11237 +[97] validation_0-rmse:0.16487 validation_1-rmse:0.11173 +[98] validation_0-rmse:0.16461 validation_1-rmse:0.11133 +[99] validation_0-rmse:0.16437 validation_1-rmse:0.11096 +2025-04-29 01:23:33,864 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.22 secs) -------------------- +2025-04-29 01:23:33,865 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:34,396 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:23:34,397 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1737417600_prediction.feather +2025-04-29 01:23:34,401 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:34,407 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:23:34,409 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:23:34,416 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:34,422 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:23:34,424 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:23:34,429 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1737417600/cb_sol_1737417600 +2025-04-29 01:23:34,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:23:34,454 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:34,454 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:23:39,621 - datasieve.pipeline - INFO - DI tossed 1523 predictions for being too far from training data. +2025-04-29 01:23:39,624 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:23:39,624 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30838 validation_1-rmse:0.28356 +[1] validation_0-rmse:0.30280 validation_1-rmse:0.27752 +[2] validation_0-rmse:0.29759 validation_1-rmse:0.27179 +[3] validation_0-rmse:0.29330 validation_1-rmse:0.26614 +[4] validation_0-rmse:0.28936 validation_1-rmse:0.26091 +[5] validation_0-rmse:0.28544 validation_1-rmse:0.25581 +[6] validation_0-rmse:0.28151 validation_1-rmse:0.25102 +[7] validation_0-rmse:0.27790 validation_1-rmse:0.24636 +[8] validation_0-rmse:0.27429 validation_1-rmse:0.24196 +[9] validation_0-rmse:0.27104 validation_1-rmse:0.23770 +[10] validation_0-rmse:0.26762 validation_1-rmse:0.23356 +[11] validation_0-rmse:0.26472 validation_1-rmse:0.22966 +[12] validation_0-rmse:0.26219 validation_1-rmse:0.22601 +[13] validation_0-rmse:0.25924 validation_1-rmse:0.22234 +[14] validation_0-rmse:0.25634 validation_1-rmse:0.21888 +[15] validation_0-rmse:0.25379 validation_1-rmse:0.21545 +[16] validation_0-rmse:0.25117 validation_1-rmse:0.21221 +[17] validation_0-rmse:0.24877 validation_1-rmse:0.20902 +[18] validation_0-rmse:0.24653 validation_1-rmse:0.20604 +[19] validation_0-rmse:0.24404 validation_1-rmse:0.20315 +[20] validation_0-rmse:0.24194 validation_1-rmse:0.20032 +[21] validation_0-rmse:0.23966 validation_1-rmse:0.19765 +[22] validation_0-rmse:0.23804 validation_1-rmse:0.19481 +[23] validation_0-rmse:0.23599 validation_1-rmse:0.19230 +[24] validation_0-rmse:0.23384 validation_1-rmse:0.18993 +[25] validation_0-rmse:0.23196 validation_1-rmse:0.18756 +[26] validation_0-rmse:0.23057 validation_1-rmse:0.18506 +[27] validation_0-rmse:0.22854 validation_1-rmse:0.18283 +[28] validation_0-rmse:0.22705 validation_1-rmse:0.18071 +[29] validation_0-rmse:0.22557 validation_1-rmse:0.17851 +[30] validation_0-rmse:0.22394 validation_1-rmse:0.17644 +[31] validation_0-rmse:0.22213 validation_1-rmse:0.17452 +[32] validation_0-rmse:0.22064 validation_1-rmse:0.17267 +[33] validation_0-rmse:0.21905 validation_1-rmse:0.17084 +[34] validation_0-rmse:0.21806 validation_1-rmse:0.16880 +[35] validation_0-rmse:0.21693 validation_1-rmse:0.16700 +[36] validation_0-rmse:0.21537 validation_1-rmse:0.16520 +[37] validation_0-rmse:0.21417 validation_1-rmse:0.16362 +[38] validation_0-rmse:0.21282 validation_1-rmse:0.16204 +[39] validation_0-rmse:0.21137 validation_1-rmse:0.16047 +[40] validation_0-rmse:0.20994 validation_1-rmse:0.15897 +[41] validation_0-rmse:0.20878 validation_1-rmse:0.15747 +[42] validation_0-rmse:0.20766 validation_1-rmse:0.15604 +[43] validation_0-rmse:0.20666 validation_1-rmse:0.15444 +[44] validation_0-rmse:0.20566 validation_1-rmse:0.15316 +[45] validation_0-rmse:0.20496 validation_1-rmse:0.15162 +[46] validation_0-rmse:0.20394 validation_1-rmse:0.15038 +[47] validation_0-rmse:0.20277 validation_1-rmse:0.14909 +[48] validation_0-rmse:0.20176 validation_1-rmse:0.14793 +[49] validation_0-rmse:0.20072 validation_1-rmse:0.14681 +[50] validation_0-rmse:0.20058 validation_1-rmse:0.14528 +[51] validation_0-rmse:0.19970 validation_1-rmse:0.14419 +[52] validation_0-rmse:0.19887 validation_1-rmse:0.14284 +[53] validation_0-rmse:0.19809 validation_1-rmse:0.14182 +[54] validation_0-rmse:0.19725 validation_1-rmse:0.14076 +[55] validation_0-rmse:0.19636 validation_1-rmse:0.13981 +[56] validation_0-rmse:0.19615 validation_1-rmse:0.13853 +[57] validation_0-rmse:0.19540 validation_1-rmse:0.13757 +[58] validation_0-rmse:0.19460 validation_1-rmse:0.13664 +[59] validation_0-rmse:0.19418 validation_1-rmse:0.13553 +[60] validation_0-rmse:0.19382 validation_1-rmse:0.13445 +[61] validation_0-rmse:0.19302 validation_1-rmse:0.13363 +[62] validation_0-rmse:0.19218 validation_1-rmse:0.13270 +[63] validation_0-rmse:0.19154 validation_1-rmse:0.13183 +[64] validation_0-rmse:0.19083 validation_1-rmse:0.13105 +[65] validation_0-rmse:0.19005 validation_1-rmse:0.13008 +[66] validation_0-rmse:0.18929 validation_1-rmse:0.12932 +[67] validation_0-rmse:0.18885 validation_1-rmse:0.12851 +[68] validation_0-rmse:0.18837 validation_1-rmse:0.12781 +[69] validation_0-rmse:0.18790 validation_1-rmse:0.12711 +[70] validation_0-rmse:0.18732 validation_1-rmse:0.12617 +[71] validation_0-rmse:0.18682 validation_1-rmse:0.12552 +[72] validation_0-rmse:0.18669 validation_1-rmse:0.12448 +[73] validation_0-rmse:0.18617 validation_1-rmse:0.12382 +[74] validation_0-rmse:0.18587 validation_1-rmse:0.12322 +[75] validation_0-rmse:0.18544 validation_1-rmse:0.12261 +[76] validation_0-rmse:0.18524 validation_1-rmse:0.12162 +[77] validation_0-rmse:0.18486 validation_1-rmse:0.12098 +[78] validation_0-rmse:0.18443 validation_1-rmse:0.12021 +[79] validation_0-rmse:0.18415 validation_1-rmse:0.11963 +[80] validation_0-rmse:0.18393 validation_1-rmse:0.11866 +[81] validation_0-rmse:0.18344 validation_1-rmse:0.11809 +[82] validation_0-rmse:0.18307 validation_1-rmse:0.11748 +[83] validation_0-rmse:0.18257 validation_1-rmse:0.11699 +[84] validation_0-rmse:0.18216 validation_1-rmse:0.11643 +[85] validation_0-rmse:0.18188 validation_1-rmse:0.11595 +[86] validation_0-rmse:0.18168 validation_1-rmse:0.11502 +[87] validation_0-rmse:0.18148 validation_1-rmse:0.11451 +[88] validation_0-rmse:0.18093 validation_1-rmse:0.11378 +[89] validation_0-rmse:0.18054 validation_1-rmse:0.11332 +[90] validation_0-rmse:0.18024 validation_1-rmse:0.11285 +[91] validation_0-rmse:0.17982 validation_1-rmse:0.11241 +[92] validation_0-rmse:0.17950 validation_1-rmse:0.11185 +[93] validation_0-rmse:0.17918 validation_1-rmse:0.11123 +[94] validation_0-rmse:0.17882 validation_1-rmse:0.11072 +[95] validation_0-rmse:0.17881 validation_1-rmse:0.10986 +[96] validation_0-rmse:0.17832 validation_1-rmse:0.10941 +[97] validation_0-rmse:0.17800 validation_1-rmse:0.10897 +[98] validation_0-rmse:0.17774 validation_1-rmse:0.10859 +[99] validation_0-rmse:0.17746 validation_1-rmse:0.10819 +2025-04-29 01:23:40,890 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.46 secs) -------------------- +2025-04-29 01:23:40,891 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:41,442 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:23:41,442 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1738281600_prediction.feather +2025-04-29 01:23:41,447 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:41,453 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:23:41,454 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:23:41,460 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:41,466 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:23:41,468 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:23:41,473 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1738281600/cb_sol_1738281600 +2025-04-29 01:23:41,474 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:23:41,497 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:41,497 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:23:46,635 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:23:46,635 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29494 validation_1-rmse:0.28739 +[1] validation_0-rmse:0.28930 validation_1-rmse:0.28164 +[2] validation_0-rmse:0.28437 validation_1-rmse:0.27613 +[3] validation_0-rmse:0.27990 validation_1-rmse:0.27106 +[4] validation_0-rmse:0.27541 validation_1-rmse:0.26617 +[5] validation_0-rmse:0.27070 validation_1-rmse:0.26147 +[6] validation_0-rmse:0.26683 validation_1-rmse:0.25687 +[7] validation_0-rmse:0.26280 validation_1-rmse:0.25263 +[8] validation_0-rmse:0.25916 validation_1-rmse:0.24830 +[9] validation_0-rmse:0.25540 validation_1-rmse:0.24420 +[10] validation_0-rmse:0.25186 validation_1-rmse:0.24022 +[11] validation_0-rmse:0.24829 validation_1-rmse:0.23647 +[12] validation_0-rmse:0.24504 validation_1-rmse:0.23286 +[13] validation_0-rmse:0.24183 validation_1-rmse:0.22943 +[14] validation_0-rmse:0.23870 validation_1-rmse:0.22619 +[15] validation_0-rmse:0.23587 validation_1-rmse:0.22274 +[16] validation_0-rmse:0.23325 validation_1-rmse:0.21951 +[17] validation_0-rmse:0.23045 validation_1-rmse:0.21650 +[18] validation_0-rmse:0.22792 validation_1-rmse:0.21367 +[19] validation_0-rmse:0.22524 validation_1-rmse:0.21092 +[20] validation_0-rmse:0.22293 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22055 validation_1-rmse:0.20549 +[22] validation_0-rmse:0.21831 validation_1-rmse:0.20307 +[23] validation_0-rmse:0.21601 validation_1-rmse:0.20062 +[24] validation_0-rmse:0.21372 validation_1-rmse:0.19810 +[25] validation_0-rmse:0.21154 validation_1-rmse:0.19580 +[26] validation_0-rmse:0.20966 validation_1-rmse:0.19369 +[27] validation_0-rmse:0.20790 validation_1-rmse:0.19130 +[28] validation_0-rmse:0.20602 validation_1-rmse:0.18921 +[29] validation_0-rmse:0.20418 validation_1-rmse:0.18723 +[30] validation_0-rmse:0.20236 validation_1-rmse:0.18525 +[31] validation_0-rmse:0.20057 validation_1-rmse:0.18324 +[32] validation_0-rmse:0.19900 validation_1-rmse:0.18144 +[33] validation_0-rmse:0.19744 validation_1-rmse:0.17941 +[34] validation_0-rmse:0.19608 validation_1-rmse:0.17767 +[35] validation_0-rmse:0.19467 validation_1-rmse:0.17605 +[36] validation_0-rmse:0.19313 validation_1-rmse:0.17422 +[37] validation_0-rmse:0.19156 validation_1-rmse:0.17260 +[38] validation_0-rmse:0.19020 validation_1-rmse:0.17103 +[39] validation_0-rmse:0.18884 validation_1-rmse:0.16948 +[40] validation_0-rmse:0.18767 validation_1-rmse:0.16797 +[41] validation_0-rmse:0.18636 validation_1-rmse:0.16647 +[42] validation_0-rmse:0.18512 validation_1-rmse:0.16505 +[43] validation_0-rmse:0.18403 validation_1-rmse:0.16340 +[44] validation_0-rmse:0.18290 validation_1-rmse:0.16210 +[45] validation_0-rmse:0.18189 validation_1-rmse:0.16085 +[46] validation_0-rmse:0.18090 validation_1-rmse:0.15966 +[47] validation_0-rmse:0.17992 validation_1-rmse:0.15841 +[48] validation_0-rmse:0.17901 validation_1-rmse:0.15728 +[49] validation_0-rmse:0.17817 validation_1-rmse:0.15582 +[50] validation_0-rmse:0.17697 validation_1-rmse:0.15458 +[51] validation_0-rmse:0.17607 validation_1-rmse:0.15349 +[52] validation_0-rmse:0.17516 validation_1-rmse:0.15235 +[53] validation_0-rmse:0.17425 validation_1-rmse:0.15131 +[54] validation_0-rmse:0.17347 validation_1-rmse:0.15032 +[55] validation_0-rmse:0.17275 validation_1-rmse:0.14932 +[56] validation_0-rmse:0.17211 validation_1-rmse:0.14834 +[57] validation_0-rmse:0.17131 validation_1-rmse:0.14741 +[58] validation_0-rmse:0.17072 validation_1-rmse:0.14617 +[59] validation_0-rmse:0.16999 validation_1-rmse:0.14528 +[60] validation_0-rmse:0.16934 validation_1-rmse:0.14416 +[61] validation_0-rmse:0.16887 validation_1-rmse:0.14321 +[62] validation_0-rmse:0.16842 validation_1-rmse:0.14213 +[63] validation_0-rmse:0.16765 validation_1-rmse:0.14130 +[64] validation_0-rmse:0.16691 validation_1-rmse:0.14048 +[65] validation_0-rmse:0.16629 validation_1-rmse:0.13956 +[66] validation_0-rmse:0.16565 validation_1-rmse:0.13882 +[67] validation_0-rmse:0.16530 validation_1-rmse:0.13793 +[68] validation_0-rmse:0.16467 validation_1-rmse:0.13710 +[69] validation_0-rmse:0.16436 validation_1-rmse:0.13621 +[70] validation_0-rmse:0.16377 validation_1-rmse:0.13542 +[71] validation_0-rmse:0.16334 validation_1-rmse:0.13463 +[72] validation_0-rmse:0.16280 validation_1-rmse:0.13394 +[73] validation_0-rmse:0.16230 validation_1-rmse:0.13328 +[74] validation_0-rmse:0.16156 validation_1-rmse:0.13246 +[75] validation_0-rmse:0.16122 validation_1-rmse:0.13151 +[76] validation_0-rmse:0.16080 validation_1-rmse:0.13080 +[77] validation_0-rmse:0.16033 validation_1-rmse:0.13015 +[78] validation_0-rmse:0.15992 validation_1-rmse:0.12951 +[79] validation_0-rmse:0.15950 validation_1-rmse:0.12888 +[80] validation_0-rmse:0.15909 validation_1-rmse:0.12822 +[81] validation_0-rmse:0.15875 validation_1-rmse:0.12744 +[82] validation_0-rmse:0.15831 validation_1-rmse:0.12683 +[83] validation_0-rmse:0.15786 validation_1-rmse:0.12626 +[84] validation_0-rmse:0.15747 validation_1-rmse:0.12572 +[85] validation_0-rmse:0.15724 validation_1-rmse:0.12495 +[86] validation_0-rmse:0.15695 validation_1-rmse:0.12442 +[87] validation_0-rmse:0.15664 validation_1-rmse:0.12382 +[88] validation_0-rmse:0.15651 validation_1-rmse:0.12326 +[89] validation_0-rmse:0.15629 validation_1-rmse:0.12256 +[90] validation_0-rmse:0.15596 validation_1-rmse:0.12196 +[91] validation_0-rmse:0.15559 validation_1-rmse:0.12141 +[92] validation_0-rmse:0.15511 validation_1-rmse:0.12088 +[93] validation_0-rmse:0.15487 validation_1-rmse:0.12033 +[94] validation_0-rmse:0.15472 validation_1-rmse:0.11975 +[95] validation_0-rmse:0.15438 validation_1-rmse:0.11924 +[96] validation_0-rmse:0.15408 validation_1-rmse:0.11882 +[97] validation_0-rmse:0.15382 validation_1-rmse:0.11819 +[98] validation_0-rmse:0.15350 validation_1-rmse:0.11777 +[99] validation_0-rmse:0.15331 validation_1-rmse:0.11727 +2025-04-29 01:23:47,608 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.13 secs) -------------------- +2025-04-29 01:23:47,608 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:48,148 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:23:48,148 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1739145600_prediction.feather +2025-04-29 01:23:48,156 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:48,162 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:23:48,163 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:23:48,169 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:48,175 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:23:48,177 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:23:48,182 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1739145600/cb_sol_1739145600 +2025-04-29 01:23:48,183 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:23:48,205 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:48,206 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:23:53,217 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:23:53,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29889 validation_1-rmse:0.30153 +[1] validation_0-rmse:0.29317 validation_1-rmse:0.29483 +[2] validation_0-rmse:0.28819 validation_1-rmse:0.28860 +[3] validation_0-rmse:0.28336 validation_1-rmse:0.28273 +[4] validation_0-rmse:0.27885 validation_1-rmse:0.27694 +[5] validation_0-rmse:0.27448 validation_1-rmse:0.27155 +[6] validation_0-rmse:0.27020 validation_1-rmse:0.26634 +[7] validation_0-rmse:0.26629 validation_1-rmse:0.26134 +[8] validation_0-rmse:0.26241 validation_1-rmse:0.25653 +[9] validation_0-rmse:0.25876 validation_1-rmse:0.25192 +[10] validation_0-rmse:0.25559 validation_1-rmse:0.24747 +[11] validation_0-rmse:0.25223 validation_1-rmse:0.24337 +[12] validation_0-rmse:0.24904 validation_1-rmse:0.23934 +[13] validation_0-rmse:0.24639 validation_1-rmse:0.23548 +[14] validation_0-rmse:0.24353 validation_1-rmse:0.23187 +[15] validation_0-rmse:0.24076 validation_1-rmse:0.22837 +[16] validation_0-rmse:0.23849 validation_1-rmse:0.22484 +[17] validation_0-rmse:0.23581 validation_1-rmse:0.22147 +[18] validation_0-rmse:0.23342 validation_1-rmse:0.21814 +[19] validation_0-rmse:0.23133 validation_1-rmse:0.21509 +[20] validation_0-rmse:0.22937 validation_1-rmse:0.21187 +[21] validation_0-rmse:0.22713 validation_1-rmse:0.20902 +[22] validation_0-rmse:0.22509 validation_1-rmse:0.20631 +[23] validation_0-rmse:0.22312 validation_1-rmse:0.20373 +[24] validation_0-rmse:0.22123 validation_1-rmse:0.20076 +[25] validation_0-rmse:0.21951 validation_1-rmse:0.19837 +[26] validation_0-rmse:0.21751 validation_1-rmse:0.19562 +[27] validation_0-rmse:0.21589 validation_1-rmse:0.19309 +[28] validation_0-rmse:0.21422 validation_1-rmse:0.19091 +[29] validation_0-rmse:0.21272 validation_1-rmse:0.18879 +[30] validation_0-rmse:0.21119 validation_1-rmse:0.18660 +[31] validation_0-rmse:0.20982 validation_1-rmse:0.18468 +[32] validation_0-rmse:0.20829 validation_1-rmse:0.18239 +[33] validation_0-rmse:0.20681 validation_1-rmse:0.18048 +[34] validation_0-rmse:0.20548 validation_1-rmse:0.17869 +[35] validation_0-rmse:0.20431 validation_1-rmse:0.17665 +[36] validation_0-rmse:0.20297 validation_1-rmse:0.17483 +[37] validation_0-rmse:0.20174 validation_1-rmse:0.17311 +[38] validation_0-rmse:0.20060 validation_1-rmse:0.17153 +[39] validation_0-rmse:0.19951 validation_1-rmse:0.16958 +[40] validation_0-rmse:0.19848 validation_1-rmse:0.16805 +[41] validation_0-rmse:0.19745 validation_1-rmse:0.16652 +[42] validation_0-rmse:0.19647 validation_1-rmse:0.16509 +[43] validation_0-rmse:0.19570 validation_1-rmse:0.16325 +[44] validation_0-rmse:0.19473 validation_1-rmse:0.16187 +[45] validation_0-rmse:0.19397 validation_1-rmse:0.16012 +[46] validation_0-rmse:0.19314 validation_1-rmse:0.15887 +[47] validation_0-rmse:0.19196 validation_1-rmse:0.15723 +[48] validation_0-rmse:0.19096 validation_1-rmse:0.15595 +[49] validation_0-rmse:0.19009 validation_1-rmse:0.15468 +[50] validation_0-rmse:0.18931 validation_1-rmse:0.15355 +[51] validation_0-rmse:0.18864 validation_1-rmse:0.15207 +[52] validation_0-rmse:0.18786 validation_1-rmse:0.15101 +[53] validation_0-rmse:0.18690 validation_1-rmse:0.14960 +[54] validation_0-rmse:0.18614 validation_1-rmse:0.14859 +[55] validation_0-rmse:0.18550 validation_1-rmse:0.14756 +[56] validation_0-rmse:0.18475 validation_1-rmse:0.14647 +[57] validation_0-rmse:0.18405 validation_1-rmse:0.14545 +[58] validation_0-rmse:0.18346 validation_1-rmse:0.14415 +[59] validation_0-rmse:0.18277 validation_1-rmse:0.14321 +[60] validation_0-rmse:0.18219 validation_1-rmse:0.14221 +[61] validation_0-rmse:0.18158 validation_1-rmse:0.14129 +[62] validation_0-rmse:0.18100 validation_1-rmse:0.14043 +[63] validation_0-rmse:0.18059 validation_1-rmse:0.13920 +[64] validation_0-rmse:0.17997 validation_1-rmse:0.13842 +[65] validation_0-rmse:0.17941 validation_1-rmse:0.13754 +[66] validation_0-rmse:0.17881 validation_1-rmse:0.13652 +[67] validation_0-rmse:0.17823 validation_1-rmse:0.13576 +[68] validation_0-rmse:0.17784 validation_1-rmse:0.13468 +[69] validation_0-rmse:0.17735 validation_1-rmse:0.13396 +[70] validation_0-rmse:0.17687 validation_1-rmse:0.13311 +[71] validation_0-rmse:0.17628 validation_1-rmse:0.13225 +[72] validation_0-rmse:0.17599 validation_1-rmse:0.13154 +[73] validation_0-rmse:0.17542 validation_1-rmse:0.13080 +[74] validation_0-rmse:0.17497 validation_1-rmse:0.13013 +[75] validation_0-rmse:0.17456 validation_1-rmse:0.12954 +[76] validation_0-rmse:0.17416 validation_1-rmse:0.12864 +[77] validation_0-rmse:0.17369 validation_1-rmse:0.12802 +[78] validation_0-rmse:0.17345 validation_1-rmse:0.12735 +[79] validation_0-rmse:0.17302 validation_1-rmse:0.12672 +[80] validation_0-rmse:0.17254 validation_1-rmse:0.12609 +[81] validation_0-rmse:0.17248 validation_1-rmse:0.12527 +[82] validation_0-rmse:0.17210 validation_1-rmse:0.12470 +[83] validation_0-rmse:0.17196 validation_1-rmse:0.12398 +[84] validation_0-rmse:0.17189 validation_1-rmse:0.12334 +[85] validation_0-rmse:0.17155 validation_1-rmse:0.12280 +[86] validation_0-rmse:0.17124 validation_1-rmse:0.12230 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12178 +[88] validation_0-rmse:0.17086 validation_1-rmse:0.12118 +[89] validation_0-rmse:0.17064 validation_1-rmse:0.12049 +[90] validation_0-rmse:0.17029 validation_1-rmse:0.11993 +[91] validation_0-rmse:0.16981 validation_1-rmse:0.11942 +[92] validation_0-rmse:0.16950 validation_1-rmse:0.11894 +[93] validation_0-rmse:0.16937 validation_1-rmse:0.11833 +[94] validation_0-rmse:0.16928 validation_1-rmse:0.11786 +[95] validation_0-rmse:0.16899 validation_1-rmse:0.11735 +[96] validation_0-rmse:0.16869 validation_1-rmse:0.11693 +[97] validation_0-rmse:0.16843 validation_1-rmse:0.11650 +[98] validation_0-rmse:0.16829 validation_1-rmse:0.11591 +[99] validation_0-rmse:0.16802 validation_1-rmse:0.11547 +2025-04-29 01:23:54,194 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.01 secs) -------------------- +2025-04-29 01:23:54,194 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:23:54,764 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:23:54,765 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740009600_prediction.feather +2025-04-29 01:23:54,773 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:54,779 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:23:54,780 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:23:54,790 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:23:54,797 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:23:54,798 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:23:54,804 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740009600/cb_sol_1740009600 +2025-04-29 01:23:54,804 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:23:54,827 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:23:54,828 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:23:59,967 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:23:59,968 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29357 validation_1-rmse:0.28852 +[1] validation_0-rmse:0.28850 validation_1-rmse:0.28219 +[2] validation_0-rmse:0.28292 validation_1-rmse:0.27618 +[3] validation_0-rmse:0.27862 validation_1-rmse:0.27046 +[4] validation_0-rmse:0.27383 validation_1-rmse:0.26510 +[5] validation_0-rmse:0.27018 validation_1-rmse:0.25989 +[6] validation_0-rmse:0.26615 validation_1-rmse:0.25477 +[7] validation_0-rmse:0.26234 validation_1-rmse:0.25007 +[8] validation_0-rmse:0.25794 validation_1-rmse:0.24560 +[9] validation_0-rmse:0.25417 validation_1-rmse:0.24109 +[10] validation_0-rmse:0.25083 validation_1-rmse:0.23700 +[11] validation_0-rmse:0.24683 validation_1-rmse:0.23303 +[12] validation_0-rmse:0.24384 validation_1-rmse:0.22908 +[13] validation_0-rmse:0.24093 validation_1-rmse:0.22542 +[14] validation_0-rmse:0.23743 validation_1-rmse:0.22186 +[15] validation_0-rmse:0.23484 validation_1-rmse:0.21841 +[16] validation_0-rmse:0.23215 validation_1-rmse:0.21525 +[17] validation_0-rmse:0.22951 validation_1-rmse:0.21206 +[18] validation_0-rmse:0.22658 validation_1-rmse:0.20906 +[19] validation_0-rmse:0.22440 validation_1-rmse:0.20615 +[20] validation_0-rmse:0.22193 validation_1-rmse:0.20314 +[21] validation_0-rmse:0.22009 validation_1-rmse:0.20016 +[22] validation_0-rmse:0.21755 validation_1-rmse:0.19751 +[23] validation_0-rmse:0.21578 validation_1-rmse:0.19498 +[24] validation_0-rmse:0.21440 validation_1-rmse:0.19241 +[25] validation_0-rmse:0.21229 validation_1-rmse:0.19006 +[26] validation_0-rmse:0.21038 validation_1-rmse:0.18780 +[27] validation_0-rmse:0.20897 validation_1-rmse:0.18529 +[28] validation_0-rmse:0.20703 validation_1-rmse:0.18313 +[29] validation_0-rmse:0.20556 validation_1-rmse:0.18091 +[30] validation_0-rmse:0.20384 validation_1-rmse:0.17884 +[31] validation_0-rmse:0.20281 validation_1-rmse:0.17690 +[32] validation_0-rmse:0.20169 validation_1-rmse:0.17483 +[33] validation_0-rmse:0.20012 validation_1-rmse:0.17300 +[34] validation_0-rmse:0.19876 validation_1-rmse:0.17106 +[35] validation_0-rmse:0.19755 validation_1-rmse:0.16934 +[36] validation_0-rmse:0.19649 validation_1-rmse:0.16752 +[37] validation_0-rmse:0.19501 validation_1-rmse:0.16586 +[38] validation_0-rmse:0.19423 validation_1-rmse:0.16418 +[39] validation_0-rmse:0.19297 validation_1-rmse:0.16264 +[40] validation_0-rmse:0.19162 validation_1-rmse:0.16092 +[41] validation_0-rmse:0.19049 validation_1-rmse:0.15952 +[42] validation_0-rmse:0.18925 validation_1-rmse:0.15810 +[43] validation_0-rmse:0.18845 validation_1-rmse:0.15638 +[44] validation_0-rmse:0.18730 validation_1-rmse:0.15506 +[45] validation_0-rmse:0.18661 validation_1-rmse:0.15357 +[46] validation_0-rmse:0.18563 validation_1-rmse:0.15226 +[47] validation_0-rmse:0.18473 validation_1-rmse:0.15101 +[48] validation_0-rmse:0.18399 validation_1-rmse:0.14957 +[49] validation_0-rmse:0.18304 validation_1-rmse:0.14841 +[50] validation_0-rmse:0.18219 validation_1-rmse:0.14717 +[51] validation_0-rmse:0.18131 validation_1-rmse:0.14599 +[52] validation_0-rmse:0.18043 validation_1-rmse:0.14492 +[53] validation_0-rmse:0.17966 validation_1-rmse:0.14388 +[54] validation_0-rmse:0.17901 validation_1-rmse:0.14274 +[55] validation_0-rmse:0.17850 validation_1-rmse:0.14134 +[56] validation_0-rmse:0.17764 validation_1-rmse:0.14035 +[57] validation_0-rmse:0.17682 validation_1-rmse:0.13937 +[58] validation_0-rmse:0.17604 validation_1-rmse:0.13844 +[59] validation_0-rmse:0.17526 validation_1-rmse:0.13754 +[60] validation_0-rmse:0.17488 validation_1-rmse:0.13621 +[61] validation_0-rmse:0.17432 validation_1-rmse:0.13530 +[62] validation_0-rmse:0.17345 validation_1-rmse:0.13439 +[63] validation_0-rmse:0.17284 validation_1-rmse:0.13358 +[64] validation_0-rmse:0.17213 validation_1-rmse:0.13278 +[65] validation_0-rmse:0.17164 validation_1-rmse:0.13175 +[66] validation_0-rmse:0.17098 validation_1-rmse:0.13088 +[67] validation_0-rmse:0.17049 validation_1-rmse:0.13002 +[68] validation_0-rmse:0.17000 validation_1-rmse:0.12918 +[69] validation_0-rmse:0.16969 validation_1-rmse:0.12815 +[70] validation_0-rmse:0.16917 validation_1-rmse:0.12746 +[71] validation_0-rmse:0.16857 validation_1-rmse:0.12678 +[72] validation_0-rmse:0.16830 validation_1-rmse:0.12595 +[73] validation_0-rmse:0.16793 validation_1-rmse:0.12522 +[74] validation_0-rmse:0.16752 validation_1-rmse:0.12457 +[75] validation_0-rmse:0.16704 validation_1-rmse:0.12395 +[76] validation_0-rmse:0.16668 validation_1-rmse:0.12316 +[77] validation_0-rmse:0.16621 validation_1-rmse:0.12251 +[78] validation_0-rmse:0.16591 validation_1-rmse:0.12185 +[79] validation_0-rmse:0.16550 validation_1-rmse:0.12115 +[80] validation_0-rmse:0.16506 validation_1-rmse:0.12055 +[81] validation_0-rmse:0.16467 validation_1-rmse:0.12001 +[82] validation_0-rmse:0.16422 validation_1-rmse:0.11944 +[83] validation_0-rmse:0.16379 validation_1-rmse:0.11892 +[84] validation_0-rmse:0.16344 validation_1-rmse:0.11825 +[85] validation_0-rmse:0.16317 validation_1-rmse:0.11766 +[86] validation_0-rmse:0.16289 validation_1-rmse:0.11712 +[87] validation_0-rmse:0.16271 validation_1-rmse:0.11639 +[88] validation_0-rmse:0.16236 validation_1-rmse:0.11591 +[89] validation_0-rmse:0.16210 validation_1-rmse:0.11515 +[90] validation_0-rmse:0.16170 validation_1-rmse:0.11457 +[91] validation_0-rmse:0.16149 validation_1-rmse:0.11411 +[92] validation_0-rmse:0.16132 validation_1-rmse:0.11360 +[93] validation_0-rmse:0.16108 validation_1-rmse:0.11292 +[94] validation_0-rmse:0.16077 validation_1-rmse:0.11247 +[95] validation_0-rmse:0.16040 validation_1-rmse:0.11205 +[96] validation_0-rmse:0.16017 validation_1-rmse:0.11157 +[97] validation_0-rmse:0.15988 validation_1-rmse:0.11117 +[98] validation_0-rmse:0.15964 validation_1-rmse:0.11074 +[99] validation_0-rmse:0.15958 validation_1-rmse:0.11029 +2025-04-29 01:24:01,761 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.96 secs) -------------------- +2025-04-29 01:24:01,762 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:24:02,314 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:24:02,315 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740873600_prediction.feather +2025-04-29 01:24:02,321 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:24:02,328 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:24:02,329 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:24:02,337 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:24:02,344 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:24:02,345 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:24:02,351 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740873600/cb_sol_1740873600 +2025-04-29 01:24:02,352 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:24:02,376 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:24:02,376 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- + + +^Z^C2025-04-29 01:24:05,324 - freqtrade - INFO - SIGINT received, aborting ... diff --git a/docker-compose.yml b/docker-compose.yml index 3dee57d1..1056856b 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -60,13 +60,14 @@ services: # --hyperopt-loss SharpeHyperOptLoss # --spaces roi stoploss # -e 200 + + #--config /freqtrade/templates/FreqaiExampleStrategy.json command: > backtesting --logfile /freqtrade/user_data/logs/freqtrade.log --freqaimodel XGBoostRegressor --config /freqtrade/config_examples/config_freqai.okx.json - --config /freqtrade/templates/FreqaiExampleStrategy.json --strategy-path /freqtrade/templates --strategy FreqaiExampleStrategy --timerange 20250401-20250420 diff --git a/e6402d0.diff b/e6402d0.diff new file mode 100644 index 00000000..06a60c49 --- /dev/null +++ b/e6402d0.diff @@ -0,0 +1,407 @@ +diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py +index 00ff1d2..40a1adc 100644 +--- a/freqtrade/templates/FreqaiExampleStrategy.py ++++ b/freqtrade/templates/FreqaiExampleStrategy.py +@@ -9,44 +9,47 @@ from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter + logger = logging.getLogger(__name__) + + class FreqaiExampleStrategy(IStrategy): +- # 移除硬编码的 minimal_roi 和 stoploss，改为动态适配 +- minimal_roi = {} # 将在 populate_indicators 中动态生成 +- stoploss = 0.0 # 将在 populate_indicators 中动态设置 ++ # 动态适配 minimal_roi 和 stoploss ++ minimal_roi = {} # populate_indicators 中动态设定 ++ stoploss = -0.15 # 默认固定止损 + trailing_stop = True ++ trailing_stop_positive = 0.05 ++ trailing_stop_positive_offset = 0.1 + process_only_new_candles = True + use_exit_signal = True + startup_candle_count: int = 40 + can_short = False + +- # 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化 ++ # 可训练参数（用于 Hyperopt） + buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True) + sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True) + +- # 为 Hyperopt 优化添加 ROI 和 stoploss 参数 + roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True) + roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True) + roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True) +- stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True) ++ stoploss_param = DecimalParameter( ++ low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True ++ ) + + # FreqAI 配置 + freqai_info = { +- "model": "CatboostClassifier", # 与config保持一致 ++ "model": "CatboostClassifier", + "feature_parameters": { +- "include_timeframes": ["3m", "15m", "1h"], # 与config一致 +- "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对 +- "label_period_candles": 20, # 与config一致 +- "include_shifted_candles": 2, # 与config一致 ++ "include_timeframes": ["3m", "15m", "1h"], ++ "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], ++ "label_period_candles": 20, ++ "include_shifted_candles": 2, + }, + "data_split_parameters": { + "test_size": 0.2, +- "shuffle": True, # 启用shuffle ++ "shuffle": True, + }, + "model_training_parameters": { +- "n_estimators": 100, # 减少树的数量 +- "learning_rate": 0.1, # 提高学习率 +- "max_depth": 6, # 限制树深度 +- "subsample": 0.8, # 添加子采样 +- "colsample_bytree": 0.8, # 添加特征采样 ++ "n_estimators": 100, ++ "learning_rate": 0.1, ++ "max_depth": 6, ++ "subsample": 0.8, ++ "colsample_bytree": 0.8, + "objective": "reg:squarederror", + "eval_metric": "rmse", + "early_stopping_rounds": 20, +@@ -54,8 +57,8 @@ class FreqaiExampleStrategy(IStrategy): + }, + "data_kitchen": { + "feature_parameters": { +- "DI_threshold": 1.5, # 降低异常值过滤阈值 +- "use_DBSCAN_to_remove_outliers": False # 禁用DBSCAN ++ "DI_threshold": 1.5, ++ "use_DBSCAN_to_remove_outliers": False + } + } + } +@@ -72,265 +75,113 @@ class FreqaiExampleStrategy(IStrategy): + } + + def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame: +- # 保留关键的技术指标 ++ # RSI 计算 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + +- # 确保 MACD 列被正确计算并保留 ++ # MACD 计算并容错 + try: + macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe["macd"] = macd["macd"] + dataframe["macdsignal"] = macd["macdsignal"] + except Exception as e: +- logger.error(f"计算 MACD 列时出错：{str(e)}") ++ logger.error(f"MACD 计算失败: {e}") + dataframe["macd"] = np.nan + dataframe["macdsignal"] = np.nan + +- # 检查 MACD 列是否存在 +- if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: +- logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") +- raise ValueError("DataFrame 缺少必要的 MACD 列") +- +- # 确保 MACD 列存在 +- if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: +- logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") +- raise ValueError("DataFrame 缺少必要的 MACD 列") +- +- # 保留布林带相关特征 ++ # 布林带 + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + +- # 保留成交量相关特征 ++ # 成交量均线 + dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() + +- # 数据清理 ++ # 清理无穷大值 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) +- dataframe[col] = dataframe[col].ffill().fillna(0) +- +- logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}") ++ dataframe[col] = dataframe[col].fillna(0) ++ + return dataframe + + def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-pct-change"] = dataframe["close"].pct_change() + dataframe["%-raw_volume"] = dataframe["volume"] + dataframe["%-raw_price"] = dataframe["close"] +-# 数据清理逻辑 ++ ++ # 数据清理 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0) +- dataframe[col] = dataframe[col].ffill() +- dataframe[col] = dataframe[col].fillna(0) +- +- # 检查是否仍有无效值 +- if dataframe[col].isna().any() or np.isinf(dataframe[col]).any(): +- logger.warning(f"列 {col} 仍包含无效值，已填充为默认值") +- dataframe[col] = dataframe[col].fillna(0) +- return dataframe +- +- def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: +- dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek +- dataframe["%-hour_of_day"] = dataframe["date"].dt.hour +- dataframe.replace([np.inf, -np.inf], 0, inplace=True) +- dataframe.ffill(inplace=True) +- dataframe.fillna(0, inplace=True) +- return dataframe +- +- def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: +- logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}") +- if "close" not in dataframe.columns: +- logger.error("数据框缺少必要的 'close' 列") +- raise ValueError("数据框缺少必要的 'close' 列") +- +- try: +- label_period = self.freqai_info["feature_parameters"]["label_period_candles"] +- +- # 定义目标变量为未来价格变化百分比（连续值） +- dataframe["up_or_down"] = ( +- dataframe["close"].shift(-label_period) - dataframe["close"] +- ) / dataframe["close"] +- +- # 数据清理：处理 NaN 和 Inf 值 +- dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) +- dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) +- +- # 确保目标变量是二维数组 +- if dataframe["up_or_down"].ndim == 1: +- dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1) +- +- # 检查并处理 NaN 或无限值 +- dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) +- dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) +- +- # 生成 %-volatility 特征 +- dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() +- +- # 确保 &-buy_rsi 列的值计算正确 +- dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14) +- +- # 数据清理 +- for col in ["&-buy_rsi", "up_or_down", "%-volatility"]: +- # 使用直接操作避免链式赋值 +- dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) +- dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill') +- dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值 +- if dataframe[col].isna().any(): +- logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值") +- +- except Exception as e: +- logger.error(f"创建 FreqAI 目标失败：{str(e)}") +- raise ++ dataframe[col] = dataframe[col].ffill().fillna(0) + +- # Log the shape of the target variable for debugging +- logger.info(f"目标列形状：{dataframe['up_or_down'].shape}") +- logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}") + return dataframe + +- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: +- logger.info(f"处理交易对：{metadata['pair']}") +- dataframe = self.freqai.start(dataframe, metadata, self) +- +- # 计算传统指标 +- dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) +- bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) +- dataframe["bb_lowerband"] = bollinger["lower"] +- dataframe["bb_middleband"] = bollinger["mid"] +- dataframe["bb_upperband"] = bollinger["upper"] +- dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) +- +- # 生成 up_or_down 信号（非 FreqAI 目标） +- label_period = self.freqai_info["feature_parameters"]["label_period_candles"] +- # 使用未来价格变化方向生成 up_or_down 信号 +- label_period = self.freqai_info["feature_parameters"]["label_period_candles"] +- dataframe["up_or_down"] = np.where( +- dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0 +- ) +- +- # 动态设置参数 +- if "&-buy_rsi" in dataframe.columns: +- # 派生其他目标 +- dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30 +- dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() +- # Ensure proper calculation and handle potential NaN values +- dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1) +- dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0) ++ def set_freqai_targets(self, df: DataFrame, metadata: dict) -> DataFrame: ++ """定义标签""" ++ df["&-up_or_down"] = np.where(df["close"].shift(-20) > df["close"], 1, 0) ++ return df + +- # Additional check to ensure no NaN values remain +- for col in ["&-stoploss", "&-roi_0"]: +- if dataframe[col].isna().any(): +- logger.warning(f"列 {col} 仍包含 NaN，填充为默认值") +- dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0) +- +- # 简化动态参数生成逻辑 +- # 放松 buy_rsi 和 sell_rsi 的生成逻辑 +- # 计算 buy_rsi_pred 并清理 NaN 值 +- dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50) +- dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median()) ++ def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame: ++ # 特征工程调用 ++ df = self.feature_engineering_expand_all(df, period=14, metadata=metadata) ++ df = self.feature_engineering_expand_basic(df, metadata=metadata) ++ df = self.set_freqai_targets(df, metadata) ++ ++ # 动态参数预测 ++ df["buy_rsi_pred"] = df["rsi"].rolling(window=10).median().clip(20, 45) ++ df["sell_rsi_pred"] = df["buy_rsi_pred"] + 20 ++ df["stoploss_pred"] = -0.1 - (df["%-pct-change"].abs().rolling(20).std() * 10).clip(0.05, 0.25) ++ df["roi_0_pred"] = self.roi_0.value * 1.2 ++ # 添加 do_predict 列（示例：每5个周期中使用3个进行预测） ++ df['do_predict'] = 0 ++ df.loc[df.index % 5 <= 2, 'do_predict'] = 1 # 每5根K线中前3根设为1 ++ ++ df.fillna(0, inplace=True) ++ ++ # 更新策略参数 ++ self.buy_rsi.value = float(df["buy_rsi_pred"].iloc[-1]) ++ self.sell_rsi.value = float(df["sell_rsi_pred"].iloc[-1]) ++ self.stoploss = float(df["stoploss_pred"].iloc[-1]) ++ ++ self.minimal_roi = { ++ 0: float(self.roi_0.value), ++ 15: float(self.roi_15.value), ++ 30: float(self.roi_30.value), ++ 60: 0 ++ } + +- # 计算 sell_rsi_pred 并清理 NaN 值 +- dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20 +- dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median()) ++ return df + +- # 计算 stoploss_pred 并清理 NaN 值 +- dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25) +- dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean()) ++ def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: ++ conditions = [ ++ (df["rsi"] < df["buy_rsi_pred"]), ++ (df["volume"] > df["volume_ma"] * 1.2), ++ (df["close"] > df["bb_middleband"]), ++ (df["macd"] > df["macdsignal"]), ++ (df["do_predict"] == 1), ++ ] + +- # 计算 roi_0_pred 并清理 NaN 值 +- dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2) +- dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean()) +- +- # 检查预测值 +- for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]: +- if dataframe[col].isna().any(): +- logger.warning(f"列 {col} 包含 NaN，填充为默认值") +- dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) +- +- # 更保守的止损和止盈设置 +- dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2) +- dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3) +- +- # 设置策略级参数 +- self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1]) +- self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1]) +-# 更保守的止损设置 +- self.stoploss = -0.15 # 固定止损 15% +- self.minimal_roi = { +- 0: float(self.roi_0.value), +- 15: float(self.roi_15.value), +- 30: float(self.roi_30.value), +- 60: 0 +- } +-# 更保守的追踪止损设置 +- self.trailing_stop_positive = 0.05 # 追踪止损触发点 +- self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量 +- +- logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, " +- f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}") +- +- dataframe.replace([np.inf, -np.inf], 0, inplace=True) +- dataframe.ffill(inplace=True) +- dataframe.fillna(0, inplace=True) +- +- logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}") +- logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}") +- +- return dataframe ++ df.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'long_entry') ++ return df + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: +-# 改进卖出信号条件 +- exit_long_conditions = [ +- (df["rsi"] > df["sell_rsi_pred"]), # RSI 高于卖出阈值 +- (df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值 +- (df["close"] < df["bb_middleband"]) # 价格低于布林带中轨 ++ conditions = [ ++ (df["rsi"] > df["sell_rsi_pred"]), ++ (df["close"] < df["bb_middleband"]), ++ (df["do_predict"] == 0), + ] +- if exit_long_conditions: +- df.loc[ +- reduce(lambda x, y: x & y, exit_long_conditions), +- "exit_long" +- ] = 1 ++ df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 + return df +- def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: +- # 改进买入信号条件 +- # 检查 MACD 列是否存在 +- if "macd" not in df.columns or "macdsignal" not in df.columns: +- logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。") +- +- try: +- macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9) +- df["macd"] = macd["macd"] +- df["macdsignal"] = macd["macdsignal"] +- logger.info("MACD 列已成功重新计算。") +- except Exception as e: +- logger.error(f"重新计算 MACD 列时出错：{str(e)}") +- raise ValueError("DataFrame 缺少必要的 MACD 列且无法重新计算。") + +- enter_long_conditions = [ +- (df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值 +- (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20% +- (df["close"] > df["bb_middleband"]) # 价格高于布林带中轨 +- ] +- +- # 如果 MACD 列存在，则添加 MACD 金叉条件 +- if "macd" in df.columns and "macdsignal" in df.columns: +- enter_long_conditions.append((df["macd"] > df["macdsignal"])) +- +- # 确保模型预测为买入 +- enter_long_conditions.append((df["do_predict"] == 1)) +- if enter_long_conditions: +- df.loc[ +- reduce(lambda x, y: x & y, enter_long_conditions), +- ["enter_long", "enter_tag"] +- ] = (1, "long") +- return df + def confirm_trade_entry( + self, pair: str, order_type: str, amount: float, rate: float, + time_in_force: str, current_time, entry_tag, side: str, **kwargs + ) -> bool: + df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) +- last_candle = df.iloc[-1].squeeze() ++ last_candle = df.iloc[-1] + if side == "long": +- if rate > (last_candle["close"] * (1 + 0.0025)): ++ if rate > (last_candle["close"] * 1.0025): # 价格超过最新价 0.25% 则拒绝下单 + return False + return True diff --git a/filtered_output.log b/filtered_output.log new file mode 100644 index 00000000..443a5acf --- /dev/null +++ b/filtered_output.log @@ -0,0 +1,888 @@ +[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950 +[99] validation_0-rmse:0.14079 validation_1-rmse:0.10036 +[99] validation_0-rmse:0.15004 validation_1-rmse:0.09815 +[99] validation_0-rmse:0.13402 validation_1-rmse:0.10289 +[99] validation_0-rmse:0.14818 validation_1-rmse:0.10475 +[99] validation_0-rmse:0.15738 validation_1-rmse:0.10531 +[99] validation_0-rmse:0.14246 validation_1-rmse:0.11071 +[99] validation_0-rmse:0.16321 validation_1-rmse:0.10649 +[99] validation_0-rmse:0.15096 validation_1-rmse:0.10882 +[99] validation_0-rmse:0.15694 validation_1-rmse:0.11396 +[99] validation_0-rmse:0.18198 validation_1-rmse:0.10994 +[99] validation_0-rmse:0.16824 validation_1-rmse:0.11403 +[99] validation_0-rmse:0.16437 validation_1-rmse:0.11096 +[99] validation_0-rmse:0.17746 validation_1-rmse:0.10819 +[99] validation_0-rmse:0.15331 validation_1-rmse:0.11727 +[99] validation_0-rmse:0.16802 validation_1-rmse:0.11547 +[99] validation_0-rmse:0.15958 validation_1-rmse:0.11029 +[99] validation_0-rmse:0.18742 validation_1-rmse:0.11149 +[99] validation_0-rmse:0.15075 validation_1-rmse:0.09770 +[99] validation_0-rmse:0.13818 validation_1-rmse:0.10038 +[99] validation_0-rmse:0.12954 validation_1-rmse:0.09825 +[99] validation_0-rmse:0.14837 validation_1-rmse:0.10398 +Creating freqtrade_freqtrade_run ... +Creating freqtrade_freqtrade_run ... done +2025-04-29 01:54:55,246 - freqtrade - INFO - freqtrade 2025.3 +2025-04-29 01:54:55,464 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-04-29 01:54:56,878 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-04-29 01:54:56,879 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ... +2025-04-29 01:54:56,881 - freqtrade.loggers - INFO - Enabling colorized output. +2025-04-29 01:54:56,881 - root - INFO - Logfile configured +2025-04-29 01:54:56,882 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-04-29 01:54:56,882 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250101-20250420 ... +2025-04-29 01:54:56,907 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250101-20250420 +2025-04-29 01:54:56,909 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-04-29 01:54:56,910 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-04-29 01:54:56,916 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-04-29 01:54:56,916 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-04-29 01:54:56,917 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:56,919 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-04-29 01:54:56,919 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-04-29 01:54:56,925 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-04-29 01:54:56,931 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-04-29 01:54:59,471 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-04-29 01:54:59,491 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-04-29 01:54:59,491 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +'minutes'}. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +'stoploss_on_exchange_interval': 60} +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-04-29 01:54:59,502 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:59,505 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-04-29 01:54:59,512 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-04-29 01:54:59,512 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:54:59,513 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Loading data from 2024-12-01 21:30:00 up to 2025-04-20 00:00:00 (139 days). +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-04-29 01:54:59,673 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-04-29 01:55:01,274 - matplotlib.font_manager - INFO - generated new fontManager +2025-04-29 01:55:01,489 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-04-29 01:55:01,490 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-04-29 01:55:01,493 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-04-29 01:55:01,498 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-04-29 01:55:01,500 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:55:01,501 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:55:01,502 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1735689600_prediction.feather +2025-04-29 01:55:01,602 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,603 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,705 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:01,706 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,814 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:55:01,815 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,942 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,943 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,037 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:02,038 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,113 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,118 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:55:02,121 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.152831 +1 0.003285 50.152831 +2 0.001898 50.152831 +3 0.000484 50.152831 +4 0.001688 50.152831 +2025-04-29 01:55:02,123 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,129 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:02,130 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:02,134 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1735689600/cb_btc_1735689600 +2025-04-29 01:55:02,135 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:02,151 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:02,152 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:55:07,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:07,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:08,221 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.09 secs) -------------------- +2025-04-29 01:55:08,222 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:08,903 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:55:08,904 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1736553600_prediction.feather +2025-04-29 01:55:08,907 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,912 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:08,914 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:08,917 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,924 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:08,925 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:08,929 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1736553600/cb_btc_1736553600 +2025-04-29 01:55:08,930 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:08,946 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:08,947 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:55:13,908 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:55:13,911 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:13,912 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:14,692 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.76 secs) -------------------- +2025-04-29 01:55:14,693 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:15,250 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:55:15,250 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1737417600_prediction.feather +2025-04-29 01:55:15,254 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,261 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:15,262 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:15,268 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,275 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:15,276 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:15,281 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1737417600/cb_btc_1737417600 +2025-04-29 01:55:15,281 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:15,297 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:15,298 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:55:20,324 - datasieve.pipeline - INFO - DI tossed 1622 predictions for being too far from training data. +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:21,007 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.73 secs) -------------------- +2025-04-29 01:55:21,008 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:21,504 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:55:21,505 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1738281600_prediction.feather +2025-04-29 01:55:21,510 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,516 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:21,517 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:21,522 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,528 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:21,529 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:21,533 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1738281600/cb_btc_1738281600 +2025-04-29 01:55:21,534 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:21,550 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:21,550 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:55:26,605 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:26,606 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:27,556 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.02 secs) -------------------- +2025-04-29 01:55:27,557 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:28,076 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:55:28,077 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1739145600_prediction.feather +2025-04-29 01:55:28,081 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,088 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:28,089 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:28,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,100 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:28,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:28,106 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1739145600/cb_btc_1739145600 +2025-04-29 01:55:28,107 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:28,123 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:28,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:55:33,123 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:33,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:33,929 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.82 secs) -------------------- +2025-04-29 01:55:33,930 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:34,433 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:55:34,434 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740009600_prediction.feather +2025-04-29 01:55:34,440 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,447 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:34,448 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:34,453 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,459 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:34,461 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:34,465 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740009600/cb_btc_1740009600 +2025-04-29 01:55:34,466 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:34,482 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:34,483 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:55:39,369 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:39,370 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:40,266 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.80 secs) -------------------- +2025-04-29 01:55:40,267 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:40,801 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:55:40,802 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740873600_prediction.feather +2025-04-29 01:55:40,807 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,814 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:40,816 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:40,821 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,827 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:40,829 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:40,833 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740873600/cb_btc_1740873600 +2025-04-29 01:55:40,834 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:40,849 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:40,850 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:55:45,643 - datasieve.pipeline - INFO - DI tossed 2275 predictions for being too far from training data. +2025-04-29 01:55:45,646 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:45,647 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:46,544 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.71 secs) -------------------- +2025-04-29 01:55:46,544 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:47,092 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:55:47,092 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1741737600_prediction.feather +2025-04-29 01:55:47,100 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,107 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:47,109 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:47,115 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,122 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:47,123 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:47,128 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1741737600/cb_btc_1741737600 +2025-04-29 01:55:47,129 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:47,145 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:47,145 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:55:51,987 - datasieve.pipeline - INFO - DI tossed 18 predictions for being too far from training data. +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:52,741 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.61 secs) -------------------- +2025-04-29 01:55:52,742 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:53,285 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:55:53,286 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1742601600_prediction.feather +2025-04-29 01:55:53,291 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,298 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:53,300 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:53,309 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,316 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:53,318 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:53,322 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1742601600/cb_btc_1742601600 +2025-04-29 01:55:53,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:53,339 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:53,340 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:55:58,184 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:58,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:55:59,097 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:55:59,098 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:59,706 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:55:59,706 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather +2025-04-29 01:55:59,715 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,723 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:59,725 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:59,732 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,739 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:55:59,741 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:55:59,745 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-04-29 01:55:59,746 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:59,762 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:59,762 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:05,520 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:56:05,521 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:06,027 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:56:06,027 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather +2025-04-29 01:56:06,037 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,045 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:56:06,046 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:56:06,057 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,064 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:56:06,065 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.093162 +1 0.003285 50.093162 +2 0.001898 50.093162 +3 0.000484 50.093162 +4 0.001688 50.093162 +2025-04-29 01:56:06,070 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-04-29 01:56:06,071 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:56:06,087 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:06,088 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:56:10,904 - datasieve.pipeline - INFO - DI tossed 2001 predictions for being too far from training data. +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:11,705 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.63 secs) -------------------- +2025-04-29 01:56:11,706 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:12,255 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:56:12,275 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +1 33535 +0 33236 +2025-04-29 01:56:12,276 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 35773 +1.0 30998 +2025-04-29 01:56:12,279 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-04-29 01:56:12,281 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:56:12,282 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:56:12,283 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1735689600_prediction.feather +2025-04-29 01:56:12,334 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:56:12,335 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,518 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:56:12,519 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:13,040 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,046 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:56:13,047 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.58814 +1 0.003044 49.58814 +2 0.000465 49.58814 +3 -0.000380 49.58814 +4 0.002829 49.58814 +2025-04-29 01:56:13,052 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,057 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:13,059 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:13,066 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1735689600/cb_sol_1735689600 +2025-04-29 01:56:13,066 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:13,095 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:13,096 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:19,586 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.52 secs) -------------------- +2025-04-29 01:56:19,587 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:20,174 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:56:20,175 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1736553600_prediction.feather +2025-04-29 01:56:20,179 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,185 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:20,186 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:20,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,197 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:20,199 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:20,204 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1736553600/cb_sol_1736553600 +2025-04-29 01:56:20,205 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:20,227 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:20,228 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:56:25,109 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:26,510 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.30 secs) -------------------- +2025-04-29 01:56:26,511 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:27,072 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:56:27,073 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1737417600_prediction.feather +2025-04-29 01:56:27,079 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,085 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:27,086 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:27,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,100 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:27,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:27,108 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1737417600/cb_sol_1737417600 +2025-04-29 01:56:27,109 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:27,130 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:27,131 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:56:32,206 - datasieve.pipeline - INFO - DI tossed 1523 predictions for being too far from training data. +2025-04-29 01:56:32,209 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:32,210 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:33,558 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:33,558 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:34,118 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:56:34,119 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1738281600_prediction.feather +2025-04-29 01:56:34,124 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,130 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:34,131 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:34,137 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,143 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:34,144 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:34,149 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1738281600/cb_sol_1738281600 +2025-04-29 01:56:34,150 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:34,173 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:34,173 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:40,600 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:40,601 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:41,171 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:56:41,172 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1739145600_prediction.feather +2025-04-29 01:56:41,177 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,183 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:41,185 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:41,193 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,200 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:41,201 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:41,206 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1739145600/cb_sol_1739145600 +2025-04-29 01:56:41,207 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:41,228 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:41,229 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:56:46,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:46,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:47,778 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.57 secs) -------------------- +2025-04-29 01:56:47,779 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:48,320 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:56:48,321 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740009600_prediction.feather +2025-04-29 01:56:48,327 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,333 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:48,334 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:48,346 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,353 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:48,354 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:48,361 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740009600/cb_sol_1740009600 +2025-04-29 01:56:48,361 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:48,383 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:48,383 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:56:53,532 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:53,533 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:56:54,862 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.50 secs) -------------------- +2025-04-29 01:56:54,863 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:55,419 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:56:55,420 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740873600_prediction.feather +2025-04-29 01:56:55,426 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,433 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:55,435 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:55,445 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,452 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:56:55,453 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:56:55,459 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740873600/cb_sol_1740873600 +2025-04-29 01:56:55,459 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:55,481 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:55,482 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:57:00,566 - datasieve.pipeline - INFO - DI tossed 2417 predictions for being too far from training data. +2025-04-29 01:57:00,569 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:00,570 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:57:02,441 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.98 secs) -------------------- +2025-04-29 01:57:02,442 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:02,968 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:57:02,968 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1741737600_prediction.feather +2025-04-29 01:57:02,980 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:02,987 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:57:02,989 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:57:03,001 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:03,007 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:03,009 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:03,014 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1741737600/cb_sol_1741737600 +2025-04-29 01:57:03,015 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:03,042 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:03,042 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:57:08,138 - datasieve.pipeline - INFO - DI tossed 3 predictions for being too far from training data. +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:57:09,614 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:09,615 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:10,150 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:57:10,151 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1742601600_prediction.feather +2025-04-29 01:57:10,159 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,167 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:10,168 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:10,181 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,188 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:10,190 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:10,195 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1742601600/cb_sol_1742601600 +2025-04-29 01:57:10,196 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:10,218 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:10,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:57:15,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:15,186 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:57:16,538 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.34 secs) -------------------- +2025-04-29 01:57:16,539 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:17,068 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:57:17,069 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather +2025-04-29 01:57:17,084 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,092 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:17,094 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:17,108 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,115 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:17,117 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:17,124 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 +2025-04-29 01:57:17,125 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:17,151 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:17,151 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:57:23,725 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:23,726 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:24,305 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:57:24,305 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather +2025-04-29 01:57:24,318 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,325 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:24,327 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:24,337 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,345 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:57:24,346 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.729824 +1 0.003044 49.729824 +2 0.000465 49.729824 +3 -0.000380 49.729824 +4 0.002829 49.729824 +2025-04-29 01:57:24,352 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 +2025-04-29 01:57:24,353 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:24,376 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:24,376 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:57:29,392 - datasieve.pipeline - INFO - DI tossed 1948 predictions for being too far from training data. +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-04-29 01:57:30,474 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.12 secs) -------------------- +2025-04-29 01:57:30,475 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:31,077 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:57:31,096 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +0 33825 +1 32946 +2025-04-29 01:57:31,097 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 36730 +1.0 30041 +2025-04-29 01:57:31,105 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-01-01 00:00:00 up to 2025-04-20 00:00:00 (109 days). +2025-04-29 01:57:31,109 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,111 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:31,193 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,195 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:33,776 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_01-57-33.meta.json" +Result for strategy FreqaiExampleStrategy + BACKTESTING REPORT +┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ BTC/USDT │ 38 │ -0.39 │ -22.029 │ -2.2 │ 22:13:00 │ 5 32 1 13.2 │ +│ SOL/USDT │ 44 │ -1.94 │ -128.236 │ -12.82 │ 16:35:00 │ 12 26 6 27.3 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + LEFT OPEN TRADES REPORT +┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │ +└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + ENTER TAG STATS +┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ long │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + EXIT REASON STATS +┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ roi │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ trailing_stop_loss │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + MIXED TAG STATS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ('long', 'roi') │ │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ ('long', 'trailing_stop_loss') │ │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + SUMMARY METRICS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Metric ┃ Value ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ Backtesting from │ 2025-01-01 00:00:00 │ +│ Backtesting to │ 2025-04-20 00:00:00 │ +│ Trading Mode │ Spot │ +│ Max open trades │ 2 │ +│ │ │ +│ Total/Daily Avg Trades │ 82 / 0.75 │ +│ Starting balance │ 1000 USDT │ +│ Final balance │ 849.735 USDT │ +│ Absolute profit │ -150.265 USDT │ +│ Total profit % │ -15.03% │ +│ CAGR % │ -42.03% │ +│ Sortino │ -252.56 │ +│ Sharpe │ -4.15 │ +│ Calmar │ -17.48 │ +│ SQN │ -2.60 │ +│ Profit factor │ 0.05 │ +│ Expectancy (Ratio) │ -1.83 (-0.79) │ +│ Avg. daily profit % │ -0.14% │ +│ Avg. stake amount │ 150 USDT │ +│ Total trade volume │ 24523.15 USDT │ +│ │ │ +│ Best Pair │ BTC/USDT -2.20% │ +│ Worst Pair │ SOL/USDT -12.82% │ +│ Best trade │ SOL/USDT 0.90% │ +│ Worst trade │ SOL/USDT -15.19% │ +│ Best day │ 1.76 USDT │ +│ Worst day │ -22.827 USDT │ +│ Days win/draw/lose │ 14 / 80 / 7 │ +│ Avg. Duration Winners │ 0:55:00 │ +│ Avg. Duration Loser │ 2 days, 18:13:00 │ +│ Max Consecutive Wins / Loss │ 2 / 16 │ +│ Rejected Entry signals │ 0 │ +│ Entry/Exit Timeouts │ 0 / 0 │ +│ │ │ +│ Min balance │ 849.735 USDT │ +│ Max balance │ 1000.508 USDT │ +│ Max % of account underwater │ 15.07% │ +│ Absolute Drawdown (Account) │ 15.07% │ +│ Absolute Drawdown │ 150.773 USDT │ +│ Drawdown high │ 0.508 USDT │ +│ Drawdown low │ -150.265 USDT │ +│ Drawdown Start │ 2025-01-06 19:48:00 │ +│ Drawdown End │ 2025-04-06 23:15:00 │ +│ Market change │ -26.79% │ +└─────────────────────────────┴─────────────────────┘ + +Backtested 2025-01-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2 + STRATEGY SUMMARY +┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ +│ FreqaiExampleStrategy │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ 150.773 USDT 15.07% │ +└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴──────────────────────┘ diff --git a/freqtrade.log b/freqtrade.log new file mode 100644 index 00000000..13c51d31 --- /dev/null +++ b/freqtrade.log @@ -0,0 +1,3066 @@ +Creating freqtrade_freqtrade_run ... +Creating freqtrade_freqtrade_run ... done +2025-04-29 01:54:55,246 - freqtrade - INFO - freqtrade 2025.3 +2025-04-29 01:54:55,464 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-04-29 01:54:56,878 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-04-29 01:54:56,879 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ... +2025-04-29 01:54:56,881 - freqtrade.loggers - INFO - Enabling colorized output. +2025-04-29 01:54:56,881 - root - INFO - Logfile configured +2025-04-29 01:54:56,882 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-04-29 01:54:56,882 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250101-20250420 ... +2025-04-29 01:54:56,907 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250101-20250420 +2025-04-29 01:54:56,909 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-04-29 01:54:56,910 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-04-29 01:54:56,916 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-04-29 01:54:56,916 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-04-29 01:54:56,917 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:56,919 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-04-29 01:54:56,919 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-04-29 01:54:56,925 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-04-29 01:54:56,931 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-04-29 01:54:59,471 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-04-29 01:54:59,491 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-04-29 01:54:59,491 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +'minutes'}. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +'stoploss_on_exchange_interval': 60} +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-04-29 01:54:59,502 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:59,505 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-04-29 01:54:59,512 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-04-29 01:54:59,512 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:54:59,513 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Loading data from 2024-12-01 21:30:00 up to 2025-04-20 00:00:00 (139 days). +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-04-29 01:54:59,673 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-04-29 01:55:01,274 - matplotlib.font_manager - INFO - generated new fontManager +2025-04-29 01:55:01,489 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-04-29 01:55:01,490 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-04-29 01:55:01,493 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-04-29 01:55:01,498 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-04-29 01:55:01,500 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:55:01,501 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:55:01,502 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1735689600_prediction.feather +2025-04-29 01:55:01,602 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,603 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,705 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:01,706 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,814 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:55:01,815 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,942 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,943 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,037 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:02,038 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,113 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,118 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:55:02,121 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.152831 +1 0.003285 50.152831 +2 0.001898 50.152831 +3 0.000484 50.152831 +4 0.001688 50.152831 +2025-04-29 01:55:02,123 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,129 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:02,130 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:02,134 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1735689600/cb_btc_1735689600 +2025-04-29 01:55:02,135 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:02,151 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:02,152 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:55:07,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:07,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24624 validation_1-rmse:0.26036 +[1] validation_0-rmse:0.24176 validation_1-rmse:0.25460 +[2] validation_0-rmse:0.23782 validation_1-rmse:0.24904 +[3] validation_0-rmse:0.23408 validation_1-rmse:0.24381 +[4] validation_0-rmse:0.23057 validation_1-rmse:0.23882 +[5] validation_0-rmse:0.22701 validation_1-rmse:0.23409 +[6] validation_0-rmse:0.22400 validation_1-rmse:0.22962 +[7] validation_0-rmse:0.22088 validation_1-rmse:0.22533 +[8] validation_0-rmse:0.21817 validation_1-rmse:0.22130 +[9] validation_0-rmse:0.21491 validation_1-rmse:0.21740 +[10] validation_0-rmse:0.21265 validation_1-rmse:0.21347 +[11] validation_0-rmse:0.20982 validation_1-rmse:0.20978 +[12] validation_0-rmse:0.20747 validation_1-rmse:0.20640 +[13] validation_0-rmse:0.20512 validation_1-rmse:0.20299 +[14] validation_0-rmse:0.20280 validation_1-rmse:0.19966 +[15] validation_0-rmse:0.20012 validation_1-rmse:0.19656 +[16] validation_0-rmse:0.19785 validation_1-rmse:0.19346 +[17] validation_0-rmse:0.19572 validation_1-rmse:0.19054 +[18] validation_0-rmse:0.19400 validation_1-rmse:0.18759 +[19] validation_0-rmse:0.19164 validation_1-rmse:0.18488 +[20] validation_0-rmse:0.18956 validation_1-rmse:0.18205 +[21] validation_0-rmse:0.18746 validation_1-rmse:0.17951 +[22] validation_0-rmse:0.18593 validation_1-rmse:0.17696 +[23] validation_0-rmse:0.18395 validation_1-rmse:0.17465 +[24] validation_0-rmse:0.18249 validation_1-rmse:0.17217 +[25] validation_0-rmse:0.18084 validation_1-rmse:0.16993 +[26] validation_0-rmse:0.17928 validation_1-rmse:0.16771 +[27] validation_0-rmse:0.17776 validation_1-rmse:0.16571 +[28] validation_0-rmse:0.17652 validation_1-rmse:0.16356 +[29] validation_0-rmse:0.17499 validation_1-rmse:0.16166 +[30] validation_0-rmse:0.17371 validation_1-rmse:0.15983 +[31] validation_0-rmse:0.17243 validation_1-rmse:0.15792 +[32] validation_0-rmse:0.17110 validation_1-rmse:0.15628 +[33] validation_0-rmse:0.16996 validation_1-rmse:0.15433 +[34] validation_0-rmse:0.16884 validation_1-rmse:0.15277 +[35] validation_0-rmse:0.16785 validation_1-rmse:0.15090 +[36] validation_0-rmse:0.16682 validation_1-rmse:0.14942 +[37] validation_0-rmse:0.16559 validation_1-rmse:0.14774 +[38] validation_0-rmse:0.16459 validation_1-rmse:0.14628 +[39] validation_0-rmse:0.16356 validation_1-rmse:0.14466 +[40] validation_0-rmse:0.16250 validation_1-rmse:0.14330 +[41] validation_0-rmse:0.16153 validation_1-rmse:0.14201 +[42] validation_0-rmse:0.16059 validation_1-rmse:0.14075 +[43] validation_0-rmse:0.15986 validation_1-rmse:0.13938 +[44] validation_0-rmse:0.15908 validation_1-rmse:0.13822 +[45] validation_0-rmse:0.15810 validation_1-rmse:0.13687 +[46] validation_0-rmse:0.15733 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.15655 validation_1-rmse:0.13458 +[48] validation_0-rmse:0.15580 validation_1-rmse:0.13355 +[49] validation_0-rmse:0.15512 validation_1-rmse:0.13228 +[50] validation_0-rmse:0.15434 validation_1-rmse:0.13121 +[51] validation_0-rmse:0.15363 validation_1-rmse:0.13030 +[52] validation_0-rmse:0.15294 validation_1-rmse:0.12937 +[53] validation_0-rmse:0.15243 validation_1-rmse:0.12818 +[54] validation_0-rmse:0.15170 validation_1-rmse:0.12720 +[55] validation_0-rmse:0.15096 validation_1-rmse:0.12632 +[56] validation_0-rmse:0.15035 validation_1-rmse:0.12538 +[57] validation_0-rmse:0.14977 validation_1-rmse:0.12453 +[58] validation_0-rmse:0.14914 validation_1-rmse:0.12363 +[59] validation_0-rmse:0.14867 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.14819 validation_1-rmse:0.12183 +[61] validation_0-rmse:0.14763 validation_1-rmse:0.12108 +[62] validation_0-rmse:0.14706 validation_1-rmse:0.12035 +[63] validation_0-rmse:0.14648 validation_1-rmse:0.11946 +[64] validation_0-rmse:0.14601 validation_1-rmse:0.11876 +[65] validation_0-rmse:0.14553 validation_1-rmse:0.11808 +[66] validation_0-rmse:0.14506 validation_1-rmse:0.11742 +[67] validation_0-rmse:0.14469 validation_1-rmse:0.11671 +[68] validation_0-rmse:0.14422 validation_1-rmse:0.11604 +[69] validation_0-rmse:0.14381 validation_1-rmse:0.11543 +[70] validation_0-rmse:0.14337 validation_1-rmse:0.11485 +[71] validation_0-rmse:0.14294 validation_1-rmse:0.11398 +[72] validation_0-rmse:0.14260 validation_1-rmse:0.11335 +[73] validation_0-rmse:0.14223 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14190 validation_1-rmse:0.11225 +[75] validation_0-rmse:0.14144 validation_1-rmse:0.11143 +[76] validation_0-rmse:0.14098 validation_1-rmse:0.11052 +[77] validation_0-rmse:0.14062 validation_1-rmse:0.10998 +[78] validation_0-rmse:0.14029 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.13993 validation_1-rmse:0.10888 +[80] validation_0-rmse:0.13958 validation_1-rmse:0.10839 +[81] validation_0-rmse:0.13918 validation_1-rmse:0.10767 +[82] validation_0-rmse:0.13897 validation_1-rmse:0.10720 +[83] validation_0-rmse:0.13864 validation_1-rmse:0.10669 +[84] validation_0-rmse:0.13836 validation_1-rmse:0.10620 +[85] validation_0-rmse:0.13810 validation_1-rmse:0.10573 +[86] validation_0-rmse:0.13782 validation_1-rmse:0.10526 +[87] validation_0-rmse:0.13756 validation_1-rmse:0.10458 +[88] validation_0-rmse:0.13736 validation_1-rmse:0.10420 +[89] validation_0-rmse:0.13708 validation_1-rmse:0.10383 +[90] validation_0-rmse:0.13685 validation_1-rmse:0.10343 +[91] validation_0-rmse:0.13658 validation_1-rmse:0.10298 +[92] validation_0-rmse:0.13646 validation_1-rmse:0.10231 +[93] validation_0-rmse:0.13615 validation_1-rmse:0.10190 +[94] validation_0-rmse:0.13589 validation_1-rmse:0.10154 +[95] validation_0-rmse:0.13572 validation_1-rmse:0.10095 +[96] validation_0-rmse:0.13550 validation_1-rmse:0.10058 +[97] validation_0-rmse:0.13530 validation_1-rmse:0.10026 +[98] validation_0-rmse:0.13513 validation_1-rmse:0.09995 +[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950 +2025-04-29 01:55:08,221 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.09 secs) -------------------- +2025-04-29 01:55:08,222 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:08,903 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:55:08,904 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1736553600_prediction.feather +2025-04-29 01:55:08,907 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,912 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:08,914 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:08,917 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,924 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:08,925 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:08,929 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1736553600/cb_btc_1736553600 +2025-04-29 01:55:08,930 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:08,946 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:08,947 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:55:13,908 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:55:13,911 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:13,912 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26037 validation_1-rmse:0.25324 +[1] validation_0-rmse:0.25572 validation_1-rmse:0.24787 +[2] validation_0-rmse:0.25117 validation_1-rmse:0.24281 +[3] validation_0-rmse:0.24697 validation_1-rmse:0.23802 +[4] validation_0-rmse:0.24328 validation_1-rmse:0.23332 +[5] validation_0-rmse:0.23939 validation_1-rmse:0.22905 +[6] validation_0-rmse:0.23522 validation_1-rmse:0.22484 +[7] validation_0-rmse:0.23148 validation_1-rmse:0.22085 +[8] validation_0-rmse:0.22873 validation_1-rmse:0.21697 +[9] validation_0-rmse:0.22519 validation_1-rmse:0.21317 +[10] validation_0-rmse:0.22206 validation_1-rmse:0.20963 +[11] validation_0-rmse:0.21866 validation_1-rmse:0.20626 +[12] validation_0-rmse:0.21563 validation_1-rmse:0.20296 +[13] validation_0-rmse:0.21313 validation_1-rmse:0.19956 +[14] validation_0-rmse:0.21062 validation_1-rmse:0.19636 +[15] validation_0-rmse:0.20808 validation_1-rmse:0.19339 +[16] validation_0-rmse:0.20570 validation_1-rmse:0.19058 +[17] validation_0-rmse:0.20318 validation_1-rmse:0.18781 +[18] validation_0-rmse:0.20113 validation_1-rmse:0.18518 +[19] validation_0-rmse:0.19934 validation_1-rmse:0.18248 +[20] validation_0-rmse:0.19735 validation_1-rmse:0.18006 +[21] validation_0-rmse:0.19541 validation_1-rmse:0.17744 +[22] validation_0-rmse:0.19336 validation_1-rmse:0.17517 +[23] validation_0-rmse:0.19145 validation_1-rmse:0.17301 +[24] validation_0-rmse:0.18989 validation_1-rmse:0.17058 +[25] validation_0-rmse:0.18782 validation_1-rmse:0.16854 +[26] validation_0-rmse:0.18634 validation_1-rmse:0.16625 +[27] validation_0-rmse:0.18471 validation_1-rmse:0.16430 +[28] validation_0-rmse:0.18312 validation_1-rmse:0.16236 +[29] validation_0-rmse:0.18157 validation_1-rmse:0.16053 +[30] validation_0-rmse:0.17991 validation_1-rmse:0.15849 +[31] validation_0-rmse:0.17839 validation_1-rmse:0.15677 +[32] validation_0-rmse:0.17693 validation_1-rmse:0.15498 +[33] validation_0-rmse:0.17574 validation_1-rmse:0.15336 +[34] validation_0-rmse:0.17469 validation_1-rmse:0.15168 +[35] validation_0-rmse:0.17352 validation_1-rmse:0.15015 +[36] validation_0-rmse:0.17228 validation_1-rmse:0.14868 +[37] validation_0-rmse:0.17127 validation_1-rmse:0.14692 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.14553 +[39] validation_0-rmse:0.16926 validation_1-rmse:0.14420 +[40] validation_0-rmse:0.16821 validation_1-rmse:0.14297 +[41] validation_0-rmse:0.16740 validation_1-rmse:0.14144 +[42] validation_0-rmse:0.16647 validation_1-rmse:0.14020 +[43] validation_0-rmse:0.16548 validation_1-rmse:0.13903 +[44] validation_0-rmse:0.16440 validation_1-rmse:0.13765 +[45] validation_0-rmse:0.16353 validation_1-rmse:0.13652 +[46] validation_0-rmse:0.16269 validation_1-rmse:0.13522 +[47] validation_0-rmse:0.16193 validation_1-rmse:0.13419 +[48] validation_0-rmse:0.16114 validation_1-rmse:0.13311 +[49] validation_0-rmse:0.16043 validation_1-rmse:0.13214 +[50] validation_0-rmse:0.15971 validation_1-rmse:0.13090 +[51] validation_0-rmse:0.15909 validation_1-rmse:0.12992 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.12899 +[53] validation_0-rmse:0.15763 validation_1-rmse:0.12809 +[54] validation_0-rmse:0.15697 validation_1-rmse:0.12724 +[55] validation_0-rmse:0.15631 validation_1-rmse:0.12637 +[56] validation_0-rmse:0.15553 validation_1-rmse:0.12535 +[57] validation_0-rmse:0.15494 validation_1-rmse:0.12456 +[58] validation_0-rmse:0.15452 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.15396 validation_1-rmse:0.12273 +[60] validation_0-rmse:0.15334 validation_1-rmse:0.12196 +[61] validation_0-rmse:0.15274 validation_1-rmse:0.12123 +[62] validation_0-rmse:0.15221 validation_1-rmse:0.12048 +[63] validation_0-rmse:0.15176 validation_1-rmse:0.11953 +[64] validation_0-rmse:0.15133 validation_1-rmse:0.11887 +[65] validation_0-rmse:0.15080 validation_1-rmse:0.11796 +[66] validation_0-rmse:0.15035 validation_1-rmse:0.11734 +[67] validation_0-rmse:0.14995 validation_1-rmse:0.11667 +[68] validation_0-rmse:0.14954 validation_1-rmse:0.11616 +[69] validation_0-rmse:0.14916 validation_1-rmse:0.11535 +[70] validation_0-rmse:0.14887 validation_1-rmse:0.11469 +[71] validation_0-rmse:0.14854 validation_1-rmse:0.11408 +[72] validation_0-rmse:0.14811 validation_1-rmse:0.11334 +[73] validation_0-rmse:0.14766 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14738 validation_1-rmse:0.11231 +[75] validation_0-rmse:0.14697 validation_1-rmse:0.11184 +[76] validation_0-rmse:0.14663 validation_1-rmse:0.11108 +[77] validation_0-rmse:0.14635 validation_1-rmse:0.11058 +[78] validation_0-rmse:0.14591 validation_1-rmse:0.10984 +[79] validation_0-rmse:0.14561 validation_1-rmse:0.10929 +[80] validation_0-rmse:0.14529 validation_1-rmse:0.10875 +[81] validation_0-rmse:0.14510 validation_1-rmse:0.10826 +[82] validation_0-rmse:0.14471 validation_1-rmse:0.10772 +[83] validation_0-rmse:0.14444 validation_1-rmse:0.10725 +[84] validation_0-rmse:0.14420 validation_1-rmse:0.10652 +[85] validation_0-rmse:0.14393 validation_1-rmse:0.10608 +[86] validation_0-rmse:0.14371 validation_1-rmse:0.10567 +[87] validation_0-rmse:0.14342 validation_1-rmse:0.10528 +[88] validation_0-rmse:0.14314 validation_1-rmse:0.10483 +[89] validation_0-rmse:0.14307 validation_1-rmse:0.10439 +[90] validation_0-rmse:0.14273 validation_1-rmse:0.10395 +[91] validation_0-rmse:0.14237 validation_1-rmse:0.10353 +[92] validation_0-rmse:0.14210 validation_1-rmse:0.10318 +[93] validation_0-rmse:0.14186 validation_1-rmse:0.10279 +[94] validation_0-rmse:0.14175 validation_1-rmse:0.10234 +[95] validation_0-rmse:0.14153 validation_1-rmse:0.10204 +[96] validation_0-rmse:0.14142 validation_1-rmse:0.10160 +[97] validation_0-rmse:0.14124 validation_1-rmse:0.10126 +[98] validation_0-rmse:0.14102 validation_1-rmse:0.10068 +[99] validation_0-rmse:0.14079 validation_1-rmse:0.10036 +2025-04-29 01:55:14,692 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.76 secs) -------------------- +2025-04-29 01:55:14,693 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:15,250 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:55:15,250 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1737417600_prediction.feather +2025-04-29 01:55:15,254 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,261 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:15,262 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:15,268 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,275 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:15,276 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:15,281 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1737417600/cb_btc_1737417600 +2025-04-29 01:55:15,281 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:15,297 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:15,298 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:55:20,324 - datasieve.pipeline - INFO - DI tossed 1622 predictions for being too far from training data. +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25769 validation_1-rmse:0.25549 +[1] validation_0-rmse:0.25314 validation_1-rmse:0.24986 +[2] validation_0-rmse:0.24864 validation_1-rmse:0.24456 +[3] validation_0-rmse:0.24486 validation_1-rmse:0.23955 +[4] validation_0-rmse:0.24144 validation_1-rmse:0.23480 +[5] validation_0-rmse:0.23803 validation_1-rmse:0.23024 +[6] validation_0-rmse:0.23468 validation_1-rmse:0.22599 +[7] validation_0-rmse:0.23134 validation_1-rmse:0.22162 +[8] validation_0-rmse:0.22843 validation_1-rmse:0.21773 +[9] validation_0-rmse:0.22560 validation_1-rmse:0.21396 +[10] validation_0-rmse:0.22402 validation_1-rmse:0.21023 +[11] validation_0-rmse:0.22155 validation_1-rmse:0.20680 +[12] validation_0-rmse:0.21899 validation_1-rmse:0.20342 +[13] validation_0-rmse:0.21654 validation_1-rmse:0.20029 +[14] validation_0-rmse:0.21431 validation_1-rmse:0.19719 +[15] validation_0-rmse:0.21282 validation_1-rmse:0.19411 +[16] validation_0-rmse:0.21076 validation_1-rmse:0.19117 +[17] validation_0-rmse:0.20882 validation_1-rmse:0.18835 +[18] validation_0-rmse:0.20695 validation_1-rmse:0.18547 +[19] validation_0-rmse:0.20538 validation_1-rmse:0.18292 +[20] validation_0-rmse:0.20345 validation_1-rmse:0.18038 +[21] validation_0-rmse:0.20148 validation_1-rmse:0.17799 +[22] validation_0-rmse:0.19991 validation_1-rmse:0.17569 +[23] validation_0-rmse:0.19832 validation_1-rmse:0.17350 +[24] validation_0-rmse:0.19658 validation_1-rmse:0.17096 +[25] validation_0-rmse:0.19474 validation_1-rmse:0.16879 +[26] validation_0-rmse:0.19292 validation_1-rmse:0.16665 +[27] validation_0-rmse:0.19134 validation_1-rmse:0.16470 +[28] validation_0-rmse:0.19034 validation_1-rmse:0.16253 +[29] validation_0-rmse:0.18882 validation_1-rmse:0.16068 +[30] validation_0-rmse:0.18736 validation_1-rmse:0.15892 +[31] validation_0-rmse:0.18605 validation_1-rmse:0.15690 +[32] validation_0-rmse:0.18481 validation_1-rmse:0.15521 +[33] validation_0-rmse:0.18346 validation_1-rmse:0.15356 +[34] validation_0-rmse:0.18222 validation_1-rmse:0.15188 +[35] validation_0-rmse:0.18095 validation_1-rmse:0.15028 +[36] validation_0-rmse:0.18015 validation_1-rmse:0.14857 +[37] validation_0-rmse:0.17915 validation_1-rmse:0.14713 +[38] validation_0-rmse:0.17817 validation_1-rmse:0.14573 +[39] validation_0-rmse:0.17723 validation_1-rmse:0.14437 +[40] validation_0-rmse:0.17619 validation_1-rmse:0.14308 +[41] validation_0-rmse:0.17509 validation_1-rmse:0.14176 +[42] validation_0-rmse:0.17407 validation_1-rmse:0.14047 +[43] validation_0-rmse:0.17340 validation_1-rmse:0.13921 +[44] validation_0-rmse:0.17245 validation_1-rmse:0.13806 +[45] validation_0-rmse:0.17212 validation_1-rmse:0.13685 +[46] validation_0-rmse:0.17133 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.17064 validation_1-rmse:0.13451 +[48] validation_0-rmse:0.17004 validation_1-rmse:0.13331 +[49] validation_0-rmse:0.16941 validation_1-rmse:0.13222 +[50] validation_0-rmse:0.16858 validation_1-rmse:0.13123 +[51] validation_0-rmse:0.16786 validation_1-rmse:0.13007 +[52] validation_0-rmse:0.16718 validation_1-rmse:0.12912 +[53] validation_0-rmse:0.16651 validation_1-rmse:0.12806 +[54] validation_0-rmse:0.16592 validation_1-rmse:0.12709 +[55] validation_0-rmse:0.16542 validation_1-rmse:0.12604 +[56] validation_0-rmse:0.16479 validation_1-rmse:0.12523 +[57] validation_0-rmse:0.16426 validation_1-rmse:0.12439 +[58] validation_0-rmse:0.16363 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.16325 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.16289 validation_1-rmse:0.12173 +[61] validation_0-rmse:0.16226 validation_1-rmse:0.12099 +[62] validation_0-rmse:0.16176 validation_1-rmse:0.12010 +[63] validation_0-rmse:0.16144 validation_1-rmse:0.11936 +[64] validation_0-rmse:0.16088 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.16030 validation_1-rmse:0.11786 +[66] validation_0-rmse:0.15991 validation_1-rmse:0.11714 +[67] validation_0-rmse:0.15947 validation_1-rmse:0.11640 +[68] validation_0-rmse:0.15912 validation_1-rmse:0.11574 +[69] validation_0-rmse:0.15874 validation_1-rmse:0.11507 +[70] validation_0-rmse:0.15837 validation_1-rmse:0.11430 +[71] validation_0-rmse:0.15798 validation_1-rmse:0.11365 +[72] validation_0-rmse:0.15763 validation_1-rmse:0.11305 +[73] validation_0-rmse:0.15713 validation_1-rmse:0.11250 +[74] validation_0-rmse:0.15648 validation_1-rmse:0.11177 +[75] validation_0-rmse:0.15619 validation_1-rmse:0.11122 +[76] validation_0-rmse:0.15593 validation_1-rmse:0.11066 +[77] validation_0-rmse:0.15562 validation_1-rmse:0.11007 +[78] validation_0-rmse:0.15519 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.15500 validation_1-rmse:0.10883 +[80] validation_0-rmse:0.15461 validation_1-rmse:0.10835 +[81] validation_0-rmse:0.15417 validation_1-rmse:0.10780 +[82] validation_0-rmse:0.15393 validation_1-rmse:0.10742 +[83] validation_0-rmse:0.15395 validation_1-rmse:0.10634 +[84] validation_0-rmse:0.15359 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.15315 validation_1-rmse:0.10539 +[86] validation_0-rmse:0.15315 validation_1-rmse:0.10440 +[87] validation_0-rmse:0.15278 validation_1-rmse:0.10400 +[88] validation_0-rmse:0.15239 validation_1-rmse:0.10353 +[89] validation_0-rmse:0.15200 validation_1-rmse:0.10310 +[90] validation_0-rmse:0.15182 validation_1-rmse:0.10245 +[91] validation_0-rmse:0.15175 validation_1-rmse:0.10182 +[92] validation_0-rmse:0.15139 validation_1-rmse:0.10138 +[93] validation_0-rmse:0.15105 validation_1-rmse:0.10095 +[94] validation_0-rmse:0.15091 validation_1-rmse:0.10056 +[95] validation_0-rmse:0.15088 validation_1-rmse:0.09964 +[96] validation_0-rmse:0.15065 validation_1-rmse:0.09927 +[97] validation_0-rmse:0.15036 validation_1-rmse:0.09888 +[98] validation_0-rmse:0.15021 validation_1-rmse:0.09852 +[99] validation_0-rmse:0.15004 validation_1-rmse:0.09815 +2025-04-29 01:55:21,007 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.73 secs) -------------------- +2025-04-29 01:55:21,008 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:21,504 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:55:21,505 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1738281600_prediction.feather +2025-04-29 01:55:21,510 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,516 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:21,517 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:21,522 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,528 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:21,529 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:21,533 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1738281600/cb_btc_1738281600 +2025-04-29 01:55:21,534 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:21,550 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:21,550 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:55:26,605 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:26,606 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25046 validation_1-rmse:0.26128 +[1] validation_0-rmse:0.24588 validation_1-rmse:0.25570 +[2] validation_0-rmse:0.24156 validation_1-rmse:0.25047 +[3] validation_0-rmse:0.23757 validation_1-rmse:0.24551 +[4] validation_0-rmse:0.23411 validation_1-rmse:0.24075 +[5] validation_0-rmse:0.23029 validation_1-rmse:0.23637 +[6] validation_0-rmse:0.22707 validation_1-rmse:0.23199 +[7] validation_0-rmse:0.22405 validation_1-rmse:0.22801 +[8] validation_0-rmse:0.22083 validation_1-rmse:0.22420 +[9] validation_0-rmse:0.21768 validation_1-rmse:0.22038 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.21674 +[11] validation_0-rmse:0.21187 validation_1-rmse:0.21322 +[12] validation_0-rmse:0.20911 validation_1-rmse:0.20996 +[13] validation_0-rmse:0.20669 validation_1-rmse:0.20679 +[14] validation_0-rmse:0.20441 validation_1-rmse:0.20366 +[15] validation_0-rmse:0.20250 validation_1-rmse:0.20054 +[16] validation_0-rmse:0.20017 validation_1-rmse:0.19757 +[17] validation_0-rmse:0.19804 validation_1-rmse:0.19490 +[18] validation_0-rmse:0.19618 validation_1-rmse:0.19221 +[19] validation_0-rmse:0.19404 validation_1-rmse:0.18954 +[20] validation_0-rmse:0.19209 validation_1-rmse:0.18666 +[21] validation_0-rmse:0.19014 validation_1-rmse:0.18430 +[22] validation_0-rmse:0.18845 validation_1-rmse:0.18197 +[23] validation_0-rmse:0.18653 validation_1-rmse:0.17972 +[24] validation_0-rmse:0.18468 validation_1-rmse:0.17722 +[25] validation_0-rmse:0.18325 validation_1-rmse:0.17491 +[26] validation_0-rmse:0.18152 validation_1-rmse:0.17284 +[27] validation_0-rmse:0.17999 validation_1-rmse:0.17092 +[28] validation_0-rmse:0.17846 validation_1-rmse:0.16892 +[29] validation_0-rmse:0.17696 validation_1-rmse:0.16709 +[30] validation_0-rmse:0.17558 validation_1-rmse:0.16510 +[31] validation_0-rmse:0.17418 validation_1-rmse:0.16335 +[32] validation_0-rmse:0.17293 validation_1-rmse:0.16161 +[33] validation_0-rmse:0.17159 validation_1-rmse:0.16003 +[34] validation_0-rmse:0.17030 validation_1-rmse:0.15831 +[35] validation_0-rmse:0.16907 validation_1-rmse:0.15681 +[36] validation_0-rmse:0.16796 validation_1-rmse:0.15513 +[37] validation_0-rmse:0.16690 validation_1-rmse:0.15349 +[38] validation_0-rmse:0.16580 validation_1-rmse:0.15204 +[39] validation_0-rmse:0.16492 validation_1-rmse:0.15050 +[40] validation_0-rmse:0.16383 validation_1-rmse:0.14918 +[41] validation_0-rmse:0.16281 validation_1-rmse:0.14788 +[42] validation_0-rmse:0.16176 validation_1-rmse:0.14660 +[43] validation_0-rmse:0.16082 validation_1-rmse:0.14516 +[44] validation_0-rmse:0.15990 validation_1-rmse:0.14395 +[45] validation_0-rmse:0.15891 validation_1-rmse:0.14281 +[46] validation_0-rmse:0.15797 validation_1-rmse:0.14168 +[47] validation_0-rmse:0.15712 validation_1-rmse:0.14040 +[48] validation_0-rmse:0.15632 validation_1-rmse:0.13933 +[49] validation_0-rmse:0.15542 validation_1-rmse:0.13821 +[50] validation_0-rmse:0.15458 validation_1-rmse:0.13705 +[51] validation_0-rmse:0.15404 validation_1-rmse:0.13583 +[52] validation_0-rmse:0.15334 validation_1-rmse:0.13483 +[53] validation_0-rmse:0.15256 validation_1-rmse:0.13387 +[54] validation_0-rmse:0.15190 validation_1-rmse:0.13290 +[55] validation_0-rmse:0.15122 validation_1-rmse:0.13174 +[56] validation_0-rmse:0.15065 validation_1-rmse:0.13080 +[57] validation_0-rmse:0.15006 validation_1-rmse:0.12993 +[58] validation_0-rmse:0.14955 validation_1-rmse:0.12897 +[59] validation_0-rmse:0.14893 validation_1-rmse:0.12814 +[60] validation_0-rmse:0.14843 validation_1-rmse:0.12735 +[61] validation_0-rmse:0.14789 validation_1-rmse:0.12642 +[62] validation_0-rmse:0.14718 validation_1-rmse:0.12561 +[63] validation_0-rmse:0.14659 validation_1-rmse:0.12486 +[64] validation_0-rmse:0.14600 validation_1-rmse:0.12397 +[65] validation_0-rmse:0.14547 validation_1-rmse:0.12324 +[66] validation_0-rmse:0.14499 validation_1-rmse:0.12255 +[67] validation_0-rmse:0.14451 validation_1-rmse:0.12188 +[68] validation_0-rmse:0.14393 validation_1-rmse:0.12114 +[69] validation_0-rmse:0.14346 validation_1-rmse:0.12048 +[70] validation_0-rmse:0.14293 validation_1-rmse:0.11974 +[71] validation_0-rmse:0.14256 validation_1-rmse:0.11893 +[72] validation_0-rmse:0.14212 validation_1-rmse:0.11830 +[73] validation_0-rmse:0.14177 validation_1-rmse:0.11748 +[74] validation_0-rmse:0.14134 validation_1-rmse:0.11686 +[75] validation_0-rmse:0.14101 validation_1-rmse:0.11609 +[76] validation_0-rmse:0.14060 validation_1-rmse:0.11536 +[77] validation_0-rmse:0.14020 validation_1-rmse:0.11484 +[78] validation_0-rmse:0.13983 validation_1-rmse:0.11412 +[79] validation_0-rmse:0.13951 validation_1-rmse:0.11357 +[80] validation_0-rmse:0.13928 validation_1-rmse:0.11273 +[81] validation_0-rmse:0.13889 validation_1-rmse:0.11221 +[82] validation_0-rmse:0.13855 validation_1-rmse:0.11166 +[83] validation_0-rmse:0.13824 validation_1-rmse:0.11114 +[84] validation_0-rmse:0.13808 validation_1-rmse:0.11050 +[85] validation_0-rmse:0.13767 validation_1-rmse:0.10998 +[86] validation_0-rmse:0.13731 validation_1-rmse:0.10947 +[87] validation_0-rmse:0.13716 validation_1-rmse:0.10876 +[88] validation_0-rmse:0.13678 validation_1-rmse:0.10832 +[89] validation_0-rmse:0.13659 validation_1-rmse:0.10782 +[90] validation_0-rmse:0.13629 validation_1-rmse:0.10736 +[91] validation_0-rmse:0.13600 validation_1-rmse:0.10662 +[92] validation_0-rmse:0.13577 validation_1-rmse:0.10613 +[93] validation_0-rmse:0.13541 validation_1-rmse:0.10565 +[94] validation_0-rmse:0.13534 validation_1-rmse:0.10501 +[95] validation_0-rmse:0.13511 validation_1-rmse:0.10453 +[96] validation_0-rmse:0.13483 validation_1-rmse:0.10401 +[97] validation_0-rmse:0.13455 validation_1-rmse:0.10362 +[98] validation_0-rmse:0.13425 validation_1-rmse:0.10323 +[99] validation_0-rmse:0.13402 validation_1-rmse:0.10289 +2025-04-29 01:55:27,556 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.02 secs) -------------------- +2025-04-29 01:55:27,557 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:28,076 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:55:28,077 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1739145600_prediction.feather +2025-04-29 01:55:28,081 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,088 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:28,089 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:28,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,100 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:28,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:28,106 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1739145600/cb_btc_1739145600 +2025-04-29 01:55:28,107 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:28,123 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:28,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:55:33,123 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:33,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26428 validation_1-rmse:0.27464 +[1] validation_0-rmse:0.25911 validation_1-rmse:0.26865 +[2] validation_0-rmse:0.25427 validation_1-rmse:0.26296 +[3] validation_0-rmse:0.24970 validation_1-rmse:0.25748 +[4] validation_0-rmse:0.24525 validation_1-rmse:0.25222 +[5] validation_0-rmse:0.24140 validation_1-rmse:0.24725 +[6] validation_0-rmse:0.23748 validation_1-rmse:0.24264 +[7] validation_0-rmse:0.23368 validation_1-rmse:0.23792 +[8] validation_0-rmse:0.23022 validation_1-rmse:0.23363 +[9] validation_0-rmse:0.22695 validation_1-rmse:0.22945 +[10] validation_0-rmse:0.22381 validation_1-rmse:0.22543 +[11] validation_0-rmse:0.22105 validation_1-rmse:0.22154 +[12] validation_0-rmse:0.21818 validation_1-rmse:0.21797 +[13] validation_0-rmse:0.21526 validation_1-rmse:0.21430 +[14] validation_0-rmse:0.21284 validation_1-rmse:0.21101 +[15] validation_0-rmse:0.21034 validation_1-rmse:0.20769 +[16] validation_0-rmse:0.20802 validation_1-rmse:0.20438 +[17] validation_0-rmse:0.20590 validation_1-rmse:0.20136 +[18] validation_0-rmse:0.20386 validation_1-rmse:0.19837 +[19] validation_0-rmse:0.20219 validation_1-rmse:0.19549 +[20] validation_0-rmse:0.20037 validation_1-rmse:0.19283 +[21] validation_0-rmse:0.19826 validation_1-rmse:0.19005 +[22] validation_0-rmse:0.19657 validation_1-rmse:0.18750 +[23] validation_0-rmse:0.19525 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.19373 validation_1-rmse:0.18267 +[25] validation_0-rmse:0.19197 validation_1-rmse:0.18037 +[26] validation_0-rmse:0.19063 validation_1-rmse:0.17799 +[27] validation_0-rmse:0.18897 validation_1-rmse:0.17587 +[28] validation_0-rmse:0.18765 validation_1-rmse:0.17382 +[29] validation_0-rmse:0.18608 validation_1-rmse:0.17185 +[30] validation_0-rmse:0.18456 validation_1-rmse:0.16992 +[31] validation_0-rmse:0.18340 validation_1-rmse:0.16793 +[32] validation_0-rmse:0.18206 validation_1-rmse:0.16616 +[33] validation_0-rmse:0.18077 validation_1-rmse:0.16437 +[34] validation_0-rmse:0.17960 validation_1-rmse:0.16270 +[35] validation_0-rmse:0.17857 validation_1-rmse:0.16105 +[36] validation_0-rmse:0.17748 validation_1-rmse:0.15925 +[37] validation_0-rmse:0.17649 validation_1-rmse:0.15762 +[38] validation_0-rmse:0.17540 validation_1-rmse:0.15611 +[39] validation_0-rmse:0.17427 validation_1-rmse:0.15469 +[40] validation_0-rmse:0.17312 validation_1-rmse:0.15301 +[41] validation_0-rmse:0.17217 validation_1-rmse:0.15169 +[42] validation_0-rmse:0.17119 validation_1-rmse:0.15037 +[43] validation_0-rmse:0.17030 validation_1-rmse:0.14910 +[44] validation_0-rmse:0.16939 validation_1-rmse:0.14786 +[45] validation_0-rmse:0.16851 validation_1-rmse:0.14660 +[46] validation_0-rmse:0.16793 validation_1-rmse:0.14518 +[47] validation_0-rmse:0.16760 validation_1-rmse:0.14365 +[48] validation_0-rmse:0.16674 validation_1-rmse:0.14258 +[49] validation_0-rmse:0.16588 validation_1-rmse:0.14152 +[50] validation_0-rmse:0.16505 validation_1-rmse:0.14051 +[51] validation_0-rmse:0.16437 validation_1-rmse:0.13919 +[52] validation_0-rmse:0.16361 validation_1-rmse:0.13818 +[53] validation_0-rmse:0.16290 validation_1-rmse:0.13715 +[54] validation_0-rmse:0.16217 validation_1-rmse:0.13621 +[55] validation_0-rmse:0.16207 validation_1-rmse:0.13493 +[56] validation_0-rmse:0.16153 validation_1-rmse:0.13395 +[57] validation_0-rmse:0.16077 validation_1-rmse:0.13302 +[58] validation_0-rmse:0.16021 validation_1-rmse:0.13218 +[59] validation_0-rmse:0.15972 validation_1-rmse:0.13117 +[60] validation_0-rmse:0.15954 validation_1-rmse:0.13003 +[61] validation_0-rmse:0.15896 validation_1-rmse:0.12926 +[62] validation_0-rmse:0.15849 validation_1-rmse:0.12848 +[63] validation_0-rmse:0.15801 validation_1-rmse:0.12770 +[64] validation_0-rmse:0.15737 validation_1-rmse:0.12678 +[65] validation_0-rmse:0.15736 validation_1-rmse:0.12578 +[66] validation_0-rmse:0.15684 validation_1-rmse:0.12506 +[67] validation_0-rmse:0.15638 validation_1-rmse:0.12437 +[68] validation_0-rmse:0.15618 validation_1-rmse:0.12336 +[69] validation_0-rmse:0.15581 validation_1-rmse:0.12269 +[70] validation_0-rmse:0.15537 validation_1-rmse:0.12205 +[71] validation_0-rmse:0.15534 validation_1-rmse:0.12117 +[72] validation_0-rmse:0.15485 validation_1-rmse:0.12049 +[73] validation_0-rmse:0.15465 validation_1-rmse:0.11968 +[74] validation_0-rmse:0.15430 validation_1-rmse:0.11906 +[75] validation_0-rmse:0.15386 validation_1-rmse:0.11840 +[76] validation_0-rmse:0.15353 validation_1-rmse:0.11781 +[77] validation_0-rmse:0.15354 validation_1-rmse:0.11697 +[78] validation_0-rmse:0.15325 validation_1-rmse:0.11630 +[79] validation_0-rmse:0.15282 validation_1-rmse:0.11572 +[80] validation_0-rmse:0.15239 validation_1-rmse:0.11514 +[81] validation_0-rmse:0.15226 validation_1-rmse:0.11431 +[82] validation_0-rmse:0.15189 validation_1-rmse:0.11381 +[83] validation_0-rmse:0.15171 validation_1-rmse:0.11316 +[84] validation_0-rmse:0.15136 validation_1-rmse:0.11270 +[85] validation_0-rmse:0.15112 validation_1-rmse:0.11212 +[86] validation_0-rmse:0.15112 validation_1-rmse:0.11140 +[87] validation_0-rmse:0.15074 validation_1-rmse:0.11094 +[88] validation_0-rmse:0.15048 validation_1-rmse:0.11035 +[89] validation_0-rmse:0.15026 validation_1-rmse:0.10983 +[90] validation_0-rmse:0.14989 validation_1-rmse:0.10938 +[91] validation_0-rmse:0.14955 validation_1-rmse:0.10893 +[92] validation_0-rmse:0.14955 validation_1-rmse:0.10815 +[93] validation_0-rmse:0.14933 validation_1-rmse:0.10765 +[94] validation_0-rmse:0.14908 validation_1-rmse:0.10711 +[95] validation_0-rmse:0.14889 validation_1-rmse:0.10668 +[96] validation_0-rmse:0.14853 validation_1-rmse:0.10627 +[97] validation_0-rmse:0.14853 validation_1-rmse:0.10553 +[98] validation_0-rmse:0.14835 validation_1-rmse:0.10513 +[99] validation_0-rmse:0.14818 validation_1-rmse:0.10475 +2025-04-29 01:55:33,929 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.82 secs) -------------------- +2025-04-29 01:55:33,930 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:34,433 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:55:34,434 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740009600_prediction.feather +2025-04-29 01:55:34,440 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,447 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:34,448 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:34,453 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,459 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:34,461 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:34,465 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740009600/cb_btc_1740009600 +2025-04-29 01:55:34,466 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:34,482 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:34,483 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:55:39,369 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:39,370 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27166 validation_1-rmse:0.27726 +[1] validation_0-rmse:0.26708 validation_1-rmse:0.27112 +[2] validation_0-rmse:0.26297 validation_1-rmse:0.26523 +[3] validation_0-rmse:0.25865 validation_1-rmse:0.25959 +[4] validation_0-rmse:0.25494 validation_1-rmse:0.25419 +[5] validation_0-rmse:0.25100 validation_1-rmse:0.24913 +[6] validation_0-rmse:0.24763 validation_1-rmse:0.24437 +[7] validation_0-rmse:0.24441 validation_1-rmse:0.23970 +[8] validation_0-rmse:0.24110 validation_1-rmse:0.23527 +[9] validation_0-rmse:0.23801 validation_1-rmse:0.23102 +[10] validation_0-rmse:0.23492 validation_1-rmse:0.22691 +[11] validation_0-rmse:0.23229 validation_1-rmse:0.22297 +[12] validation_0-rmse:0.22956 validation_1-rmse:0.21923 +[13] validation_0-rmse:0.22707 validation_1-rmse:0.21564 +[14] validation_0-rmse:0.22482 validation_1-rmse:0.21221 +[15] validation_0-rmse:0.22237 validation_1-rmse:0.20891 +[16] validation_0-rmse:0.22030 validation_1-rmse:0.20557 +[17] validation_0-rmse:0.21784 validation_1-rmse:0.20243 +[18] validation_0-rmse:0.21591 validation_1-rmse:0.19949 +[19] validation_0-rmse:0.21399 validation_1-rmse:0.19664 +[20] validation_0-rmse:0.21182 validation_1-rmse:0.19378 +[21] validation_0-rmse:0.20992 validation_1-rmse:0.19110 +[22] validation_0-rmse:0.20821 validation_1-rmse:0.18850 +[23] validation_0-rmse:0.20621 validation_1-rmse:0.18597 +[24] validation_0-rmse:0.20490 validation_1-rmse:0.18353 +[25] validation_0-rmse:0.20318 validation_1-rmse:0.18126 +[26] validation_0-rmse:0.20168 validation_1-rmse:0.17896 +[27] validation_0-rmse:0.19992 validation_1-rmse:0.17679 +[28] validation_0-rmse:0.19865 validation_1-rmse:0.17458 +[29] validation_0-rmse:0.19722 validation_1-rmse:0.17257 +[30] validation_0-rmse:0.19571 validation_1-rmse:0.17039 +[31] validation_0-rmse:0.19429 validation_1-rmse:0.16855 +[32] validation_0-rmse:0.19285 validation_1-rmse:0.16664 +[33] validation_0-rmse:0.19141 validation_1-rmse:0.16488 +[34] validation_0-rmse:0.19022 validation_1-rmse:0.16312 +[35] validation_0-rmse:0.18904 validation_1-rmse:0.16145 +[36] validation_0-rmse:0.18832 validation_1-rmse:0.15973 +[37] validation_0-rmse:0.18723 validation_1-rmse:0.15815 +[38] validation_0-rmse:0.18610 validation_1-rmse:0.15653 +[39] validation_0-rmse:0.18504 validation_1-rmse:0.15503 +[40] validation_0-rmse:0.18402 validation_1-rmse:0.15358 +[41] validation_0-rmse:0.18333 validation_1-rmse:0.15193 +[42] validation_0-rmse:0.18213 validation_1-rmse:0.15058 +[43] validation_0-rmse:0.18176 validation_1-rmse:0.14922 +[44] validation_0-rmse:0.18093 validation_1-rmse:0.14792 +[45] validation_0-rmse:0.18017 validation_1-rmse:0.14667 +[46] validation_0-rmse:0.17928 validation_1-rmse:0.14537 +[47] validation_0-rmse:0.17858 validation_1-rmse:0.14420 +[48] validation_0-rmse:0.17770 validation_1-rmse:0.14306 +[49] validation_0-rmse:0.17695 validation_1-rmse:0.14199 +[50] validation_0-rmse:0.17613 validation_1-rmse:0.14094 +[51] validation_0-rmse:0.17545 validation_1-rmse:0.13979 +[52] validation_0-rmse:0.17490 validation_1-rmse:0.13874 +[53] validation_0-rmse:0.17452 validation_1-rmse:0.13755 +[54] validation_0-rmse:0.17383 validation_1-rmse:0.13663 +[55] validation_0-rmse:0.17327 validation_1-rmse:0.13568 +[56] validation_0-rmse:0.17255 validation_1-rmse:0.13477 +[57] validation_0-rmse:0.17192 validation_1-rmse:0.13382 +[58] validation_0-rmse:0.17138 validation_1-rmse:0.13277 +[59] validation_0-rmse:0.17074 validation_1-rmse:0.13188 +[60] validation_0-rmse:0.17026 validation_1-rmse:0.13089 +[61] validation_0-rmse:0.16969 validation_1-rmse:0.13010 +[62] validation_0-rmse:0.16932 validation_1-rmse:0.12904 +[63] validation_0-rmse:0.16888 validation_1-rmse:0.12818 +[64] validation_0-rmse:0.16849 validation_1-rmse:0.12745 +[65] validation_0-rmse:0.16802 validation_1-rmse:0.12639 +[66] validation_0-rmse:0.16747 validation_1-rmse:0.12567 +[67] validation_0-rmse:0.16710 validation_1-rmse:0.12496 +[68] validation_0-rmse:0.16672 validation_1-rmse:0.12426 +[69] validation_0-rmse:0.16635 validation_1-rmse:0.12331 +[70] validation_0-rmse:0.16597 validation_1-rmse:0.12267 +[71] validation_0-rmse:0.16554 validation_1-rmse:0.12196 +[72] validation_0-rmse:0.16522 validation_1-rmse:0.12121 +[73] validation_0-rmse:0.16481 validation_1-rmse:0.12054 +[74] validation_0-rmse:0.16442 validation_1-rmse:0.11996 +[75] validation_0-rmse:0.16409 validation_1-rmse:0.11939 +[76] validation_0-rmse:0.16375 validation_1-rmse:0.11878 +[77] validation_0-rmse:0.16275 validation_1-rmse:0.11753 +[78] validation_0-rmse:0.16248 validation_1-rmse:0.11692 +[79] validation_0-rmse:0.16215 validation_1-rmse:0.11619 +[80] validation_0-rmse:0.16187 validation_1-rmse:0.11564 +[81] validation_0-rmse:0.16150 validation_1-rmse:0.11493 +[82] validation_0-rmse:0.16123 validation_1-rmse:0.11438 +[83] validation_0-rmse:0.16109 validation_1-rmse:0.11358 +[84] validation_0-rmse:0.16065 validation_1-rmse:0.11304 +[85] validation_0-rmse:0.16038 validation_1-rmse:0.11256 +[86] validation_0-rmse:0.16022 validation_1-rmse:0.11205 +[87] validation_0-rmse:0.16007 validation_1-rmse:0.11158 +[88] validation_0-rmse:0.15945 validation_1-rmse:0.11054 +[89] validation_0-rmse:0.15912 validation_1-rmse:0.11008 +[90] validation_0-rmse:0.15894 validation_1-rmse:0.10937 +[91] validation_0-rmse:0.15868 validation_1-rmse:0.10886 +[92] validation_0-rmse:0.15845 validation_1-rmse:0.10844 +[93] validation_0-rmse:0.15817 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.15789 validation_1-rmse:0.10758 +[95] validation_0-rmse:0.15772 validation_1-rmse:0.10721 +[96] validation_0-rmse:0.15763 validation_1-rmse:0.10676 +[97] validation_0-rmse:0.15751 validation_1-rmse:0.10609 +[98] validation_0-rmse:0.15731 validation_1-rmse:0.10574 +[99] validation_0-rmse:0.15738 validation_1-rmse:0.10531 +2025-04-29 01:55:40,266 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.80 secs) -------------------- +2025-04-29 01:55:40,267 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:40,801 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:55:40,802 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740873600_prediction.feather +2025-04-29 01:55:40,807 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,814 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:40,816 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:40,821 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,827 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:40,829 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:40,833 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740873600/cb_btc_1740873600 +2025-04-29 01:55:40,834 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:40,849 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:40,850 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:55:45,643 - datasieve.pipeline - INFO - DI tossed 2275 predictions for being too far from training data. +2025-04-29 01:55:45,646 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:45,647 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27618 validation_1-rmse:0.28955 +[1] validation_0-rmse:0.27005 validation_1-rmse:0.28323 +[2] validation_0-rmse:0.26414 validation_1-rmse:0.27722 +[3] validation_0-rmse:0.25897 validation_1-rmse:0.27161 +[4] validation_0-rmse:0.25425 validation_1-rmse:0.26622 +[5] validation_0-rmse:0.24886 validation_1-rmse:0.26100 +[6] validation_0-rmse:0.24522 validation_1-rmse:0.25606 +[7] validation_0-rmse:0.24137 validation_1-rmse:0.25132 +[8] validation_0-rmse:0.23765 validation_1-rmse:0.24687 +[9] validation_0-rmse:0.23323 validation_1-rmse:0.24254 +[10] validation_0-rmse:0.22900 validation_1-rmse:0.23827 +[11] validation_0-rmse:0.22588 validation_1-rmse:0.23450 +[12] validation_0-rmse:0.22228 validation_1-rmse:0.23055 +[13] validation_0-rmse:0.21872 validation_1-rmse:0.22698 +[14] validation_0-rmse:0.21492 validation_1-rmse:0.22348 +[15] validation_0-rmse:0.21329 validation_1-rmse:0.22011 +[16] validation_0-rmse:0.21024 validation_1-rmse:0.21686 +[17] validation_0-rmse:0.20823 validation_1-rmse:0.21380 +[18] validation_0-rmse:0.20544 validation_1-rmse:0.21075 +[19] validation_0-rmse:0.20415 validation_1-rmse:0.20787 +[20] validation_0-rmse:0.20143 validation_1-rmse:0.20515 +[21] validation_0-rmse:0.19917 validation_1-rmse:0.20247 +[22] validation_0-rmse:0.19745 validation_1-rmse:0.19994 +[23] validation_0-rmse:0.19508 validation_1-rmse:0.19746 +[24] validation_0-rmse:0.19300 validation_1-rmse:0.19490 +[25] validation_0-rmse:0.19085 validation_1-rmse:0.19254 +[26] validation_0-rmse:0.18898 validation_1-rmse:0.19031 +[27] validation_0-rmse:0.18720 validation_1-rmse:0.18794 +[28] validation_0-rmse:0.18503 validation_1-rmse:0.18584 +[29] validation_0-rmse:0.18314 validation_1-rmse:0.18382 +[30] validation_0-rmse:0.18132 validation_1-rmse:0.18164 +[31] validation_0-rmse:0.17984 validation_1-rmse:0.17967 +[32] validation_0-rmse:0.17818 validation_1-rmse:0.17779 +[33] validation_0-rmse:0.17637 validation_1-rmse:0.17572 +[34] validation_0-rmse:0.17473 validation_1-rmse:0.17399 +[35] validation_0-rmse:0.17338 validation_1-rmse:0.17229 +[36] validation_0-rmse:0.17253 validation_1-rmse:0.17055 +[37] validation_0-rmse:0.17149 validation_1-rmse:0.16883 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.16730 +[39] validation_0-rmse:0.16950 validation_1-rmse:0.16556 +[40] validation_0-rmse:0.16815 validation_1-rmse:0.16412 +[41] validation_0-rmse:0.16704 validation_1-rmse:0.16268 +[42] validation_0-rmse:0.16617 validation_1-rmse:0.16128 +[43] validation_0-rmse:0.16542 validation_1-rmse:0.15970 +[44] validation_0-rmse:0.16438 validation_1-rmse:0.15840 +[45] validation_0-rmse:0.16356 validation_1-rmse:0.15692 +[46] validation_0-rmse:0.16239 validation_1-rmse:0.15574 +[47] validation_0-rmse:0.16153 validation_1-rmse:0.15456 +[48] validation_0-rmse:0.16076 validation_1-rmse:0.15314 +[49] validation_0-rmse:0.15998 validation_1-rmse:0.15201 +[50] validation_0-rmse:0.15946 validation_1-rmse:0.15084 +[51] validation_0-rmse:0.15891 validation_1-rmse:0.14954 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.14847 +[53] validation_0-rmse:0.15764 validation_1-rmse:0.14722 +[54] validation_0-rmse:0.15707 validation_1-rmse:0.14623 +[55] validation_0-rmse:0.15653 validation_1-rmse:0.14527 +[56] validation_0-rmse:0.15583 validation_1-rmse:0.14434 +[57] validation_0-rmse:0.15549 validation_1-rmse:0.14329 +[58] validation_0-rmse:0.15507 validation_1-rmse:0.14241 +[59] validation_0-rmse:0.15468 validation_1-rmse:0.14053 +[60] validation_0-rmse:0.15398 validation_1-rmse:0.13968 +[61] validation_0-rmse:0.15390 validation_1-rmse:0.13864 +[62] validation_0-rmse:0.15360 validation_1-rmse:0.13783 +[63] validation_0-rmse:0.15368 validation_1-rmse:0.13704 +[64] validation_0-rmse:0.15338 validation_1-rmse:0.13624 +[65] validation_0-rmse:0.15273 validation_1-rmse:0.13551 +[66] validation_0-rmse:0.15238 validation_1-rmse:0.13451 +[67] validation_0-rmse:0.15212 validation_1-rmse:0.13290 +[68] validation_0-rmse:0.15191 validation_1-rmse:0.13217 +[69] validation_0-rmse:0.15138 validation_1-rmse:0.13143 +[70] validation_0-rmse:0.15090 validation_1-rmse:0.13071 +[71] validation_0-rmse:0.15082 validation_1-rmse:0.13001 +[72] validation_0-rmse:0.14988 validation_1-rmse:0.12847 +[73] validation_0-rmse:0.14953 validation_1-rmse:0.12783 +[74] validation_0-rmse:0.14924 validation_1-rmse:0.12709 +[75] validation_0-rmse:0.14926 validation_1-rmse:0.12578 +[76] validation_0-rmse:0.14903 validation_1-rmse:0.12499 +[77] validation_0-rmse:0.14851 validation_1-rmse:0.12435 +[78] validation_0-rmse:0.14808 validation_1-rmse:0.12368 +[79] validation_0-rmse:0.14768 validation_1-rmse:0.12305 +[80] validation_0-rmse:0.14741 validation_1-rmse:0.12217 +[81] validation_0-rmse:0.14712 validation_1-rmse:0.12165 +[82] validation_0-rmse:0.14696 validation_1-rmse:0.12110 +[83] validation_0-rmse:0.14686 validation_1-rmse:0.12045 +[84] validation_0-rmse:0.14648 validation_1-rmse:0.11984 +[85] validation_0-rmse:0.14623 validation_1-rmse:0.11923 +[86] validation_0-rmse:0.14606 validation_1-rmse:0.11869 +[87] validation_0-rmse:0.14583 validation_1-rmse:0.11754 +[88] validation_0-rmse:0.14572 validation_1-rmse:0.11710 +[89] validation_0-rmse:0.14537 validation_1-rmse:0.11660 +[90] validation_0-rmse:0.14510 validation_1-rmse:0.11614 +[91] validation_0-rmse:0.14516 validation_1-rmse:0.11514 +[92] validation_0-rmse:0.14480 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.14475 validation_1-rmse:0.11414 +[94] validation_0-rmse:0.14443 validation_1-rmse:0.11374 +[95] validation_0-rmse:0.14409 validation_1-rmse:0.11331 +[96] validation_0-rmse:0.14391 validation_1-rmse:0.11240 +[97] validation_0-rmse:0.14303 validation_1-rmse:0.11154 +[98] validation_0-rmse:0.14274 validation_1-rmse:0.11114 +[99] validation_0-rmse:0.14246 validation_1-rmse:0.11071 +2025-04-29 01:55:46,544 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.71 secs) -------------------- +2025-04-29 01:55:46,544 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:47,092 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:55:47,092 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1741737600_prediction.feather +2025-04-29 01:55:47,100 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,107 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:47,109 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:47,115 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,122 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:47,123 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:47,128 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1741737600/cb_btc_1741737600 +2025-04-29 01:55:47,129 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:47,145 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:47,145 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:55:51,987 - datasieve.pipeline - INFO - DI tossed 18 predictions for being too far from training data. +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26738 validation_1-rmse:0.26816 +[1] validation_0-rmse:0.26268 validation_1-rmse:0.26258 +[2] validation_0-rmse:0.25808 validation_1-rmse:0.25725 +[3] validation_0-rmse:0.25395 validation_1-rmse:0.25212 +[4] validation_0-rmse:0.24987 validation_1-rmse:0.24723 +[5] validation_0-rmse:0.24633 validation_1-rmse:0.24263 +[6] validation_0-rmse:0.24308 validation_1-rmse:0.23814 +[7] validation_0-rmse:0.23959 validation_1-rmse:0.23402 +[8] validation_0-rmse:0.23612 validation_1-rmse:0.22977 +[9] validation_0-rmse:0.23322 validation_1-rmse:0.22577 +[10] validation_0-rmse:0.23012 validation_1-rmse:0.22207 +[11] validation_0-rmse:0.22730 validation_1-rmse:0.21843 +[12] validation_0-rmse:0.22453 validation_1-rmse:0.21489 +[13] validation_0-rmse:0.22236 validation_1-rmse:0.21145 +[14] validation_0-rmse:0.22000 validation_1-rmse:0.20841 +[15] validation_0-rmse:0.21744 validation_1-rmse:0.20529 +[16] validation_0-rmse:0.21556 validation_1-rmse:0.20225 +[17] validation_0-rmse:0.21331 validation_1-rmse:0.19932 +[18] validation_0-rmse:0.21171 validation_1-rmse:0.19643 +[19] validation_0-rmse:0.21051 validation_1-rmse:0.19382 +[20] validation_0-rmse:0.20880 validation_1-rmse:0.19128 +[21] validation_0-rmse:0.20711 validation_1-rmse:0.18854 +[22] validation_0-rmse:0.20538 validation_1-rmse:0.18612 +[23] validation_0-rmse:0.20350 validation_1-rmse:0.18381 +[24] validation_0-rmse:0.20234 validation_1-rmse:0.18144 +[25] validation_0-rmse:0.20081 validation_1-rmse:0.17917 +[26] validation_0-rmse:0.19918 validation_1-rmse:0.17714 +[27] validation_0-rmse:0.19804 validation_1-rmse:0.17496 +[28] validation_0-rmse:0.19662 validation_1-rmse:0.17304 +[29] validation_0-rmse:0.19580 validation_1-rmse:0.17082 +[30] validation_0-rmse:0.19454 validation_1-rmse:0.16901 +[31] validation_0-rmse:0.19331 validation_1-rmse:0.16691 +[32] validation_0-rmse:0.19234 validation_1-rmse:0.16517 +[33] validation_0-rmse:0.19118 validation_1-rmse:0.16354 +[34] validation_0-rmse:0.19024 validation_1-rmse:0.16175 +[35] validation_0-rmse:0.18915 validation_1-rmse:0.16020 +[36] validation_0-rmse:0.18823 validation_1-rmse:0.15865 +[37] validation_0-rmse:0.18756 validation_1-rmse:0.15712 +[38] validation_0-rmse:0.18698 validation_1-rmse:0.15541 +[39] validation_0-rmse:0.18643 validation_1-rmse:0.15395 +[40] validation_0-rmse:0.18562 validation_1-rmse:0.15265 +[41] validation_0-rmse:0.18516 validation_1-rmse:0.15124 +[42] validation_0-rmse:0.18421 validation_1-rmse:0.14979 +[43] validation_0-rmse:0.18360 validation_1-rmse:0.14850 +[44] validation_0-rmse:0.18275 validation_1-rmse:0.14733 +[45] validation_0-rmse:0.18253 validation_1-rmse:0.14597 +[46] validation_0-rmse:0.18183 validation_1-rmse:0.14470 +[47] validation_0-rmse:0.18111 validation_1-rmse:0.14361 +[48] validation_0-rmse:0.18060 validation_1-rmse:0.14243 +[49] validation_0-rmse:0.18001 validation_1-rmse:0.14134 +[50] validation_0-rmse:0.17953 validation_1-rmse:0.14030 +[51] validation_0-rmse:0.17899 validation_1-rmse:0.13927 +[52] validation_0-rmse:0.17830 validation_1-rmse:0.13817 +[53] validation_0-rmse:0.17770 validation_1-rmse:0.13720 +[54] validation_0-rmse:0.17702 validation_1-rmse:0.13629 +[55] validation_0-rmse:0.17650 validation_1-rmse:0.13531 +[56] validation_0-rmse:0.17625 validation_1-rmse:0.13440 +[57] validation_0-rmse:0.17580 validation_1-rmse:0.13352 +[58] validation_0-rmse:0.17530 validation_1-rmse:0.13268 +[59] validation_0-rmse:0.17486 validation_1-rmse:0.13166 +[60] validation_0-rmse:0.17438 validation_1-rmse:0.13071 +[61] validation_0-rmse:0.17387 validation_1-rmse:0.12991 +[62] validation_0-rmse:0.17356 validation_1-rmse:0.12914 +[63] validation_0-rmse:0.17311 validation_1-rmse:0.12839 +[64] validation_0-rmse:0.17265 validation_1-rmse:0.12767 +[65] validation_0-rmse:0.17209 validation_1-rmse:0.12682 +[66] validation_0-rmse:0.17197 validation_1-rmse:0.12595 +[67] validation_0-rmse:0.17157 validation_1-rmse:0.12506 +[68] validation_0-rmse:0.17131 validation_1-rmse:0.12439 +[69] validation_0-rmse:0.17088 validation_1-rmse:0.12371 +[70] validation_0-rmse:0.17038 validation_1-rmse:0.12298 +[71] validation_0-rmse:0.17009 validation_1-rmse:0.12235 +[72] validation_0-rmse:0.16979 validation_1-rmse:0.12172 +[73] validation_0-rmse:0.16934 validation_1-rmse:0.12118 +[74] validation_0-rmse:0.16902 validation_1-rmse:0.12050 +[75] validation_0-rmse:0.16881 validation_1-rmse:0.11988 +[76] validation_0-rmse:0.16846 validation_1-rmse:0.11928 +[77] validation_0-rmse:0.16809 validation_1-rmse:0.11846 +[78] validation_0-rmse:0.16774 validation_1-rmse:0.11791 +[79] validation_0-rmse:0.16745 validation_1-rmse:0.11738 +[80] validation_0-rmse:0.16717 validation_1-rmse:0.11683 +[81] validation_0-rmse:0.16702 validation_1-rmse:0.11599 +[82] validation_0-rmse:0.16677 validation_1-rmse:0.11535 +[83] validation_0-rmse:0.16649 validation_1-rmse:0.11468 +[84] validation_0-rmse:0.16605 validation_1-rmse:0.11415 +[85] validation_0-rmse:0.16591 validation_1-rmse:0.11350 +[86] validation_0-rmse:0.16560 validation_1-rmse:0.11303 +[87] validation_0-rmse:0.16531 validation_1-rmse:0.11259 +[88] validation_0-rmse:0.16504 validation_1-rmse:0.11185 +[89] validation_0-rmse:0.16485 validation_1-rmse:0.11134 +[90] validation_0-rmse:0.16463 validation_1-rmse:0.11083 +[91] validation_0-rmse:0.16436 validation_1-rmse:0.11041 +[92] validation_0-rmse:0.16412 validation_1-rmse:0.10988 +[93] validation_0-rmse:0.16388 validation_1-rmse:0.10942 +[94] validation_0-rmse:0.16391 validation_1-rmse:0.10881 +[95] validation_0-rmse:0.16357 validation_1-rmse:0.10838 +[96] validation_0-rmse:0.16358 validation_1-rmse:0.10796 +[97] validation_0-rmse:0.16338 validation_1-rmse:0.10756 +[98] validation_0-rmse:0.16339 validation_1-rmse:0.10688 +[99] validation_0-rmse:0.16321 validation_1-rmse:0.10649 +2025-04-29 01:55:52,741 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.61 secs) -------------------- +2025-04-29 01:55:52,742 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:53,285 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:55:53,286 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1742601600_prediction.feather +2025-04-29 01:55:53,291 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,298 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:53,300 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:53,309 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,316 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:53,318 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:53,322 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1742601600/cb_btc_1742601600 +2025-04-29 01:55:53,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:53,339 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:53,340 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:55:58,184 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:58,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26992 validation_1-rmse:0.26756 +[1] validation_0-rmse:0.26551 validation_1-rmse:0.26201 +[2] validation_0-rmse:0.26111 validation_1-rmse:0.25656 +[3] validation_0-rmse:0.25690 validation_1-rmse:0.25154 +[4] validation_0-rmse:0.25291 validation_1-rmse:0.24683 +[5] validation_0-rmse:0.24933 validation_1-rmse:0.24228 +[6] validation_0-rmse:0.24598 validation_1-rmse:0.23796 +[7] validation_0-rmse:0.24252 validation_1-rmse:0.23392 +[8] validation_0-rmse:0.23953 validation_1-rmse:0.22978 +[9] validation_0-rmse:0.23634 validation_1-rmse:0.22592 +[10] validation_0-rmse:0.23330 validation_1-rmse:0.22229 +[11] validation_0-rmse:0.23059 validation_1-rmse:0.21875 +[12] validation_0-rmse:0.22799 validation_1-rmse:0.21546 +[13] validation_0-rmse:0.22565 validation_1-rmse:0.21212 +[14] validation_0-rmse:0.22329 validation_1-rmse:0.20904 +[15] validation_0-rmse:0.22111 validation_1-rmse:0.20604 +[16] validation_0-rmse:0.21894 validation_1-rmse:0.20318 +[17] validation_0-rmse:0.21715 validation_1-rmse:0.20021 +[18] validation_0-rmse:0.21499 validation_1-rmse:0.19735 +[19] validation_0-rmse:0.21283 validation_1-rmse:0.19480 +[20] validation_0-rmse:0.21109 validation_1-rmse:0.19209 +[21] validation_0-rmse:0.20904 validation_1-rmse:0.18969 +[22] validation_0-rmse:0.20762 validation_1-rmse:0.18718 +[23] validation_0-rmse:0.20580 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.20434 validation_1-rmse:0.18262 +[25] validation_0-rmse:0.20267 validation_1-rmse:0.18048 +[26] validation_0-rmse:0.20106 validation_1-rmse:0.17844 +[27] validation_0-rmse:0.19945 validation_1-rmse:0.17647 +[28] validation_0-rmse:0.19813 validation_1-rmse:0.17443 +[29] validation_0-rmse:0.19669 validation_1-rmse:0.17264 +[30] validation_0-rmse:0.19541 validation_1-rmse:0.17054 +[31] validation_0-rmse:0.19401 validation_1-rmse:0.16881 +[32] validation_0-rmse:0.19263 validation_1-rmse:0.16719 +[33] validation_0-rmse:0.19134 validation_1-rmse:0.16560 +[34] validation_0-rmse:0.18996 validation_1-rmse:0.16365 +[35] validation_0-rmse:0.18864 validation_1-rmse:0.16211 +[36] validation_0-rmse:0.18752 validation_1-rmse:0.16069 +[37] validation_0-rmse:0.18652 validation_1-rmse:0.15898 +[38] validation_0-rmse:0.18540 validation_1-rmse:0.15751 +[39] validation_0-rmse:0.18429 validation_1-rmse:0.15616 +[40] validation_0-rmse:0.18317 validation_1-rmse:0.15475 +[41] validation_0-rmse:0.18215 validation_1-rmse:0.15324 +[42] validation_0-rmse:0.18119 validation_1-rmse:0.15199 +[43] validation_0-rmse:0.18008 validation_1-rmse:0.15057 +[44] validation_0-rmse:0.17926 validation_1-rmse:0.14942 +[45] validation_0-rmse:0.17841 validation_1-rmse:0.14813 +[46] validation_0-rmse:0.17755 validation_1-rmse:0.14700 +[47] validation_0-rmse:0.17672 validation_1-rmse:0.14572 +[48] validation_0-rmse:0.17586 validation_1-rmse:0.14466 +[49] validation_0-rmse:0.17511 validation_1-rmse:0.14354 +[50] validation_0-rmse:0.17440 validation_1-rmse:0.14236 +[51] validation_0-rmse:0.17354 validation_1-rmse:0.14130 +[52] validation_0-rmse:0.17281 validation_1-rmse:0.14035 +[53] validation_0-rmse:0.17210 validation_1-rmse:0.13942 +[54] validation_0-rmse:0.17136 validation_1-rmse:0.13843 +[55] validation_0-rmse:0.17045 validation_1-rmse:0.13715 +[56] validation_0-rmse:0.16971 validation_1-rmse:0.13629 +[57] validation_0-rmse:0.16900 validation_1-rmse:0.13511 +[58] validation_0-rmse:0.16834 validation_1-rmse:0.13426 +[59] validation_0-rmse:0.16763 validation_1-rmse:0.13323 +[60] validation_0-rmse:0.16702 validation_1-rmse:0.13242 +[61] validation_0-rmse:0.16639 validation_1-rmse:0.13164 +[62] validation_0-rmse:0.16586 validation_1-rmse:0.13079 +[63] validation_0-rmse:0.16527 validation_1-rmse:0.13006 +[64] validation_0-rmse:0.16458 validation_1-rmse:0.12914 +[65] validation_0-rmse:0.16396 validation_1-rmse:0.12841 +[66] validation_0-rmse:0.16332 validation_1-rmse:0.12742 +[67] validation_0-rmse:0.16290 validation_1-rmse:0.12665 +[68] validation_0-rmse:0.16248 validation_1-rmse:0.12584 +[69] validation_0-rmse:0.16192 validation_1-rmse:0.12503 +[70] validation_0-rmse:0.16128 validation_1-rmse:0.12435 +[71] validation_0-rmse:0.16078 validation_1-rmse:0.12371 +[72] validation_0-rmse:0.16032 validation_1-rmse:0.12311 +[73] validation_0-rmse:0.15998 validation_1-rmse:0.12241 +[74] validation_0-rmse:0.15959 validation_1-rmse:0.12184 +[75] validation_0-rmse:0.15922 validation_1-rmse:0.12121 +[76] validation_0-rmse:0.15877 validation_1-rmse:0.12064 +[77] validation_0-rmse:0.15830 validation_1-rmse:0.11981 +[78] validation_0-rmse:0.15791 validation_1-rmse:0.11927 +[79] validation_0-rmse:0.15751 validation_1-rmse:0.11859 +[80] validation_0-rmse:0.15716 validation_1-rmse:0.11795 +[81] validation_0-rmse:0.15680 validation_1-rmse:0.11740 +[82] validation_0-rmse:0.15624 validation_1-rmse:0.11683 +[83] validation_0-rmse:0.15578 validation_1-rmse:0.11632 +[84] validation_0-rmse:0.15553 validation_1-rmse:0.11586 +[85] validation_0-rmse:0.15471 validation_1-rmse:0.11513 +[86] validation_0-rmse:0.15444 validation_1-rmse:0.11465 +[87] validation_0-rmse:0.15417 validation_1-rmse:0.11406 +[88] validation_0-rmse:0.15387 validation_1-rmse:0.11359 +[89] validation_0-rmse:0.15359 validation_1-rmse:0.11319 +[90] validation_0-rmse:0.15332 validation_1-rmse:0.11269 +[91] validation_0-rmse:0.15301 validation_1-rmse:0.11221 +[92] validation_0-rmse:0.15258 validation_1-rmse:0.11176 +[93] validation_0-rmse:0.15231 validation_1-rmse:0.11135 +[94] validation_0-rmse:0.15202 validation_1-rmse:0.11093 +[95] validation_0-rmse:0.15185 validation_1-rmse:0.11041 +[96] validation_0-rmse:0.15173 validation_1-rmse:0.11000 +[97] validation_0-rmse:0.15150 validation_1-rmse:0.10961 +[98] validation_0-rmse:0.15114 validation_1-rmse:0.10917 +[99] validation_0-rmse:0.15096 validation_1-rmse:0.10882 +2025-04-29 01:55:59,097 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:55:59,098 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:59,706 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:55:59,706 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather +2025-04-29 01:55:59,715 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,723 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:59,725 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:59,732 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,739 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:55:59,741 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:55:59,745 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-04-29 01:55:59,746 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:59,762 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:59,762 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.28468 validation_1-rmse:0.28491 +[1] validation_0-rmse:0.27961 validation_1-rmse:0.27914 +[2] validation_0-rmse:0.27524 validation_1-rmse:0.27370 +[3] validation_0-rmse:0.27054 validation_1-rmse:0.26858 +[4] validation_0-rmse:0.26601 validation_1-rmse:0.26362 +[5] validation_0-rmse:0.26219 validation_1-rmse:0.25896 +[6] validation_0-rmse:0.25814 validation_1-rmse:0.25450 +[7] validation_0-rmse:0.25500 validation_1-rmse:0.25026 +[8] validation_0-rmse:0.25145 validation_1-rmse:0.24602 +[9] validation_0-rmse:0.24843 validation_1-rmse:0.24213 +[10] validation_0-rmse:0.24527 validation_1-rmse:0.23824 +[11] validation_0-rmse:0.24238 validation_1-rmse:0.23440 +[12] validation_0-rmse:0.23940 validation_1-rmse:0.23096 +[13] validation_0-rmse:0.23630 validation_1-rmse:0.22764 +[14] validation_0-rmse:0.23385 validation_1-rmse:0.22440 +[15] validation_0-rmse:0.23099 validation_1-rmse:0.22128 +[16] validation_0-rmse:0.22865 validation_1-rmse:0.21801 +[17] validation_0-rmse:0.22620 validation_1-rmse:0.21515 +[18] validation_0-rmse:0.22375 validation_1-rmse:0.21210 +[19] validation_0-rmse:0.22142 validation_1-rmse:0.20925 +[20] validation_0-rmse:0.21927 validation_1-rmse:0.20663 +[21] validation_0-rmse:0.21720 validation_1-rmse:0.20416 +[22] validation_0-rmse:0.21528 validation_1-rmse:0.20170 +[23] validation_0-rmse:0.21330 validation_1-rmse:0.19913 +[24] validation_0-rmse:0.21136 validation_1-rmse:0.19693 +[25] validation_0-rmse:0.21002 validation_1-rmse:0.19438 +[26] validation_0-rmse:0.20807 validation_1-rmse:0.19222 +[27] validation_0-rmse:0.20636 validation_1-rmse:0.19016 +[28] validation_0-rmse:0.20439 validation_1-rmse:0.18763 +[29] validation_0-rmse:0.20276 validation_1-rmse:0.18559 +[30] validation_0-rmse:0.20114 validation_1-rmse:0.18380 +[31] validation_0-rmse:0.19965 validation_1-rmse:0.18163 +[32] validation_0-rmse:0.19833 validation_1-rmse:0.17955 +[33] validation_0-rmse:0.19688 validation_1-rmse:0.17782 +[34] validation_0-rmse:0.19558 validation_1-rmse:0.17614 +[35] validation_0-rmse:0.19420 validation_1-rmse:0.17451 +[36] validation_0-rmse:0.19297 validation_1-rmse:0.17293 +[37] validation_0-rmse:0.19169 validation_1-rmse:0.17111 +[38] validation_0-rmse:0.19038 validation_1-rmse:0.16943 +[39] validation_0-rmse:0.18941 validation_1-rmse:0.16798 +[40] validation_0-rmse:0.18828 validation_1-rmse:0.16657 +[41] validation_0-rmse:0.18724 validation_1-rmse:0.16485 +[42] validation_0-rmse:0.18620 validation_1-rmse:0.16347 +[43] validation_0-rmse:0.18525 validation_1-rmse:0.16204 +[44] validation_0-rmse:0.18429 validation_1-rmse:0.16073 +[45] validation_0-rmse:0.18324 validation_1-rmse:0.15951 +[46] validation_0-rmse:0.18250 validation_1-rmse:0.15797 +[47] validation_0-rmse:0.18157 validation_1-rmse:0.15682 +[48] validation_0-rmse:0.18069 validation_1-rmse:0.15566 +[49] validation_0-rmse:0.18002 validation_1-rmse:0.15440 +[50] validation_0-rmse:0.17914 validation_1-rmse:0.15322 +[51] validation_0-rmse:0.17842 validation_1-rmse:0.15220 +[52] validation_0-rmse:0.17756 validation_1-rmse:0.15107 +[53] validation_0-rmse:0.17668 validation_1-rmse:0.15007 +[54] validation_0-rmse:0.17596 validation_1-rmse:0.14866 +[55] validation_0-rmse:0.17525 validation_1-rmse:0.14775 +[56] validation_0-rmse:0.17467 validation_1-rmse:0.14653 +[57] validation_0-rmse:0.17390 validation_1-rmse:0.14564 +[58] validation_0-rmse:0.17326 validation_1-rmse:0.14478 +[59] validation_0-rmse:0.17273 validation_1-rmse:0.14356 +[60] validation_0-rmse:0.17218 validation_1-rmse:0.14269 +[61] validation_0-rmse:0.17157 validation_1-rmse:0.14186 +[62] validation_0-rmse:0.17120 validation_1-rmse:0.14083 +[63] validation_0-rmse:0.17069 validation_1-rmse:0.14002 +[64] validation_0-rmse:0.17012 validation_1-rmse:0.13912 +[65] validation_0-rmse:0.16942 validation_1-rmse:0.13834 +[66] validation_0-rmse:0.16914 validation_1-rmse:0.13720 +[67] validation_0-rmse:0.16856 validation_1-rmse:0.13648 +[68] validation_0-rmse:0.16800 validation_1-rmse:0.13569 +[69] validation_0-rmse:0.16796 validation_1-rmse:0.13472 +[70] validation_0-rmse:0.16737 validation_1-rmse:0.13405 +[71] validation_0-rmse:0.16686 validation_1-rmse:0.13342 +[72] validation_0-rmse:0.16639 validation_1-rmse:0.13270 +[73] validation_0-rmse:0.16648 validation_1-rmse:0.13149 +[74] validation_0-rmse:0.16609 validation_1-rmse:0.13086 +[75] validation_0-rmse:0.16560 validation_1-rmse:0.13025 +[76] validation_0-rmse:0.16530 validation_1-rmse:0.12925 +[77] validation_0-rmse:0.16492 validation_1-rmse:0.12824 +[78] validation_0-rmse:0.16451 validation_1-rmse:0.12770 +[79] validation_0-rmse:0.16414 validation_1-rmse:0.12710 +[80] validation_0-rmse:0.16377 validation_1-rmse:0.12654 +[81] validation_0-rmse:0.16338 validation_1-rmse:0.12595 +[82] validation_0-rmse:0.16317 validation_1-rmse:0.12490 +[83] validation_0-rmse:0.16249 validation_1-rmse:0.12361 +[84] validation_0-rmse:0.16217 validation_1-rmse:0.12307 +[85] validation_0-rmse:0.16178 validation_1-rmse:0.12255 +[86] validation_0-rmse:0.16149 validation_1-rmse:0.12206 +[87] validation_0-rmse:0.16113 validation_1-rmse:0.12155 +[88] validation_0-rmse:0.16049 validation_1-rmse:0.12061 +[89] validation_0-rmse:0.16008 validation_1-rmse:0.11990 +[90] validation_0-rmse:0.15955 validation_1-rmse:0.11882 +[91] validation_0-rmse:0.15927 validation_1-rmse:0.11842 +[92] validation_0-rmse:0.15891 validation_1-rmse:0.11796 +[93] validation_0-rmse:0.15880 validation_1-rmse:0.11730 +[94] validation_0-rmse:0.15829 validation_1-rmse:0.11631 +[95] validation_0-rmse:0.15809 validation_1-rmse:0.11584 +[96] validation_0-rmse:0.15778 validation_1-rmse:0.11544 +[97] validation_0-rmse:0.15763 validation_1-rmse:0.11504 +[98] validation_0-rmse:0.15724 validation_1-rmse:0.11438 +[99] validation_0-rmse:0.15694 validation_1-rmse:0.11396 +2025-04-29 01:56:05,520 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:56:05,521 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:06,027 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:56:06,027 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather +2025-04-29 01:56:06,037 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,045 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:56:06,046 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:56:06,057 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,064 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:56:06,065 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.093162 +1 0.003285 50.093162 +2 0.001898 50.093162 +3 0.000484 50.093162 +4 0.001688 50.093162 +2025-04-29 01:56:06,070 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-04-29 01:56:06,071 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:56:06,087 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:06,088 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:56:10,904 - datasieve.pipeline - INFO - DI tossed 2001 predictions for being too far from training data. +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.32950 validation_1-rmse:0.29220 +[1] validation_0-rmse:0.32402 validation_1-rmse:0.28580 +[2] validation_0-rmse:0.31922 validation_1-rmse:0.27974 +[3] validation_0-rmse:0.31450 validation_1-rmse:0.27409 +[4] validation_0-rmse:0.30969 validation_1-rmse:0.26866 +[5] validation_0-rmse:0.30585 validation_1-rmse:0.26346 +[6] validation_0-rmse:0.30202 validation_1-rmse:0.25855 +[7] validation_0-rmse:0.29888 validation_1-rmse:0.25375 +[8] validation_0-rmse:0.29520 validation_1-rmse:0.24919 +[9] validation_0-rmse:0.29164 validation_1-rmse:0.24487 +[10] validation_0-rmse:0.28843 validation_1-rmse:0.24072 +[11] validation_0-rmse:0.28514 validation_1-rmse:0.23667 +[12] validation_0-rmse:0.28114 validation_1-rmse:0.23279 +[13] validation_0-rmse:0.27740 validation_1-rmse:0.22909 +[14] validation_0-rmse:0.27421 validation_1-rmse:0.22543 +[15] validation_0-rmse:0.27115 validation_1-rmse:0.22210 +[16] validation_0-rmse:0.26820 validation_1-rmse:0.21859 +[17] validation_0-rmse:0.26549 validation_1-rmse:0.21528 +[18] validation_0-rmse:0.26254 validation_1-rmse:0.21226 +[19] validation_0-rmse:0.25967 validation_1-rmse:0.20927 +[20] validation_0-rmse:0.25735 validation_1-rmse:0.20641 +[21] validation_0-rmse:0.25470 validation_1-rmse:0.20366 +[22] validation_0-rmse:0.25265 validation_1-rmse:0.20073 +[23] validation_0-rmse:0.25054 validation_1-rmse:0.19819 +[24] validation_0-rmse:0.24806 validation_1-rmse:0.19573 +[25] validation_0-rmse:0.24570 validation_1-rmse:0.19304 +[26] validation_0-rmse:0.24361 validation_1-rmse:0.19076 +[27] validation_0-rmse:0.24148 validation_1-rmse:0.18853 +[28] validation_0-rmse:0.24014 validation_1-rmse:0.18621 +[29] validation_0-rmse:0.23792 validation_1-rmse:0.18410 +[30] validation_0-rmse:0.23603 validation_1-rmse:0.18203 +[31] validation_0-rmse:0.23421 validation_1-rmse:0.17990 +[32] validation_0-rmse:0.23264 validation_1-rmse:0.17800 +[33] validation_0-rmse:0.23087 validation_1-rmse:0.17616 +[34] validation_0-rmse:0.22949 validation_1-rmse:0.17427 +[35] validation_0-rmse:0.22857 validation_1-rmse:0.17234 +[36] validation_0-rmse:0.22690 validation_1-rmse:0.17065 +[37] validation_0-rmse:0.22566 validation_1-rmse:0.16898 +[38] validation_0-rmse:0.22462 validation_1-rmse:0.16738 +[39] validation_0-rmse:0.22376 validation_1-rmse:0.16567 +[40] validation_0-rmse:0.22232 validation_1-rmse:0.16410 +[41] validation_0-rmse:0.22105 validation_1-rmse:0.16265 +[42] validation_0-rmse:0.22006 validation_1-rmse:0.16111 +[43] validation_0-rmse:0.21847 validation_1-rmse:0.15976 +[44] validation_0-rmse:0.21782 validation_1-rmse:0.15824 +[45] validation_0-rmse:0.21641 validation_1-rmse:0.15686 +[46] validation_0-rmse:0.21552 validation_1-rmse:0.15554 +[47] validation_0-rmse:0.21459 validation_1-rmse:0.15417 +[48] validation_0-rmse:0.21339 validation_1-rmse:0.15293 +[49] validation_0-rmse:0.21255 validation_1-rmse:0.15176 +[50] validation_0-rmse:0.21192 validation_1-rmse:0.15047 +[51] validation_0-rmse:0.21115 validation_1-rmse:0.14910 +[52] validation_0-rmse:0.21072 validation_1-rmse:0.14774 +[53] validation_0-rmse:0.20992 validation_1-rmse:0.14670 +[54] validation_0-rmse:0.20839 validation_1-rmse:0.14541 +[55] validation_0-rmse:0.20753 validation_1-rmse:0.14442 +[56] validation_0-rmse:0.20648 validation_1-rmse:0.14328 +[57] validation_0-rmse:0.20564 validation_1-rmse:0.14229 +[58] validation_0-rmse:0.20473 validation_1-rmse:0.14137 +[59] validation_0-rmse:0.20418 validation_1-rmse:0.14011 +[60] validation_0-rmse:0.20341 validation_1-rmse:0.13923 +[61] validation_0-rmse:0.20258 validation_1-rmse:0.13839 +[62] validation_0-rmse:0.20230 validation_1-rmse:0.13723 +[63] validation_0-rmse:0.20075 validation_1-rmse:0.13546 +[64] validation_0-rmse:0.20007 validation_1-rmse:0.13467 +[65] validation_0-rmse:0.19937 validation_1-rmse:0.13387 +[66] validation_0-rmse:0.19875 validation_1-rmse:0.13296 +[67] validation_0-rmse:0.19709 validation_1-rmse:0.13137 +[68] validation_0-rmse:0.19675 validation_1-rmse:0.13042 +[69] validation_0-rmse:0.19617 validation_1-rmse:0.12968 +[70] validation_0-rmse:0.19560 validation_1-rmse:0.12900 +[71] validation_0-rmse:0.19492 validation_1-rmse:0.12834 +[72] validation_0-rmse:0.19319 validation_1-rmse:0.12681 +[73] validation_0-rmse:0.19272 validation_1-rmse:0.12612 +[74] validation_0-rmse:0.19230 validation_1-rmse:0.12535 +[75] validation_0-rmse:0.19170 validation_1-rmse:0.12474 +[76] validation_0-rmse:0.19058 validation_1-rmse:0.12338 +[77] validation_0-rmse:0.19010 validation_1-rmse:0.12279 +[78] validation_0-rmse:0.18961 validation_1-rmse:0.12223 +[79] validation_0-rmse:0.18960 validation_1-rmse:0.12156 +[80] validation_0-rmse:0.18882 validation_1-rmse:0.12038 +[81] validation_0-rmse:0.18819 validation_1-rmse:0.11975 +[82] validation_0-rmse:0.18789 validation_1-rmse:0.11916 +[83] validation_0-rmse:0.18738 validation_1-rmse:0.11864 +[84] validation_0-rmse:0.18718 validation_1-rmse:0.11801 +[85] validation_0-rmse:0.18600 validation_1-rmse:0.11698 +[86] validation_0-rmse:0.18572 validation_1-rmse:0.11653 +[87] validation_0-rmse:0.18534 validation_1-rmse:0.11603 +[88] validation_0-rmse:0.18478 validation_1-rmse:0.11508 +[89] validation_0-rmse:0.18430 validation_1-rmse:0.11459 +[90] validation_0-rmse:0.18447 validation_1-rmse:0.11396 +[91] validation_0-rmse:0.18424 validation_1-rmse:0.11352 +[92] validation_0-rmse:0.18367 validation_1-rmse:0.11307 +[93] validation_0-rmse:0.18333 validation_1-rmse:0.11265 +[94] validation_0-rmse:0.18313 validation_1-rmse:0.11216 +[95] validation_0-rmse:0.18275 validation_1-rmse:0.11157 +[96] validation_0-rmse:0.18275 validation_1-rmse:0.11106 +[97] validation_0-rmse:0.18248 validation_1-rmse:0.11068 +[98] validation_0-rmse:0.18220 validation_1-rmse:0.11033 +[99] validation_0-rmse:0.18198 validation_1-rmse:0.10994 +2025-04-29 01:56:11,705 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.63 secs) -------------------- +2025-04-29 01:56:11,706 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:12,255 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:56:12,275 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +1 33535 +0 33236 +2025-04-29 01:56:12,276 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 35773 +1.0 30998 +2025-04-29 01:56:12,279 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-04-29 01:56:12,281 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:56:12,282 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:56:12,283 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1735689600_prediction.feather +2025-04-29 01:56:12,334 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:56:12,335 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,518 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:56:12,519 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:13,040 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,046 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:56:13,047 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.58814 +1 0.003044 49.58814 +2 0.000465 49.58814 +3 -0.000380 49.58814 +4 0.002829 49.58814 +2025-04-29 01:56:13,052 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,057 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:13,059 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:13,066 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1735689600/cb_sol_1735689600 +2025-04-29 01:56:13,066 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:13,095 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:13,096 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30164 validation_1-rmse:0.29585 +[1] validation_0-rmse:0.29609 validation_1-rmse:0.28921 +[2] validation_0-rmse:0.29103 validation_1-rmse:0.28298 +[3] validation_0-rmse:0.28604 validation_1-rmse:0.27706 +[4] validation_0-rmse:0.28108 validation_1-rmse:0.27129 +[5] validation_0-rmse:0.27670 validation_1-rmse:0.26609 +[6] validation_0-rmse:0.27234 validation_1-rmse:0.26092 +[7] validation_0-rmse:0.26874 validation_1-rmse:0.25593 +[8] validation_0-rmse:0.26461 validation_1-rmse:0.25118 +[9] validation_0-rmse:0.26074 validation_1-rmse:0.24677 +[10] validation_0-rmse:0.25745 validation_1-rmse:0.24239 +[11] validation_0-rmse:0.25460 validation_1-rmse:0.23832 +[12] validation_0-rmse:0.25121 validation_1-rmse:0.23441 +[13] validation_0-rmse:0.24825 validation_1-rmse:0.23068 +[14] validation_0-rmse:0.24580 validation_1-rmse:0.22694 +[15] validation_0-rmse:0.24286 validation_1-rmse:0.22346 +[16] validation_0-rmse:0.24051 validation_1-rmse:0.22006 +[17] validation_0-rmse:0.23821 validation_1-rmse:0.21690 +[18] validation_0-rmse:0.23549 validation_1-rmse:0.21383 +[19] validation_0-rmse:0.23335 validation_1-rmse:0.21087 +[20] validation_0-rmse:0.23089 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22918 validation_1-rmse:0.20505 +[22] validation_0-rmse:0.22716 validation_1-rmse:0.20240 +[23] validation_0-rmse:0.22562 validation_1-rmse:0.19981 +[24] validation_0-rmse:0.22385 validation_1-rmse:0.19723 +[25] validation_0-rmse:0.22201 validation_1-rmse:0.19473 +[26] validation_0-rmse:0.22016 validation_1-rmse:0.19245 +[27] validation_0-rmse:0.21834 validation_1-rmse:0.19024 +[28] validation_0-rmse:0.21671 validation_1-rmse:0.18789 +[29] validation_0-rmse:0.21493 validation_1-rmse:0.18579 +[30] validation_0-rmse:0.21385 validation_1-rmse:0.18351 +[31] validation_0-rmse:0.21216 validation_1-rmse:0.18156 +[32] validation_0-rmse:0.21088 validation_1-rmse:0.17941 +[33] validation_0-rmse:0.20953 validation_1-rmse:0.17754 +[34] validation_0-rmse:0.20805 validation_1-rmse:0.17575 +[35] validation_0-rmse:0.20648 validation_1-rmse:0.17399 +[36] validation_0-rmse:0.20515 validation_1-rmse:0.17220 +[37] validation_0-rmse:0.20382 validation_1-rmse:0.17031 +[38] validation_0-rmse:0.20257 validation_1-rmse:0.16871 +[39] validation_0-rmse:0.20125 validation_1-rmse:0.16718 +[40] validation_0-rmse:0.20005 validation_1-rmse:0.16574 +[41] validation_0-rmse:0.19885 validation_1-rmse:0.16415 +[42] validation_0-rmse:0.19789 validation_1-rmse:0.16270 +[43] validation_0-rmse:0.19680 validation_1-rmse:0.16130 +[44] validation_0-rmse:0.19564 validation_1-rmse:0.15993 +[45] validation_0-rmse:0.19480 validation_1-rmse:0.15854 +[46] validation_0-rmse:0.19376 validation_1-rmse:0.15728 +[47] validation_0-rmse:0.19290 validation_1-rmse:0.15568 +[48] validation_0-rmse:0.19223 validation_1-rmse:0.15445 +[49] validation_0-rmse:0.19129 validation_1-rmse:0.15330 +[50] validation_0-rmse:0.19035 validation_1-rmse:0.15194 +[51] validation_0-rmse:0.18948 validation_1-rmse:0.15082 +[52] validation_0-rmse:0.18882 validation_1-rmse:0.14945 +[53] validation_0-rmse:0.18801 validation_1-rmse:0.14840 +[54] validation_0-rmse:0.18707 validation_1-rmse:0.14736 +[55] validation_0-rmse:0.18637 validation_1-rmse:0.14635 +[56] validation_0-rmse:0.18571 validation_1-rmse:0.14542 +[57] validation_0-rmse:0.18497 validation_1-rmse:0.14413 +[58] validation_0-rmse:0.18443 validation_1-rmse:0.14297 +[59] validation_0-rmse:0.18375 validation_1-rmse:0.14203 +[60] validation_0-rmse:0.18319 validation_1-rmse:0.14111 +[61] validation_0-rmse:0.18266 validation_1-rmse:0.14030 +[62] validation_0-rmse:0.18185 validation_1-rmse:0.13914 +[63] validation_0-rmse:0.18145 validation_1-rmse:0.13831 +[64] validation_0-rmse:0.18135 validation_1-rmse:0.13720 +[65] validation_0-rmse:0.18075 validation_1-rmse:0.13643 +[66] validation_0-rmse:0.18020 validation_1-rmse:0.13560 +[67] validation_0-rmse:0.17951 validation_1-rmse:0.13485 +[68] validation_0-rmse:0.17888 validation_1-rmse:0.13414 +[69] validation_0-rmse:0.17850 validation_1-rmse:0.13343 +[70] validation_0-rmse:0.17798 validation_1-rmse:0.13224 +[71] validation_0-rmse:0.17751 validation_1-rmse:0.13133 +[72] validation_0-rmse:0.17711 validation_1-rmse:0.13062 +[73] validation_0-rmse:0.17701 validation_1-rmse:0.12966 +[74] validation_0-rmse:0.17648 validation_1-rmse:0.12872 +[75] validation_0-rmse:0.17611 validation_1-rmse:0.12806 +[76] validation_0-rmse:0.17573 validation_1-rmse:0.12732 +[77] validation_0-rmse:0.17528 validation_1-rmse:0.12664 +[78] validation_0-rmse:0.17478 validation_1-rmse:0.12605 +[79] validation_0-rmse:0.17432 validation_1-rmse:0.12518 +[80] validation_0-rmse:0.17391 validation_1-rmse:0.12466 +[81] validation_0-rmse:0.17358 validation_1-rmse:0.12398 +[82] validation_0-rmse:0.17315 validation_1-rmse:0.12342 +[83] validation_0-rmse:0.17260 validation_1-rmse:0.12276 +[84] validation_0-rmse:0.17220 validation_1-rmse:0.12222 +[85] validation_0-rmse:0.17182 validation_1-rmse:0.12176 +[86] validation_0-rmse:0.17152 validation_1-rmse:0.12124 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12046 +[88] validation_0-rmse:0.17085 validation_1-rmse:0.11974 +[89] validation_0-rmse:0.17053 validation_1-rmse:0.11930 +[90] validation_0-rmse:0.17018 validation_1-rmse:0.11888 +[91] validation_0-rmse:0.17011 validation_1-rmse:0.11810 +[92] validation_0-rmse:0.16980 validation_1-rmse:0.11762 +[93] validation_0-rmse:0.16956 validation_1-rmse:0.11689 +[94] validation_0-rmse:0.16923 validation_1-rmse:0.11641 +[95] validation_0-rmse:0.16912 validation_1-rmse:0.11579 +[96] validation_0-rmse:0.16878 validation_1-rmse:0.11530 +[97] validation_0-rmse:0.16857 validation_1-rmse:0.11489 +[98] validation_0-rmse:0.16824 validation_1-rmse:0.11442 +[99] validation_0-rmse:0.16824 validation_1-rmse:0.11403 +2025-04-29 01:56:19,586 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.52 secs) -------------------- +2025-04-29 01:56:19,587 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:20,174 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:56:20,175 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1736553600_prediction.feather +2025-04-29 01:56:20,179 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,185 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:20,186 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:20,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,197 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:20,199 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:20,204 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1736553600/cb_sol_1736553600 +2025-04-29 01:56:20,205 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:20,227 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:20,228 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:56:25,109 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29597 validation_1-rmse:0.29016 +[1] validation_0-rmse:0.29075 validation_1-rmse:0.28391 +[2] validation_0-rmse:0.28602 validation_1-rmse:0.27798 +[3] validation_0-rmse:0.28062 validation_1-rmse:0.27213 +[4] validation_0-rmse:0.27647 validation_1-rmse:0.26682 +[5] validation_0-rmse:0.27188 validation_1-rmse:0.26144 +[6] validation_0-rmse:0.26781 validation_1-rmse:0.25655 +[7] validation_0-rmse:0.26412 validation_1-rmse:0.25180 +[8] validation_0-rmse:0.25994 validation_1-rmse:0.24709 +[9] validation_0-rmse:0.25649 validation_1-rmse:0.24277 +[10] validation_0-rmse:0.25332 validation_1-rmse:0.23850 +[11] validation_0-rmse:0.24999 validation_1-rmse:0.23452 +[12] validation_0-rmse:0.24687 validation_1-rmse:0.23072 +[13] validation_0-rmse:0.24432 validation_1-rmse:0.22694 +[14] validation_0-rmse:0.24128 validation_1-rmse:0.22341 +[15] validation_0-rmse:0.23869 validation_1-rmse:0.21969 +[16] validation_0-rmse:0.23628 validation_1-rmse:0.21635 +[17] validation_0-rmse:0.23354 validation_1-rmse:0.21326 +[18] validation_0-rmse:0.23123 validation_1-rmse:0.21007 +[19] validation_0-rmse:0.22919 validation_1-rmse:0.20707 +[20] validation_0-rmse:0.22705 validation_1-rmse:0.20418 +[21] validation_0-rmse:0.22505 validation_1-rmse:0.20149 +[22] validation_0-rmse:0.22285 validation_1-rmse:0.19887 +[23] validation_0-rmse:0.22084 validation_1-rmse:0.19631 +[24] validation_0-rmse:0.21877 validation_1-rmse:0.19389 +[25] validation_0-rmse:0.21748 validation_1-rmse:0.19133 +[26] validation_0-rmse:0.21557 validation_1-rmse:0.18870 +[27] validation_0-rmse:0.21374 validation_1-rmse:0.18648 +[28] validation_0-rmse:0.21183 validation_1-rmse:0.18432 +[29] validation_0-rmse:0.21047 validation_1-rmse:0.18209 +[30] validation_0-rmse:0.20873 validation_1-rmse:0.17990 +[31] validation_0-rmse:0.20717 validation_1-rmse:0.17795 +[32] validation_0-rmse:0.20564 validation_1-rmse:0.17599 +[33] validation_0-rmse:0.20428 validation_1-rmse:0.17421 +[34] validation_0-rmse:0.20290 validation_1-rmse:0.17229 +[35] validation_0-rmse:0.20161 validation_1-rmse:0.17047 +[36] validation_0-rmse:0.20018 validation_1-rmse:0.16878 +[37] validation_0-rmse:0.19923 validation_1-rmse:0.16688 +[38] validation_0-rmse:0.19796 validation_1-rmse:0.16534 +[39] validation_0-rmse:0.19668 validation_1-rmse:0.16355 +[40] validation_0-rmse:0.19543 validation_1-rmse:0.16204 +[41] validation_0-rmse:0.19441 validation_1-rmse:0.16062 +[42] validation_0-rmse:0.19344 validation_1-rmse:0.15910 +[43] validation_0-rmse:0.19256 validation_1-rmse:0.15759 +[44] validation_0-rmse:0.19154 validation_1-rmse:0.15625 +[45] validation_0-rmse:0.19048 validation_1-rmse:0.15494 +[46] validation_0-rmse:0.18937 validation_1-rmse:0.15366 +[47] validation_0-rmse:0.18865 validation_1-rmse:0.15236 +[48] validation_0-rmse:0.18784 validation_1-rmse:0.15112 +[49] validation_0-rmse:0.18704 validation_1-rmse:0.14998 +[50] validation_0-rmse:0.18625 validation_1-rmse:0.14874 +[51] validation_0-rmse:0.18541 validation_1-rmse:0.14763 +[52] validation_0-rmse:0.18456 validation_1-rmse:0.14659 +[53] validation_0-rmse:0.18383 validation_1-rmse:0.14530 +[54] validation_0-rmse:0.18315 validation_1-rmse:0.14420 +[55] validation_0-rmse:0.18234 validation_1-rmse:0.14321 +[56] validation_0-rmse:0.18181 validation_1-rmse:0.14206 +[57] validation_0-rmse:0.18109 validation_1-rmse:0.14106 +[58] validation_0-rmse:0.18033 validation_1-rmse:0.13996 +[59] validation_0-rmse:0.17964 validation_1-rmse:0.13905 +[60] validation_0-rmse:0.17921 validation_1-rmse:0.13820 +[61] validation_0-rmse:0.17865 validation_1-rmse:0.13731 +[62] validation_0-rmse:0.17795 validation_1-rmse:0.13648 +[63] validation_0-rmse:0.17737 validation_1-rmse:0.13559 +[64] validation_0-rmse:0.17680 validation_1-rmse:0.13483 +[65] validation_0-rmse:0.17628 validation_1-rmse:0.13408 +[66] validation_0-rmse:0.17588 validation_1-rmse:0.13303 +[67] validation_0-rmse:0.17530 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.17478 validation_1-rmse:0.13153 +[69] validation_0-rmse:0.17439 validation_1-rmse:0.13081 +[70] validation_0-rmse:0.17401 validation_1-rmse:0.12991 +[71] validation_0-rmse:0.17347 validation_1-rmse:0.12911 +[72] validation_0-rmse:0.17304 validation_1-rmse:0.12838 +[73] validation_0-rmse:0.17254 validation_1-rmse:0.12774 +[74] validation_0-rmse:0.17207 validation_1-rmse:0.12656 +[75] validation_0-rmse:0.17185 validation_1-rmse:0.12571 +[76] validation_0-rmse:0.17126 validation_1-rmse:0.12512 +[77] validation_0-rmse:0.17096 validation_1-rmse:0.12447 +[78] validation_0-rmse:0.17064 validation_1-rmse:0.12381 +[79] validation_0-rmse:0.17024 validation_1-rmse:0.12300 +[80] validation_0-rmse:0.16989 validation_1-rmse:0.12244 +[81] validation_0-rmse:0.16955 validation_1-rmse:0.12180 +[82] validation_0-rmse:0.16924 validation_1-rmse:0.12129 +[83] validation_0-rmse:0.16931 validation_1-rmse:0.12037 +[84] validation_0-rmse:0.16888 validation_1-rmse:0.11970 +[85] validation_0-rmse:0.16845 validation_1-rmse:0.11914 +[86] validation_0-rmse:0.16809 validation_1-rmse:0.11840 +[87] validation_0-rmse:0.16766 validation_1-rmse:0.11760 +[88] validation_0-rmse:0.16741 validation_1-rmse:0.11714 +[89] validation_0-rmse:0.16707 validation_1-rmse:0.11667 +[90] validation_0-rmse:0.16683 validation_1-rmse:0.11592 +[91] validation_0-rmse:0.16643 validation_1-rmse:0.11537 +[92] validation_0-rmse:0.16621 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.16611 validation_1-rmse:0.11396 +[94] validation_0-rmse:0.16587 validation_1-rmse:0.11350 +[95] validation_0-rmse:0.16563 validation_1-rmse:0.11308 +[96] validation_0-rmse:0.16535 validation_1-rmse:0.11237 +[97] validation_0-rmse:0.16487 validation_1-rmse:0.11173 +[98] validation_0-rmse:0.16461 validation_1-rmse:0.11133 +[99] validation_0-rmse:0.16437 validation_1-rmse:0.11096 +2025-04-29 01:56:26,510 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.30 secs) -------------------- +2025-04-29 01:56:26,511 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:27,072 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:56:27,073 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1737417600_prediction.feather +2025-04-29 01:56:27,079 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,085 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:27,086 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:27,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,100 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:27,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:27,108 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1737417600/cb_sol_1737417600 +2025-04-29 01:56:27,109 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:27,130 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:27,131 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:56:32,206 - datasieve.pipeline - INFO - DI tossed 1523 predictions for being too far from training data. +2025-04-29 01:56:32,209 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:32,210 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30838 validation_1-rmse:0.28356 +[1] validation_0-rmse:0.30280 validation_1-rmse:0.27752 +[2] validation_0-rmse:0.29759 validation_1-rmse:0.27179 +[3] validation_0-rmse:0.29330 validation_1-rmse:0.26614 +[4] validation_0-rmse:0.28936 validation_1-rmse:0.26091 +[5] validation_0-rmse:0.28544 validation_1-rmse:0.25581 +[6] validation_0-rmse:0.28151 validation_1-rmse:0.25102 +[7] validation_0-rmse:0.27790 validation_1-rmse:0.24636 +[8] validation_0-rmse:0.27429 validation_1-rmse:0.24196 +[9] validation_0-rmse:0.27104 validation_1-rmse:0.23770 +[10] validation_0-rmse:0.26762 validation_1-rmse:0.23356 +[11] validation_0-rmse:0.26472 validation_1-rmse:0.22966 +[12] validation_0-rmse:0.26219 validation_1-rmse:0.22601 +[13] validation_0-rmse:0.25924 validation_1-rmse:0.22234 +[14] validation_0-rmse:0.25634 validation_1-rmse:0.21888 +[15] validation_0-rmse:0.25379 validation_1-rmse:0.21545 +[16] validation_0-rmse:0.25117 validation_1-rmse:0.21221 +[17] validation_0-rmse:0.24877 validation_1-rmse:0.20902 +[18] validation_0-rmse:0.24653 validation_1-rmse:0.20604 +[19] validation_0-rmse:0.24404 validation_1-rmse:0.20315 +[20] validation_0-rmse:0.24194 validation_1-rmse:0.20032 +[21] validation_0-rmse:0.23966 validation_1-rmse:0.19765 +[22] validation_0-rmse:0.23804 validation_1-rmse:0.19481 +[23] validation_0-rmse:0.23599 validation_1-rmse:0.19230 +[24] validation_0-rmse:0.23384 validation_1-rmse:0.18993 +[25] validation_0-rmse:0.23196 validation_1-rmse:0.18756 +[26] validation_0-rmse:0.23057 validation_1-rmse:0.18506 +[27] validation_0-rmse:0.22854 validation_1-rmse:0.18283 +[28] validation_0-rmse:0.22705 validation_1-rmse:0.18071 +[29] validation_0-rmse:0.22557 validation_1-rmse:0.17851 +[30] validation_0-rmse:0.22394 validation_1-rmse:0.17644 +[31] validation_0-rmse:0.22213 validation_1-rmse:0.17452 +[32] validation_0-rmse:0.22064 validation_1-rmse:0.17267 +[33] validation_0-rmse:0.21905 validation_1-rmse:0.17084 +[34] validation_0-rmse:0.21806 validation_1-rmse:0.16880 +[35] validation_0-rmse:0.21693 validation_1-rmse:0.16700 +[36] validation_0-rmse:0.21537 validation_1-rmse:0.16520 +[37] validation_0-rmse:0.21417 validation_1-rmse:0.16362 +[38] validation_0-rmse:0.21282 validation_1-rmse:0.16204 +[39] validation_0-rmse:0.21137 validation_1-rmse:0.16047 +[40] validation_0-rmse:0.20994 validation_1-rmse:0.15897 +[41] validation_0-rmse:0.20878 validation_1-rmse:0.15747 +[42] validation_0-rmse:0.20766 validation_1-rmse:0.15604 +[43] validation_0-rmse:0.20666 validation_1-rmse:0.15444 +[44] validation_0-rmse:0.20566 validation_1-rmse:0.15316 +[45] validation_0-rmse:0.20496 validation_1-rmse:0.15162 +[46] validation_0-rmse:0.20394 validation_1-rmse:0.15038 +[47] validation_0-rmse:0.20277 validation_1-rmse:0.14909 +[48] validation_0-rmse:0.20176 validation_1-rmse:0.14793 +[49] validation_0-rmse:0.20072 validation_1-rmse:0.14681 +[50] validation_0-rmse:0.20058 validation_1-rmse:0.14528 +[51] validation_0-rmse:0.19970 validation_1-rmse:0.14419 +[52] validation_0-rmse:0.19887 validation_1-rmse:0.14284 +[53] validation_0-rmse:0.19809 validation_1-rmse:0.14182 +[54] validation_0-rmse:0.19725 validation_1-rmse:0.14076 +[55] validation_0-rmse:0.19636 validation_1-rmse:0.13981 +[56] validation_0-rmse:0.19615 validation_1-rmse:0.13853 +[57] validation_0-rmse:0.19540 validation_1-rmse:0.13757 +[58] validation_0-rmse:0.19460 validation_1-rmse:0.13664 +[59] validation_0-rmse:0.19418 validation_1-rmse:0.13553 +[60] validation_0-rmse:0.19382 validation_1-rmse:0.13445 +[61] validation_0-rmse:0.19302 validation_1-rmse:0.13363 +[62] validation_0-rmse:0.19218 validation_1-rmse:0.13270 +[63] validation_0-rmse:0.19154 validation_1-rmse:0.13183 +[64] validation_0-rmse:0.19083 validation_1-rmse:0.13105 +[65] validation_0-rmse:0.19005 validation_1-rmse:0.13008 +[66] validation_0-rmse:0.18929 validation_1-rmse:0.12932 +[67] validation_0-rmse:0.18885 validation_1-rmse:0.12851 +[68] validation_0-rmse:0.18837 validation_1-rmse:0.12781 +[69] validation_0-rmse:0.18790 validation_1-rmse:0.12711 +[70] validation_0-rmse:0.18732 validation_1-rmse:0.12617 +[71] validation_0-rmse:0.18682 validation_1-rmse:0.12552 +[72] validation_0-rmse:0.18669 validation_1-rmse:0.12448 +[73] validation_0-rmse:0.18617 validation_1-rmse:0.12382 +[74] validation_0-rmse:0.18587 validation_1-rmse:0.12322 +[75] validation_0-rmse:0.18544 validation_1-rmse:0.12261 +[76] validation_0-rmse:0.18524 validation_1-rmse:0.12162 +[77] validation_0-rmse:0.18486 validation_1-rmse:0.12098 +[78] validation_0-rmse:0.18443 validation_1-rmse:0.12021 +[79] validation_0-rmse:0.18415 validation_1-rmse:0.11963 +[80] validation_0-rmse:0.18393 validation_1-rmse:0.11866 +[81] validation_0-rmse:0.18344 validation_1-rmse:0.11809 +[82] validation_0-rmse:0.18307 validation_1-rmse:0.11748 +[83] validation_0-rmse:0.18257 validation_1-rmse:0.11699 +[84] validation_0-rmse:0.18216 validation_1-rmse:0.11643 +[85] validation_0-rmse:0.18188 validation_1-rmse:0.11595 +[86] validation_0-rmse:0.18168 validation_1-rmse:0.11502 +[87] validation_0-rmse:0.18148 validation_1-rmse:0.11451 +[88] validation_0-rmse:0.18093 validation_1-rmse:0.11378 +[89] validation_0-rmse:0.18054 validation_1-rmse:0.11332 +[90] validation_0-rmse:0.18024 validation_1-rmse:0.11285 +[91] validation_0-rmse:0.17982 validation_1-rmse:0.11241 +[92] validation_0-rmse:0.17950 validation_1-rmse:0.11185 +[93] validation_0-rmse:0.17918 validation_1-rmse:0.11123 +[94] validation_0-rmse:0.17882 validation_1-rmse:0.11072 +[95] validation_0-rmse:0.17881 validation_1-rmse:0.10986 +[96] validation_0-rmse:0.17832 validation_1-rmse:0.10941 +[97] validation_0-rmse:0.17800 validation_1-rmse:0.10897 +[98] validation_0-rmse:0.17774 validation_1-rmse:0.10859 +[99] validation_0-rmse:0.17746 validation_1-rmse:0.10819 +2025-04-29 01:56:33,558 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:33,558 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:34,118 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:56:34,119 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1738281600_prediction.feather +2025-04-29 01:56:34,124 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,130 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:34,131 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:34,137 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,143 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:34,144 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:34,149 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1738281600/cb_sol_1738281600 +2025-04-29 01:56:34,150 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:34,173 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:34,173 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29494 validation_1-rmse:0.28739 +[1] validation_0-rmse:0.28930 validation_1-rmse:0.28164 +[2] validation_0-rmse:0.28437 validation_1-rmse:0.27613 +[3] validation_0-rmse:0.27990 validation_1-rmse:0.27106 +[4] validation_0-rmse:0.27541 validation_1-rmse:0.26617 +[5] validation_0-rmse:0.27070 validation_1-rmse:0.26147 +[6] validation_0-rmse:0.26683 validation_1-rmse:0.25687 +[7] validation_0-rmse:0.26280 validation_1-rmse:0.25263 +[8] validation_0-rmse:0.25916 validation_1-rmse:0.24830 +[9] validation_0-rmse:0.25540 validation_1-rmse:0.24420 +[10] validation_0-rmse:0.25186 validation_1-rmse:0.24022 +[11] validation_0-rmse:0.24829 validation_1-rmse:0.23647 +[12] validation_0-rmse:0.24504 validation_1-rmse:0.23286 +[13] validation_0-rmse:0.24183 validation_1-rmse:0.22943 +[14] validation_0-rmse:0.23870 validation_1-rmse:0.22619 +[15] validation_0-rmse:0.23587 validation_1-rmse:0.22274 +[16] validation_0-rmse:0.23325 validation_1-rmse:0.21951 +[17] validation_0-rmse:0.23045 validation_1-rmse:0.21650 +[18] validation_0-rmse:0.22792 validation_1-rmse:0.21367 +[19] validation_0-rmse:0.22524 validation_1-rmse:0.21092 +[20] validation_0-rmse:0.22293 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22055 validation_1-rmse:0.20549 +[22] validation_0-rmse:0.21831 validation_1-rmse:0.20307 +[23] validation_0-rmse:0.21601 validation_1-rmse:0.20062 +[24] validation_0-rmse:0.21372 validation_1-rmse:0.19810 +[25] validation_0-rmse:0.21154 validation_1-rmse:0.19580 +[26] validation_0-rmse:0.20966 validation_1-rmse:0.19369 +[27] validation_0-rmse:0.20790 validation_1-rmse:0.19130 +[28] validation_0-rmse:0.20602 validation_1-rmse:0.18921 +[29] validation_0-rmse:0.20418 validation_1-rmse:0.18723 +[30] validation_0-rmse:0.20236 validation_1-rmse:0.18525 +[31] validation_0-rmse:0.20057 validation_1-rmse:0.18324 +[32] validation_0-rmse:0.19900 validation_1-rmse:0.18144 +[33] validation_0-rmse:0.19744 validation_1-rmse:0.17941 +[34] validation_0-rmse:0.19608 validation_1-rmse:0.17767 +[35] validation_0-rmse:0.19467 validation_1-rmse:0.17605 +[36] validation_0-rmse:0.19313 validation_1-rmse:0.17422 +[37] validation_0-rmse:0.19156 validation_1-rmse:0.17260 +[38] validation_0-rmse:0.19020 validation_1-rmse:0.17103 +[39] validation_0-rmse:0.18884 validation_1-rmse:0.16948 +[40] validation_0-rmse:0.18767 validation_1-rmse:0.16797 +[41] validation_0-rmse:0.18636 validation_1-rmse:0.16647 +[42] validation_0-rmse:0.18512 validation_1-rmse:0.16505 +[43] validation_0-rmse:0.18403 validation_1-rmse:0.16340 +[44] validation_0-rmse:0.18290 validation_1-rmse:0.16210 +[45] validation_0-rmse:0.18189 validation_1-rmse:0.16085 +[46] validation_0-rmse:0.18090 validation_1-rmse:0.15966 +[47] validation_0-rmse:0.17992 validation_1-rmse:0.15841 +[48] validation_0-rmse:0.17901 validation_1-rmse:0.15728 +[49] validation_0-rmse:0.17817 validation_1-rmse:0.15582 +[50] validation_0-rmse:0.17697 validation_1-rmse:0.15458 +[51] validation_0-rmse:0.17607 validation_1-rmse:0.15349 +[52] validation_0-rmse:0.17516 validation_1-rmse:0.15235 +[53] validation_0-rmse:0.17425 validation_1-rmse:0.15131 +[54] validation_0-rmse:0.17347 validation_1-rmse:0.15032 +[55] validation_0-rmse:0.17275 validation_1-rmse:0.14932 +[56] validation_0-rmse:0.17211 validation_1-rmse:0.14834 +[57] validation_0-rmse:0.17131 validation_1-rmse:0.14741 +[58] validation_0-rmse:0.17072 validation_1-rmse:0.14617 +[59] validation_0-rmse:0.16999 validation_1-rmse:0.14528 +[60] validation_0-rmse:0.16934 validation_1-rmse:0.14416 +[61] validation_0-rmse:0.16887 validation_1-rmse:0.14321 +[62] validation_0-rmse:0.16842 validation_1-rmse:0.14213 +[63] validation_0-rmse:0.16765 validation_1-rmse:0.14130 +[64] validation_0-rmse:0.16691 validation_1-rmse:0.14048 +[65] validation_0-rmse:0.16629 validation_1-rmse:0.13956 +[66] validation_0-rmse:0.16565 validation_1-rmse:0.13882 +[67] validation_0-rmse:0.16530 validation_1-rmse:0.13793 +[68] validation_0-rmse:0.16467 validation_1-rmse:0.13710 +[69] validation_0-rmse:0.16436 validation_1-rmse:0.13621 +[70] validation_0-rmse:0.16377 validation_1-rmse:0.13542 +[71] validation_0-rmse:0.16334 validation_1-rmse:0.13463 +[72] validation_0-rmse:0.16280 validation_1-rmse:0.13394 +[73] validation_0-rmse:0.16230 validation_1-rmse:0.13328 +[74] validation_0-rmse:0.16156 validation_1-rmse:0.13246 +[75] validation_0-rmse:0.16122 validation_1-rmse:0.13151 +[76] validation_0-rmse:0.16080 validation_1-rmse:0.13080 +[77] validation_0-rmse:0.16033 validation_1-rmse:0.13015 +[78] validation_0-rmse:0.15992 validation_1-rmse:0.12951 +[79] validation_0-rmse:0.15950 validation_1-rmse:0.12888 +[80] validation_0-rmse:0.15909 validation_1-rmse:0.12822 +[81] validation_0-rmse:0.15875 validation_1-rmse:0.12744 +[82] validation_0-rmse:0.15831 validation_1-rmse:0.12683 +[83] validation_0-rmse:0.15786 validation_1-rmse:0.12626 +[84] validation_0-rmse:0.15747 validation_1-rmse:0.12572 +[85] validation_0-rmse:0.15724 validation_1-rmse:0.12495 +[86] validation_0-rmse:0.15695 validation_1-rmse:0.12442 +[87] validation_0-rmse:0.15664 validation_1-rmse:0.12382 +[88] validation_0-rmse:0.15651 validation_1-rmse:0.12326 +[89] validation_0-rmse:0.15629 validation_1-rmse:0.12256 +[90] validation_0-rmse:0.15596 validation_1-rmse:0.12196 +[91] validation_0-rmse:0.15559 validation_1-rmse:0.12141 +[92] validation_0-rmse:0.15511 validation_1-rmse:0.12088 +[93] validation_0-rmse:0.15487 validation_1-rmse:0.12033 +[94] validation_0-rmse:0.15472 validation_1-rmse:0.11975 +[95] validation_0-rmse:0.15438 validation_1-rmse:0.11924 +[96] validation_0-rmse:0.15408 validation_1-rmse:0.11882 +[97] validation_0-rmse:0.15382 validation_1-rmse:0.11819 +[98] validation_0-rmse:0.15350 validation_1-rmse:0.11777 +[99] validation_0-rmse:0.15331 validation_1-rmse:0.11727 +2025-04-29 01:56:40,600 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:40,601 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:41,171 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:56:41,172 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1739145600_prediction.feather +2025-04-29 01:56:41,177 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,183 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:41,185 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:41,193 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,200 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:41,201 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:41,206 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1739145600/cb_sol_1739145600 +2025-04-29 01:56:41,207 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:41,228 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:41,229 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:56:46,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:46,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29889 validation_1-rmse:0.30153 +[1] validation_0-rmse:0.29317 validation_1-rmse:0.29483 +[2] validation_0-rmse:0.28819 validation_1-rmse:0.28860 +[3] validation_0-rmse:0.28336 validation_1-rmse:0.28273 +[4] validation_0-rmse:0.27885 validation_1-rmse:0.27694 +[5] validation_0-rmse:0.27448 validation_1-rmse:0.27155 +[6] validation_0-rmse:0.27020 validation_1-rmse:0.26634 +[7] validation_0-rmse:0.26629 validation_1-rmse:0.26134 +[8] validation_0-rmse:0.26241 validation_1-rmse:0.25653 +[9] validation_0-rmse:0.25876 validation_1-rmse:0.25192 +[10] validation_0-rmse:0.25559 validation_1-rmse:0.24747 +[11] validation_0-rmse:0.25223 validation_1-rmse:0.24337 +[12] validation_0-rmse:0.24904 validation_1-rmse:0.23934 +[13] validation_0-rmse:0.24639 validation_1-rmse:0.23548 +[14] validation_0-rmse:0.24353 validation_1-rmse:0.23187 +[15] validation_0-rmse:0.24076 validation_1-rmse:0.22837 +[16] validation_0-rmse:0.23849 validation_1-rmse:0.22484 +[17] validation_0-rmse:0.23581 validation_1-rmse:0.22147 +[18] validation_0-rmse:0.23342 validation_1-rmse:0.21814 +[19] validation_0-rmse:0.23133 validation_1-rmse:0.21509 +[20] validation_0-rmse:0.22937 validation_1-rmse:0.21187 +[21] validation_0-rmse:0.22713 validation_1-rmse:0.20902 +[22] validation_0-rmse:0.22509 validation_1-rmse:0.20631 +[23] validation_0-rmse:0.22312 validation_1-rmse:0.20373 +[24] validation_0-rmse:0.22123 validation_1-rmse:0.20076 +[25] validation_0-rmse:0.21951 validation_1-rmse:0.19837 +[26] validation_0-rmse:0.21751 validation_1-rmse:0.19562 +[27] validation_0-rmse:0.21589 validation_1-rmse:0.19309 +[28] validation_0-rmse:0.21422 validation_1-rmse:0.19091 +[29] validation_0-rmse:0.21272 validation_1-rmse:0.18879 +[30] validation_0-rmse:0.21119 validation_1-rmse:0.18660 +[31] validation_0-rmse:0.20982 validation_1-rmse:0.18468 +[32] validation_0-rmse:0.20829 validation_1-rmse:0.18239 +[33] validation_0-rmse:0.20681 validation_1-rmse:0.18048 +[34] validation_0-rmse:0.20548 validation_1-rmse:0.17869 +[35] validation_0-rmse:0.20431 validation_1-rmse:0.17665 +[36] validation_0-rmse:0.20297 validation_1-rmse:0.17483 +[37] validation_0-rmse:0.20174 validation_1-rmse:0.17311 +[38] validation_0-rmse:0.20060 validation_1-rmse:0.17153 +[39] validation_0-rmse:0.19951 validation_1-rmse:0.16958 +[40] validation_0-rmse:0.19848 validation_1-rmse:0.16805 +[41] validation_0-rmse:0.19745 validation_1-rmse:0.16652 +[42] validation_0-rmse:0.19647 validation_1-rmse:0.16509 +[43] validation_0-rmse:0.19570 validation_1-rmse:0.16325 +[44] validation_0-rmse:0.19473 validation_1-rmse:0.16187 +[45] validation_0-rmse:0.19397 validation_1-rmse:0.16012 +[46] validation_0-rmse:0.19314 validation_1-rmse:0.15887 +[47] validation_0-rmse:0.19196 validation_1-rmse:0.15723 +[48] validation_0-rmse:0.19096 validation_1-rmse:0.15595 +[49] validation_0-rmse:0.19009 validation_1-rmse:0.15468 +[50] validation_0-rmse:0.18931 validation_1-rmse:0.15355 +[51] validation_0-rmse:0.18864 validation_1-rmse:0.15207 +[52] validation_0-rmse:0.18786 validation_1-rmse:0.15101 +[53] validation_0-rmse:0.18690 validation_1-rmse:0.14960 +[54] validation_0-rmse:0.18614 validation_1-rmse:0.14859 +[55] validation_0-rmse:0.18550 validation_1-rmse:0.14756 +[56] validation_0-rmse:0.18475 validation_1-rmse:0.14647 +[57] validation_0-rmse:0.18405 validation_1-rmse:0.14545 +[58] validation_0-rmse:0.18346 validation_1-rmse:0.14415 +[59] validation_0-rmse:0.18277 validation_1-rmse:0.14321 +[60] validation_0-rmse:0.18219 validation_1-rmse:0.14221 +[61] validation_0-rmse:0.18158 validation_1-rmse:0.14129 +[62] validation_0-rmse:0.18100 validation_1-rmse:0.14043 +[63] validation_0-rmse:0.18059 validation_1-rmse:0.13920 +[64] validation_0-rmse:0.17997 validation_1-rmse:0.13842 +[65] validation_0-rmse:0.17941 validation_1-rmse:0.13754 +[66] validation_0-rmse:0.17881 validation_1-rmse:0.13652 +[67] validation_0-rmse:0.17823 validation_1-rmse:0.13576 +[68] validation_0-rmse:0.17784 validation_1-rmse:0.13468 +[69] validation_0-rmse:0.17735 validation_1-rmse:0.13396 +[70] validation_0-rmse:0.17687 validation_1-rmse:0.13311 +[71] validation_0-rmse:0.17628 validation_1-rmse:0.13225 +[72] validation_0-rmse:0.17599 validation_1-rmse:0.13154 +[73] validation_0-rmse:0.17542 validation_1-rmse:0.13080 +[74] validation_0-rmse:0.17497 validation_1-rmse:0.13013 +[75] validation_0-rmse:0.17456 validation_1-rmse:0.12954 +[76] validation_0-rmse:0.17416 validation_1-rmse:0.12864 +[77] validation_0-rmse:0.17369 validation_1-rmse:0.12802 +[78] validation_0-rmse:0.17345 validation_1-rmse:0.12735 +[79] validation_0-rmse:0.17302 validation_1-rmse:0.12672 +[80] validation_0-rmse:0.17254 validation_1-rmse:0.12609 +[81] validation_0-rmse:0.17248 validation_1-rmse:0.12527 +[82] validation_0-rmse:0.17210 validation_1-rmse:0.12470 +[83] validation_0-rmse:0.17196 validation_1-rmse:0.12398 +[84] validation_0-rmse:0.17189 validation_1-rmse:0.12334 +[85] validation_0-rmse:0.17155 validation_1-rmse:0.12280 +[86] validation_0-rmse:0.17124 validation_1-rmse:0.12230 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12178 +[88] validation_0-rmse:0.17086 validation_1-rmse:0.12118 +[89] validation_0-rmse:0.17064 validation_1-rmse:0.12049 +[90] validation_0-rmse:0.17029 validation_1-rmse:0.11993 +[91] validation_0-rmse:0.16981 validation_1-rmse:0.11942 +[92] validation_0-rmse:0.16950 validation_1-rmse:0.11894 +[93] validation_0-rmse:0.16937 validation_1-rmse:0.11833 +[94] validation_0-rmse:0.16928 validation_1-rmse:0.11786 +[95] validation_0-rmse:0.16899 validation_1-rmse:0.11735 +[96] validation_0-rmse:0.16869 validation_1-rmse:0.11693 +[97] validation_0-rmse:0.16843 validation_1-rmse:0.11650 +[98] validation_0-rmse:0.16829 validation_1-rmse:0.11591 +[99] validation_0-rmse:0.16802 validation_1-rmse:0.11547 +2025-04-29 01:56:47,778 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.57 secs) -------------------- +2025-04-29 01:56:47,779 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:48,320 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:56:48,321 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740009600_prediction.feather +2025-04-29 01:56:48,327 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,333 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:48,334 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:48,346 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,353 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:48,354 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:48,361 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740009600/cb_sol_1740009600 +2025-04-29 01:56:48,361 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:48,383 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:48,383 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:56:53,532 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:53,533 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29357 validation_1-rmse:0.28852 +[1] validation_0-rmse:0.28850 validation_1-rmse:0.28219 +[2] validation_0-rmse:0.28292 validation_1-rmse:0.27618 +[3] validation_0-rmse:0.27862 validation_1-rmse:0.27046 +[4] validation_0-rmse:0.27383 validation_1-rmse:0.26510 +[5] validation_0-rmse:0.27018 validation_1-rmse:0.25989 +[6] validation_0-rmse:0.26615 validation_1-rmse:0.25477 +[7] validation_0-rmse:0.26234 validation_1-rmse:0.25007 +[8] validation_0-rmse:0.25794 validation_1-rmse:0.24560 +[9] validation_0-rmse:0.25417 validation_1-rmse:0.24109 +[10] validation_0-rmse:0.25083 validation_1-rmse:0.23700 +[11] validation_0-rmse:0.24683 validation_1-rmse:0.23303 +[12] validation_0-rmse:0.24384 validation_1-rmse:0.22908 +[13] validation_0-rmse:0.24093 validation_1-rmse:0.22542 +[14] validation_0-rmse:0.23743 validation_1-rmse:0.22186 +[15] validation_0-rmse:0.23484 validation_1-rmse:0.21841 +[16] validation_0-rmse:0.23215 validation_1-rmse:0.21525 +[17] validation_0-rmse:0.22951 validation_1-rmse:0.21206 +[18] validation_0-rmse:0.22658 validation_1-rmse:0.20906 +[19] validation_0-rmse:0.22440 validation_1-rmse:0.20615 +[20] validation_0-rmse:0.22193 validation_1-rmse:0.20314 +[21] validation_0-rmse:0.22009 validation_1-rmse:0.20016 +[22] validation_0-rmse:0.21755 validation_1-rmse:0.19751 +[23] validation_0-rmse:0.21578 validation_1-rmse:0.19498 +[24] validation_0-rmse:0.21440 validation_1-rmse:0.19241 +[25] validation_0-rmse:0.21229 validation_1-rmse:0.19006 +[26] validation_0-rmse:0.21038 validation_1-rmse:0.18780 +[27] validation_0-rmse:0.20897 validation_1-rmse:0.18529 +[28] validation_0-rmse:0.20703 validation_1-rmse:0.18313 +[29] validation_0-rmse:0.20556 validation_1-rmse:0.18091 +[30] validation_0-rmse:0.20384 validation_1-rmse:0.17884 +[31] validation_0-rmse:0.20281 validation_1-rmse:0.17690 +[32] validation_0-rmse:0.20169 validation_1-rmse:0.17483 +[33] validation_0-rmse:0.20012 validation_1-rmse:0.17300 +[34] validation_0-rmse:0.19876 validation_1-rmse:0.17106 +[35] validation_0-rmse:0.19755 validation_1-rmse:0.16934 +[36] validation_0-rmse:0.19649 validation_1-rmse:0.16752 +[37] validation_0-rmse:0.19501 validation_1-rmse:0.16586 +[38] validation_0-rmse:0.19423 validation_1-rmse:0.16418 +[39] validation_0-rmse:0.19297 validation_1-rmse:0.16264 +[40] validation_0-rmse:0.19162 validation_1-rmse:0.16092 +[41] validation_0-rmse:0.19049 validation_1-rmse:0.15952 +[42] validation_0-rmse:0.18925 validation_1-rmse:0.15810 +[43] validation_0-rmse:0.18845 validation_1-rmse:0.15638 +[44] validation_0-rmse:0.18730 validation_1-rmse:0.15506 +[45] validation_0-rmse:0.18661 validation_1-rmse:0.15357 +[46] validation_0-rmse:0.18563 validation_1-rmse:0.15226 +[47] validation_0-rmse:0.18473 validation_1-rmse:0.15101 +[48] validation_0-rmse:0.18399 validation_1-rmse:0.14957 +[49] validation_0-rmse:0.18304 validation_1-rmse:0.14841 +[50] validation_0-rmse:0.18219 validation_1-rmse:0.14717 +[51] validation_0-rmse:0.18131 validation_1-rmse:0.14599 +[52] validation_0-rmse:0.18043 validation_1-rmse:0.14492 +[53] validation_0-rmse:0.17966 validation_1-rmse:0.14388 +[54] validation_0-rmse:0.17901 validation_1-rmse:0.14274 +[55] validation_0-rmse:0.17850 validation_1-rmse:0.14134 +[56] validation_0-rmse:0.17764 validation_1-rmse:0.14035 +[57] validation_0-rmse:0.17682 validation_1-rmse:0.13937 +[58] validation_0-rmse:0.17604 validation_1-rmse:0.13844 +[59] validation_0-rmse:0.17526 validation_1-rmse:0.13754 +[60] validation_0-rmse:0.17488 validation_1-rmse:0.13621 +[61] validation_0-rmse:0.17432 validation_1-rmse:0.13530 +[62] validation_0-rmse:0.17345 validation_1-rmse:0.13439 +[63] validation_0-rmse:0.17284 validation_1-rmse:0.13358 +[64] validation_0-rmse:0.17213 validation_1-rmse:0.13278 +[65] validation_0-rmse:0.17164 validation_1-rmse:0.13175 +[66] validation_0-rmse:0.17098 validation_1-rmse:0.13088 +[67] validation_0-rmse:0.17049 validation_1-rmse:0.13002 +[68] validation_0-rmse:0.17000 validation_1-rmse:0.12918 +[69] validation_0-rmse:0.16969 validation_1-rmse:0.12815 +[70] validation_0-rmse:0.16917 validation_1-rmse:0.12746 +[71] validation_0-rmse:0.16857 validation_1-rmse:0.12678 +[72] validation_0-rmse:0.16830 validation_1-rmse:0.12595 +[73] validation_0-rmse:0.16793 validation_1-rmse:0.12522 +[74] validation_0-rmse:0.16752 validation_1-rmse:0.12457 +[75] validation_0-rmse:0.16704 validation_1-rmse:0.12395 +[76] validation_0-rmse:0.16668 validation_1-rmse:0.12316 +[77] validation_0-rmse:0.16621 validation_1-rmse:0.12251 +[78] validation_0-rmse:0.16591 validation_1-rmse:0.12185 +[79] validation_0-rmse:0.16550 validation_1-rmse:0.12115 +[80] validation_0-rmse:0.16506 validation_1-rmse:0.12055 +[81] validation_0-rmse:0.16467 validation_1-rmse:0.12001 +[82] validation_0-rmse:0.16422 validation_1-rmse:0.11944 +[83] validation_0-rmse:0.16379 validation_1-rmse:0.11892 +[84] validation_0-rmse:0.16344 validation_1-rmse:0.11825 +[85] validation_0-rmse:0.16317 validation_1-rmse:0.11766 +[86] validation_0-rmse:0.16289 validation_1-rmse:0.11712 +[87] validation_0-rmse:0.16271 validation_1-rmse:0.11639 +[88] validation_0-rmse:0.16236 validation_1-rmse:0.11591 +[89] validation_0-rmse:0.16210 validation_1-rmse:0.11515 +[90] validation_0-rmse:0.16170 validation_1-rmse:0.11457 +[91] validation_0-rmse:0.16149 validation_1-rmse:0.11411 +[92] validation_0-rmse:0.16132 validation_1-rmse:0.11360 +[93] validation_0-rmse:0.16108 validation_1-rmse:0.11292 +[94] validation_0-rmse:0.16077 validation_1-rmse:0.11247 +[95] validation_0-rmse:0.16040 validation_1-rmse:0.11205 +[96] validation_0-rmse:0.16017 validation_1-rmse:0.11157 +[97] validation_0-rmse:0.15988 validation_1-rmse:0.11117 +[98] validation_0-rmse:0.15964 validation_1-rmse:0.11074 +[99] validation_0-rmse:0.15958 validation_1-rmse:0.11029 +2025-04-29 01:56:54,862 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.50 secs) -------------------- +2025-04-29 01:56:54,863 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:55,419 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:56:55,420 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740873600_prediction.feather +2025-04-29 01:56:55,426 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,433 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:55,435 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:55,445 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,452 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:56:55,453 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:56:55,459 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740873600/cb_sol_1740873600 +2025-04-29 01:56:55,459 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:55,481 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:55,482 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:57:00,566 - datasieve.pipeline - INFO - DI tossed 2417 predictions for being too far from training data. +2025-04-29 01:57:00,569 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:00,570 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.33058 validation_1-rmse:0.29214 +[1] validation_0-rmse:0.32414 validation_1-rmse:0.28558 +[2] validation_0-rmse:0.31832 validation_1-rmse:0.27962 +[3] validation_0-rmse:0.31280 validation_1-rmse:0.27380 +[4] validation_0-rmse:0.30679 validation_1-rmse:0.26829 +[5] validation_0-rmse:0.30182 validation_1-rmse:0.26306 +[6] validation_0-rmse:0.29686 validation_1-rmse:0.25797 +[7] validation_0-rmse:0.29291 validation_1-rmse:0.25323 +[8] validation_0-rmse:0.28868 validation_1-rmse:0.24871 +[9] validation_0-rmse:0.28559 validation_1-rmse:0.24437 +[10] validation_0-rmse:0.28262 validation_1-rmse:0.24018 +[11] validation_0-rmse:0.27890 validation_1-rmse:0.23606 +[12] validation_0-rmse:0.27663 validation_1-rmse:0.23225 +[13] validation_0-rmse:0.27306 validation_1-rmse:0.22870 +[14] validation_0-rmse:0.26948 validation_1-rmse:0.22493 +[15] validation_0-rmse:0.26663 validation_1-rmse:0.22157 +[16] validation_0-rmse:0.26427 validation_1-rmse:0.21817 +[17] validation_0-rmse:0.26195 validation_1-rmse:0.21492 +[18] validation_0-rmse:0.25856 validation_1-rmse:0.21177 +[19] validation_0-rmse:0.25606 validation_1-rmse:0.20861 +[20] validation_0-rmse:0.25343 validation_1-rmse:0.20580 +[21] validation_0-rmse:0.25243 validation_1-rmse:0.20301 +[22] validation_0-rmse:0.25066 validation_1-rmse:0.20027 +[23] validation_0-rmse:0.24864 validation_1-rmse:0.19761 +[24] validation_0-rmse:0.24630 validation_1-rmse:0.19522 +[25] validation_0-rmse:0.24491 validation_1-rmse:0.19283 +[26] validation_0-rmse:0.24339 validation_1-rmse:0.19036 +[27] validation_0-rmse:0.24108 validation_1-rmse:0.18818 +[28] validation_0-rmse:0.23976 validation_1-rmse:0.18592 +[29] validation_0-rmse:0.23882 validation_1-rmse:0.18348 +[30] validation_0-rmse:0.23676 validation_1-rmse:0.18142 +[31] validation_0-rmse:0.23520 validation_1-rmse:0.17945 +[32] validation_0-rmse:0.23395 validation_1-rmse:0.17754 +[33] validation_0-rmse:0.23229 validation_1-rmse:0.17545 +[34] validation_0-rmse:0.23073 validation_1-rmse:0.17360 +[35] validation_0-rmse:0.22951 validation_1-rmse:0.17182 +[36] validation_0-rmse:0.22806 validation_1-rmse:0.16995 +[37] validation_0-rmse:0.22713 validation_1-rmse:0.16834 +[38] validation_0-rmse:0.22541 validation_1-rmse:0.16668 +[39] validation_0-rmse:0.22393 validation_1-rmse:0.16509 +[40] validation_0-rmse:0.22282 validation_1-rmse:0.16343 +[41] validation_0-rmse:0.22168 validation_1-rmse:0.16185 +[42] validation_0-rmse:0.22085 validation_1-rmse:0.16046 +[43] validation_0-rmse:0.21991 validation_1-rmse:0.15907 +[44] validation_0-rmse:0.21833 validation_1-rmse:0.15756 +[45] validation_0-rmse:0.21710 validation_1-rmse:0.15618 +[46] validation_0-rmse:0.21619 validation_1-rmse:0.15490 +[47] validation_0-rmse:0.21518 validation_1-rmse:0.15345 +[48] validation_0-rmse:0.21402 validation_1-rmse:0.15221 +[49] validation_0-rmse:0.21305 validation_1-rmse:0.15086 +[50] validation_0-rmse:0.21229 validation_1-rmse:0.14968 +[51] validation_0-rmse:0.21119 validation_1-rmse:0.14854 +[52] validation_0-rmse:0.21019 validation_1-rmse:0.14745 +[53] validation_0-rmse:0.20924 validation_1-rmse:0.14637 +[54] validation_0-rmse:0.20982 validation_1-rmse:0.14517 +[55] validation_0-rmse:0.20888 validation_1-rmse:0.14405 +[56] validation_0-rmse:0.20806 validation_1-rmse:0.14305 +[57] validation_0-rmse:0.20822 validation_1-rmse:0.14169 +[58] validation_0-rmse:0.20741 validation_1-rmse:0.14071 +[59] validation_0-rmse:0.20663 validation_1-rmse:0.13976 +[60] validation_0-rmse:0.20602 validation_1-rmse:0.13882 +[61] validation_0-rmse:0.20523 validation_1-rmse:0.13776 +[62] validation_0-rmse:0.20558 validation_1-rmse:0.13689 +[63] validation_0-rmse:0.20501 validation_1-rmse:0.13605 +[64] validation_0-rmse:0.20348 validation_1-rmse:0.13462 +[65] validation_0-rmse:0.20273 validation_1-rmse:0.13382 +[66] validation_0-rmse:0.20203 validation_1-rmse:0.13306 +[67] validation_0-rmse:0.20166 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.20002 validation_1-rmse:0.13102 +[69] validation_0-rmse:0.19928 validation_1-rmse:0.13021 +[70] validation_0-rmse:0.19870 validation_1-rmse:0.12946 +[71] validation_0-rmse:0.19830 validation_1-rmse:0.12876 +[72] validation_0-rmse:0.19814 validation_1-rmse:0.12801 +[73] validation_0-rmse:0.19798 validation_1-rmse:0.12711 +[74] validation_0-rmse:0.19746 validation_1-rmse:0.12649 +[75] validation_0-rmse:0.19701 validation_1-rmse:0.12588 +[76] validation_0-rmse:0.19555 validation_1-rmse:0.12467 +[77] validation_0-rmse:0.19514 validation_1-rmse:0.12407 +[78] validation_0-rmse:0.19468 validation_1-rmse:0.12347 +[79] validation_0-rmse:0.19439 validation_1-rmse:0.12277 +[80] validation_0-rmse:0.19473 validation_1-rmse:0.12220 +[81] validation_0-rmse:0.19448 validation_1-rmse:0.12154 +[82] validation_0-rmse:0.19418 validation_1-rmse:0.12086 +[83] validation_0-rmse:0.19370 validation_1-rmse:0.12030 +[84] validation_0-rmse:0.19346 validation_1-rmse:0.11976 +[85] validation_0-rmse:0.19322 validation_1-rmse:0.11879 +[86] validation_0-rmse:0.19282 validation_1-rmse:0.11819 +[87] validation_0-rmse:0.19226 validation_1-rmse:0.11770 +[88] validation_0-rmse:0.19187 validation_1-rmse:0.11719 +[89] validation_0-rmse:0.19145 validation_1-rmse:0.11671 +[90] validation_0-rmse:0.19134 validation_1-rmse:0.11619 +[91] validation_0-rmse:0.19030 validation_1-rmse:0.11531 +[92] validation_0-rmse:0.18998 validation_1-rmse:0.11487 +[93] validation_0-rmse:0.18945 validation_1-rmse:0.11445 +[94] validation_0-rmse:0.18919 validation_1-rmse:0.11395 +[95] validation_0-rmse:0.18862 validation_1-rmse:0.11324 +[96] validation_0-rmse:0.18824 validation_1-rmse:0.11283 +[97] validation_0-rmse:0.18778 validation_1-rmse:0.11225 +[98] validation_0-rmse:0.18755 validation_1-rmse:0.11186 +[99] validation_0-rmse:0.18742 validation_1-rmse:0.11149 +2025-04-29 01:57:02,441 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.98 secs) -------------------- +2025-04-29 01:57:02,442 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:02,968 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:57:02,968 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1741737600_prediction.feather +2025-04-29 01:57:02,980 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:02,987 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:57:02,989 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:57:03,001 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:03,007 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:03,009 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:03,014 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1741737600/cb_sol_1741737600 +2025-04-29 01:57:03,015 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:03,042 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:03,042 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:57:08,138 - datasieve.pipeline - INFO - DI tossed 3 predictions for being too far from training data. +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25025 validation_1-rmse:0.24842 +[1] validation_0-rmse:0.24557 validation_1-rmse:0.24302 +[2] validation_0-rmse:0.24132 validation_1-rmse:0.23806 +[3] validation_0-rmse:0.23733 validation_1-rmse:0.23332 +[4] validation_0-rmse:0.23376 validation_1-rmse:0.22859 +[5] validation_0-rmse:0.23049 validation_1-rmse:0.22444 +[6] validation_0-rmse:0.22702 validation_1-rmse:0.22025 +[7] validation_0-rmse:0.22380 validation_1-rmse:0.21631 +[8] validation_0-rmse:0.22066 validation_1-rmse:0.21250 +[9] validation_0-rmse:0.21772 validation_1-rmse:0.20892 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.20536 +[11] validation_0-rmse:0.21219 validation_1-rmse:0.20211 +[12] validation_0-rmse:0.20968 validation_1-rmse:0.19897 +[13] validation_0-rmse:0.20730 validation_1-rmse:0.19597 +[14] validation_0-rmse:0.20552 validation_1-rmse:0.19281 +[15] validation_0-rmse:0.20361 validation_1-rmse:0.18992 +[16] validation_0-rmse:0.20165 validation_1-rmse:0.18721 +[17] validation_0-rmse:0.19948 validation_1-rmse:0.18463 +[18] validation_0-rmse:0.19807 validation_1-rmse:0.18193 +[19] validation_0-rmse:0.19703 validation_1-rmse:0.17939 +[20] validation_0-rmse:0.19569 validation_1-rmse:0.17683 +[21] validation_0-rmse:0.19388 validation_1-rmse:0.17462 +[22] validation_0-rmse:0.19219 validation_1-rmse:0.17240 +[23] validation_0-rmse:0.19062 validation_1-rmse:0.17026 +[24] validation_0-rmse:0.18933 validation_1-rmse:0.16813 +[25] validation_0-rmse:0.18829 validation_1-rmse:0.16598 +[26] validation_0-rmse:0.18704 validation_1-rmse:0.16411 +[27] validation_0-rmse:0.18563 validation_1-rmse:0.16221 +[28] validation_0-rmse:0.18446 validation_1-rmse:0.16034 +[29] validation_0-rmse:0.18316 validation_1-rmse:0.15841 +[30] validation_0-rmse:0.18192 validation_1-rmse:0.15674 +[31] validation_0-rmse:0.18091 validation_1-rmse:0.15479 +[32] validation_0-rmse:0.18003 validation_1-rmse:0.15312 +[33] validation_0-rmse:0.17886 validation_1-rmse:0.15150 +[34] validation_0-rmse:0.17786 validation_1-rmse:0.14997 +[35] validation_0-rmse:0.17692 validation_1-rmse:0.14855 +[36] validation_0-rmse:0.17613 validation_1-rmse:0.14709 +[37] validation_0-rmse:0.17547 validation_1-rmse:0.14549 +[38] validation_0-rmse:0.17467 validation_1-rmse:0.14404 +[39] validation_0-rmse:0.17393 validation_1-rmse:0.14267 +[40] validation_0-rmse:0.17348 validation_1-rmse:0.14118 +[41] validation_0-rmse:0.17258 validation_1-rmse:0.13993 +[42] validation_0-rmse:0.17168 validation_1-rmse:0.13871 +[43] validation_0-rmse:0.17077 validation_1-rmse:0.13757 +[44] validation_0-rmse:0.17015 validation_1-rmse:0.13621 +[45] validation_0-rmse:0.16924 validation_1-rmse:0.13509 +[46] validation_0-rmse:0.16833 validation_1-rmse:0.13401 +[47] validation_0-rmse:0.16756 validation_1-rmse:0.13297 +[48] validation_0-rmse:0.16717 validation_1-rmse:0.13198 +[49] validation_0-rmse:0.16664 validation_1-rmse:0.13081 +[50] validation_0-rmse:0.16615 validation_1-rmse:0.12979 +[51] validation_0-rmse:0.16541 validation_1-rmse:0.12879 +[52] validation_0-rmse:0.16478 validation_1-rmse:0.12767 +[53] validation_0-rmse:0.16408 validation_1-rmse:0.12675 +[54] validation_0-rmse:0.16363 validation_1-rmse:0.12571 +[55] validation_0-rmse:0.16320 validation_1-rmse:0.12485 +[56] validation_0-rmse:0.16253 validation_1-rmse:0.12398 +[57] validation_0-rmse:0.16192 validation_1-rmse:0.12307 +[58] validation_0-rmse:0.16149 validation_1-rmse:0.12229 +[59] validation_0-rmse:0.16137 validation_1-rmse:0.12128 +[60] validation_0-rmse:0.16117 validation_1-rmse:0.12045 +[61] validation_0-rmse:0.16064 validation_1-rmse:0.11966 +[62] validation_0-rmse:0.16050 validation_1-rmse:0.11890 +[63] validation_0-rmse:0.16003 validation_1-rmse:0.11809 +[64] validation_0-rmse:0.15969 validation_1-rmse:0.11739 +[65] validation_0-rmse:0.15922 validation_1-rmse:0.11661 +[66] validation_0-rmse:0.15868 validation_1-rmse:0.11577 +[67] validation_0-rmse:0.15830 validation_1-rmse:0.11509 +[68] validation_0-rmse:0.15789 validation_1-rmse:0.11446 +[69] validation_0-rmse:0.15733 validation_1-rmse:0.11372 +[70] validation_0-rmse:0.15694 validation_1-rmse:0.11307 +[71] validation_0-rmse:0.15692 validation_1-rmse:0.11224 +[72] validation_0-rmse:0.15659 validation_1-rmse:0.11166 +[73] validation_0-rmse:0.15634 validation_1-rmse:0.11111 +[74] validation_0-rmse:0.15595 validation_1-rmse:0.11056 +[75] validation_0-rmse:0.15579 validation_1-rmse:0.10985 +[76] validation_0-rmse:0.15543 validation_1-rmse:0.10903 +[77] validation_0-rmse:0.15500 validation_1-rmse:0.10848 +[78] validation_0-rmse:0.15499 validation_1-rmse:0.10778 +[79] validation_0-rmse:0.15471 validation_1-rmse:0.10721 +[80] validation_0-rmse:0.15442 validation_1-rmse:0.10666 +[81] validation_0-rmse:0.15416 validation_1-rmse:0.10608 +[82] validation_0-rmse:0.15388 validation_1-rmse:0.10560 +[83] validation_0-rmse:0.15368 validation_1-rmse:0.10498 +[84] validation_0-rmse:0.15346 validation_1-rmse:0.10449 +[85] validation_0-rmse:0.15329 validation_1-rmse:0.10392 +[86] validation_0-rmse:0.15302 validation_1-rmse:0.10347 +[87] validation_0-rmse:0.15270 validation_1-rmse:0.10303 +[88] validation_0-rmse:0.15259 validation_1-rmse:0.10258 +[89] validation_0-rmse:0.15269 validation_1-rmse:0.10204 +[90] validation_0-rmse:0.15239 validation_1-rmse:0.10159 +[91] validation_0-rmse:0.15204 validation_1-rmse:0.10116 +[92] validation_0-rmse:0.15175 validation_1-rmse:0.10070 +[93] validation_0-rmse:0.15167 validation_1-rmse:0.10017 +[94] validation_0-rmse:0.15154 validation_1-rmse:0.09982 +[95] validation_0-rmse:0.15122 validation_1-rmse:0.09932 +[96] validation_0-rmse:0.15119 validation_1-rmse:0.09880 +[97] validation_0-rmse:0.15112 validation_1-rmse:0.09842 +[98] validation_0-rmse:0.15095 validation_1-rmse:0.09807 +[99] validation_0-rmse:0.15075 validation_1-rmse:0.09770 +2025-04-29 01:57:09,614 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:09,615 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:10,150 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:57:10,151 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1742601600_prediction.feather +2025-04-29 01:57:10,159 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,167 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:10,168 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:10,181 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,188 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:10,190 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:10,195 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1742601600/cb_sol_1742601600 +2025-04-29 01:57:10,196 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:10,218 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:10,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:57:15,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:15,186 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24126 validation_1-rmse:0.24985 +[1] validation_0-rmse:0.23710 validation_1-rmse:0.24457 +[2] validation_0-rmse:0.23323 validation_1-rmse:0.23968 +[3] validation_0-rmse:0.22960 validation_1-rmse:0.23491 +[4] validation_0-rmse:0.22600 validation_1-rmse:0.23038 +[5] validation_0-rmse:0.22255 validation_1-rmse:0.22616 +[6] validation_0-rmse:0.21946 validation_1-rmse:0.22213 +[7] validation_0-rmse:0.21640 validation_1-rmse:0.21827 +[8] validation_0-rmse:0.21381 validation_1-rmse:0.21453 +[9] validation_0-rmse:0.21110 validation_1-rmse:0.21092 +[10] validation_0-rmse:0.20854 validation_1-rmse:0.20742 +[11] validation_0-rmse:0.20586 validation_1-rmse:0.20418 +[12] validation_0-rmse:0.20373 validation_1-rmse:0.20097 +[13] validation_0-rmse:0.20156 validation_1-rmse:0.19779 +[14] validation_0-rmse:0.19934 validation_1-rmse:0.19493 +[15] validation_0-rmse:0.19739 validation_1-rmse:0.19211 +[16] validation_0-rmse:0.19537 validation_1-rmse:0.18929 +[17] validation_0-rmse:0.19333 validation_1-rmse:0.18671 +[18] validation_0-rmse:0.19163 validation_1-rmse:0.18396 +[19] validation_0-rmse:0.18975 validation_1-rmse:0.18157 +[20] validation_0-rmse:0.18799 validation_1-rmse:0.17903 +[21] validation_0-rmse:0.18612 validation_1-rmse:0.17673 +[22] validation_0-rmse:0.18451 validation_1-rmse:0.17454 +[23] validation_0-rmse:0.18299 validation_1-rmse:0.17225 +[24] validation_0-rmse:0.18150 validation_1-rmse:0.17025 +[25] validation_0-rmse:0.18016 validation_1-rmse:0.16803 +[26] validation_0-rmse:0.17866 validation_1-rmse:0.16614 +[27] validation_0-rmse:0.17732 validation_1-rmse:0.16429 +[28] validation_0-rmse:0.17619 validation_1-rmse:0.16247 +[29] validation_0-rmse:0.17494 validation_1-rmse:0.16080 +[30] validation_0-rmse:0.17391 validation_1-rmse:0.15889 +[31] validation_0-rmse:0.17282 validation_1-rmse:0.15724 +[32] validation_0-rmse:0.17156 validation_1-rmse:0.15548 +[33] validation_0-rmse:0.17054 validation_1-rmse:0.15393 +[34] validation_0-rmse:0.16943 validation_1-rmse:0.15244 +[35] validation_0-rmse:0.16841 validation_1-rmse:0.15088 +[36] validation_0-rmse:0.16736 validation_1-rmse:0.14950 +[37] validation_0-rmse:0.16647 validation_1-rmse:0.14797 +[38] validation_0-rmse:0.16544 validation_1-rmse:0.14641 +[39] validation_0-rmse:0.16454 validation_1-rmse:0.14508 +[40] validation_0-rmse:0.16357 validation_1-rmse:0.14380 +[41] validation_0-rmse:0.16266 validation_1-rmse:0.14261 +[42] validation_0-rmse:0.16198 validation_1-rmse:0.14134 +[43] validation_0-rmse:0.16105 validation_1-rmse:0.14019 +[44] validation_0-rmse:0.16046 validation_1-rmse:0.13896 +[45] validation_0-rmse:0.15963 validation_1-rmse:0.13773 +[46] validation_0-rmse:0.15899 validation_1-rmse:0.13662 +[47] validation_0-rmse:0.15822 validation_1-rmse:0.13555 +[48] validation_0-rmse:0.15757 validation_1-rmse:0.13452 +[49] validation_0-rmse:0.15688 validation_1-rmse:0.13322 +[50] validation_0-rmse:0.15627 validation_1-rmse:0.13206 +[51] validation_0-rmse:0.15558 validation_1-rmse:0.13110 +[52] validation_0-rmse:0.15493 validation_1-rmse:0.13017 +[53] validation_0-rmse:0.15429 validation_1-rmse:0.12924 +[54] validation_0-rmse:0.15365 validation_1-rmse:0.12838 +[55] validation_0-rmse:0.15303 validation_1-rmse:0.12741 +[56] validation_0-rmse:0.15258 validation_1-rmse:0.12653 +[57] validation_0-rmse:0.15202 validation_1-rmse:0.12569 +[58] validation_0-rmse:0.15142 validation_1-rmse:0.12478 +[59] validation_0-rmse:0.15106 validation_1-rmse:0.12392 +[60] validation_0-rmse:0.15049 validation_1-rmse:0.12297 +[61] validation_0-rmse:0.14990 validation_1-rmse:0.12223 +[62] validation_0-rmse:0.14932 validation_1-rmse:0.12144 +[63] validation_0-rmse:0.14876 validation_1-rmse:0.12071 +[64] validation_0-rmse:0.14826 validation_1-rmse:0.12000 +[65] validation_0-rmse:0.14788 validation_1-rmse:0.11931 +[66] validation_0-rmse:0.14753 validation_1-rmse:0.11842 +[67] validation_0-rmse:0.14714 validation_1-rmse:0.11776 +[68] validation_0-rmse:0.14665 validation_1-rmse:0.11706 +[69] validation_0-rmse:0.14655 validation_1-rmse:0.11614 +[70] validation_0-rmse:0.14616 validation_1-rmse:0.11556 +[71] validation_0-rmse:0.14579 validation_1-rmse:0.11478 +[72] validation_0-rmse:0.14533 validation_1-rmse:0.11418 +[73] validation_0-rmse:0.14491 validation_1-rmse:0.11358 +[74] validation_0-rmse:0.14448 validation_1-rmse:0.11300 +[75] validation_0-rmse:0.14446 validation_1-rmse:0.11235 +[76] validation_0-rmse:0.14414 validation_1-rmse:0.11173 +[77] validation_0-rmse:0.14371 validation_1-rmse:0.11116 +[78] validation_0-rmse:0.14344 validation_1-rmse:0.11066 +[79] validation_0-rmse:0.14321 validation_1-rmse:0.10996 +[80] validation_0-rmse:0.14280 validation_1-rmse:0.10942 +[81] validation_0-rmse:0.14250 validation_1-rmse:0.10885 +[82] validation_0-rmse:0.14222 validation_1-rmse:0.10837 +[83] validation_0-rmse:0.14184 validation_1-rmse:0.10787 +[84] validation_0-rmse:0.14140 validation_1-rmse:0.10731 +[85] validation_0-rmse:0.14114 validation_1-rmse:0.10683 +[86] validation_0-rmse:0.14100 validation_1-rmse:0.10625 +[87] validation_0-rmse:0.14077 validation_1-rmse:0.10574 +[88] validation_0-rmse:0.14048 validation_1-rmse:0.10534 +[89] validation_0-rmse:0.14010 validation_1-rmse:0.10485 +[90] validation_0-rmse:0.13990 validation_1-rmse:0.10443 +[91] validation_0-rmse:0.13956 validation_1-rmse:0.10400 +[92] validation_0-rmse:0.13949 validation_1-rmse:0.10341 +[93] validation_0-rmse:0.13930 validation_1-rmse:0.10298 +[94] validation_0-rmse:0.13905 validation_1-rmse:0.10254 +[95] validation_0-rmse:0.13884 validation_1-rmse:0.10211 +[96] validation_0-rmse:0.13867 validation_1-rmse:0.10167 +[97] validation_0-rmse:0.13859 validation_1-rmse:0.10114 +[98] validation_0-rmse:0.13839 validation_1-rmse:0.10078 +[99] validation_0-rmse:0.13818 validation_1-rmse:0.10038 +2025-04-29 01:57:16,538 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.34 secs) -------------------- +2025-04-29 01:57:16,539 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:17,068 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:57:17,069 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather +2025-04-29 01:57:17,084 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,092 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:17,094 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:17,108 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,115 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:17,117 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:17,124 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 +2025-04-29 01:57:17,125 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:17,151 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:17,151 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.23717 validation_1-rmse:0.24954 +[1] validation_0-rmse:0.23311 validation_1-rmse:0.24432 +[2] validation_0-rmse:0.22894 validation_1-rmse:0.23928 +[3] validation_0-rmse:0.22483 validation_1-rmse:0.23452 +[4] validation_0-rmse:0.22141 validation_1-rmse:0.23005 +[5] validation_0-rmse:0.21750 validation_1-rmse:0.22575 +[6] validation_0-rmse:0.21419 validation_1-rmse:0.22161 +[7] validation_0-rmse:0.21074 validation_1-rmse:0.21764 +[8] validation_0-rmse:0.20776 validation_1-rmse:0.21374 +[9] validation_0-rmse:0.20479 validation_1-rmse:0.21020 +[10] validation_0-rmse:0.20193 validation_1-rmse:0.20664 +[11] validation_0-rmse:0.19915 validation_1-rmse:0.20326 +[12] validation_0-rmse:0.19683 validation_1-rmse:0.20013 +[13] validation_0-rmse:0.19459 validation_1-rmse:0.19705 +[14] validation_0-rmse:0.19243 validation_1-rmse:0.19415 +[15] validation_0-rmse:0.19013 validation_1-rmse:0.19115 +[16] validation_0-rmse:0.18828 validation_1-rmse:0.18836 +[17] validation_0-rmse:0.18621 validation_1-rmse:0.18557 +[18] validation_0-rmse:0.18402 validation_1-rmse:0.18302 +[19] validation_0-rmse:0.18198 validation_1-rmse:0.18050 +[20] validation_0-rmse:0.18015 validation_1-rmse:0.17803 +[21] validation_0-rmse:0.17857 validation_1-rmse:0.17575 +[22] validation_0-rmse:0.17681 validation_1-rmse:0.17350 +[23] validation_0-rmse:0.17537 validation_1-rmse:0.17132 +[24] validation_0-rmse:0.17377 validation_1-rmse:0.16919 +[25] validation_0-rmse:0.17225 validation_1-rmse:0.16720 +[26] validation_0-rmse:0.17072 validation_1-rmse:0.16529 +[27] validation_0-rmse:0.16931 validation_1-rmse:0.16310 +[28] validation_0-rmse:0.16784 validation_1-rmse:0.16126 +[29] validation_0-rmse:0.16650 validation_1-rmse:0.15940 +[30] validation_0-rmse:0.16512 validation_1-rmse:0.15771 +[31] validation_0-rmse:0.16392 validation_1-rmse:0.15605 +[32] validation_0-rmse:0.16287 validation_1-rmse:0.15428 +[33] validation_0-rmse:0.16159 validation_1-rmse:0.15277 +[34] validation_0-rmse:0.16033 validation_1-rmse:0.15125 +[35] validation_0-rmse:0.15910 validation_1-rmse:0.14974 +[36] validation_0-rmse:0.15821 validation_1-rmse:0.14832 +[37] validation_0-rmse:0.15733 validation_1-rmse:0.14664 +[38] validation_0-rmse:0.15624 validation_1-rmse:0.14525 +[39] validation_0-rmse:0.15518 validation_1-rmse:0.14395 +[40] validation_0-rmse:0.15451 validation_1-rmse:0.14267 +[41] validation_0-rmse:0.15396 validation_1-rmse:0.14127 +[42] validation_0-rmse:0.15309 validation_1-rmse:0.14006 +[43] validation_0-rmse:0.15219 validation_1-rmse:0.13890 +[44] validation_0-rmse:0.15156 validation_1-rmse:0.13749 +[45] validation_0-rmse:0.15061 validation_1-rmse:0.13637 +[46] validation_0-rmse:0.14982 validation_1-rmse:0.13528 +[47] validation_0-rmse:0.14918 validation_1-rmse:0.13414 +[48] validation_0-rmse:0.14840 validation_1-rmse:0.13312 +[49] validation_0-rmse:0.14802 validation_1-rmse:0.13212 +[50] validation_0-rmse:0.14738 validation_1-rmse:0.13089 +[51] validation_0-rmse:0.14671 validation_1-rmse:0.12994 +[52] validation_0-rmse:0.14604 validation_1-rmse:0.12894 +[53] validation_0-rmse:0.14534 validation_1-rmse:0.12802 +[54] validation_0-rmse:0.14464 validation_1-rmse:0.12718 +[55] validation_0-rmse:0.14423 validation_1-rmse:0.12625 +[56] validation_0-rmse:0.14371 validation_1-rmse:0.12531 +[57] validation_0-rmse:0.14321 validation_1-rmse:0.12446 +[58] validation_0-rmse:0.14279 validation_1-rmse:0.12346 +[59] validation_0-rmse:0.14234 validation_1-rmse:0.12257 +[60] validation_0-rmse:0.14194 validation_1-rmse:0.12181 +[61] validation_0-rmse:0.14176 validation_1-rmse:0.12077 +[62] validation_0-rmse:0.14120 validation_1-rmse:0.12003 +[63] validation_0-rmse:0.14073 validation_1-rmse:0.11932 +[64] validation_0-rmse:0.14023 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.14001 validation_1-rmse:0.11791 +[66] validation_0-rmse:0.13966 validation_1-rmse:0.11720 +[67] validation_0-rmse:0.13920 validation_1-rmse:0.11644 +[68] validation_0-rmse:0.13872 validation_1-rmse:0.11560 +[69] validation_0-rmse:0.13831 validation_1-rmse:0.11494 +[70] validation_0-rmse:0.13808 validation_1-rmse:0.11425 +[71] validation_0-rmse:0.13762 validation_1-rmse:0.11348 +[72] validation_0-rmse:0.13725 validation_1-rmse:0.11284 +[73] validation_0-rmse:0.13681 validation_1-rmse:0.11225 +[74] validation_0-rmse:0.13629 validation_1-rmse:0.11165 +[75] validation_0-rmse:0.13595 validation_1-rmse:0.11109 +[76] validation_0-rmse:0.13585 validation_1-rmse:0.11023 +[77] validation_0-rmse:0.13541 validation_1-rmse:0.10972 +[78] validation_0-rmse:0.13505 validation_1-rmse:0.10920 +[79] validation_0-rmse:0.13465 validation_1-rmse:0.10861 +[80] validation_0-rmse:0.13433 validation_1-rmse:0.10810 +[81] validation_0-rmse:0.13409 validation_1-rmse:0.10744 +[82] validation_0-rmse:0.13377 validation_1-rmse:0.10695 +[83] validation_0-rmse:0.13353 validation_1-rmse:0.10641 +[84] validation_0-rmse:0.13337 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.13329 validation_1-rmse:0.10533 +[86] validation_0-rmse:0.13296 validation_1-rmse:0.10488 +[87] validation_0-rmse:0.13264 validation_1-rmse:0.10442 +[88] validation_0-rmse:0.13247 validation_1-rmse:0.10394 +[89] validation_0-rmse:0.13216 validation_1-rmse:0.10351 +[90] validation_0-rmse:0.13188 validation_1-rmse:0.10297 +[91] validation_0-rmse:0.13145 validation_1-rmse:0.10203 +[92] validation_0-rmse:0.13122 validation_1-rmse:0.10157 +[93] validation_0-rmse:0.13102 validation_1-rmse:0.10118 +[94] validation_0-rmse:0.13060 validation_1-rmse:0.10033 +[95] validation_0-rmse:0.13033 validation_1-rmse:0.09981 +[96] validation_0-rmse:0.13016 validation_1-rmse:0.09933 +[97] validation_0-rmse:0.12995 validation_1-rmse:0.09894 +[98] validation_0-rmse:0.12972 validation_1-rmse:0.09860 +[99] validation_0-rmse:0.12954 validation_1-rmse:0.09825 +2025-04-29 01:57:23,725 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:23,726 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:24,305 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:57:24,305 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather +2025-04-29 01:57:24,318 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,325 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:24,327 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:24,337 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,345 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:57:24,346 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.729824 +1 0.003044 49.729824 +2 0.000465 49.729824 +3 -0.000380 49.729824 +4 0.002829 49.729824 +2025-04-29 01:57:24,352 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 +2025-04-29 01:57:24,353 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:24,376 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:24,376 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:57:29,392 - datasieve.pipeline - INFO - DI tossed 1948 predictions for being too far from training data. +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30616 validation_1-rmse:0.27906 +[1] validation_0-rmse:0.30021 validation_1-rmse:0.27322 +[2] validation_0-rmse:0.29443 validation_1-rmse:0.26757 +[3] validation_0-rmse:0.28911 validation_1-rmse:0.26205 +[4] validation_0-rmse:0.28365 validation_1-rmse:0.25699 +[5] validation_0-rmse:0.27823 validation_1-rmse:0.25219 +[6] validation_0-rmse:0.27295 validation_1-rmse:0.24748 +[7] validation_0-rmse:0.26797 validation_1-rmse:0.24295 +[8] validation_0-rmse:0.26320 validation_1-rmse:0.23854 +[9] validation_0-rmse:0.25898 validation_1-rmse:0.23437 +[10] validation_0-rmse:0.25517 validation_1-rmse:0.23021 +[11] validation_0-rmse:0.25113 validation_1-rmse:0.22639 +[12] validation_0-rmse:0.24762 validation_1-rmse:0.22270 +[13] validation_0-rmse:0.24393 validation_1-rmse:0.21915 +[14] validation_0-rmse:0.24169 validation_1-rmse:0.21579 +[15] validation_0-rmse:0.23898 validation_1-rmse:0.21236 +[16] validation_0-rmse:0.23539 validation_1-rmse:0.20924 +[17] validation_0-rmse:0.23364 validation_1-rmse:0.20621 +[18] validation_0-rmse:0.23062 validation_1-rmse:0.20322 +[19] validation_0-rmse:0.22764 validation_1-rmse:0.20024 +[20] validation_0-rmse:0.22488 validation_1-rmse:0.19731 +[21] validation_0-rmse:0.22211 validation_1-rmse:0.19445 +[22] validation_0-rmse:0.21952 validation_1-rmse:0.19188 +[23] validation_0-rmse:0.21699 validation_1-rmse:0.18935 +[24] validation_0-rmse:0.21549 validation_1-rmse:0.18686 +[25] validation_0-rmse:0.21310 validation_1-rmse:0.18454 +[26] validation_0-rmse:0.21118 validation_1-rmse:0.18198 +[27] validation_0-rmse:0.20904 validation_1-rmse:0.17979 +[28] validation_0-rmse:0.20726 validation_1-rmse:0.17755 +[29] validation_0-rmse:0.20511 validation_1-rmse:0.17547 +[30] validation_0-rmse:0.20336 validation_1-rmse:0.17335 +[31] validation_0-rmse:0.20172 validation_1-rmse:0.17144 +[32] validation_0-rmse:0.19983 validation_1-rmse:0.16961 +[33] validation_0-rmse:0.19794 validation_1-rmse:0.16759 +[34] validation_0-rmse:0.19658 validation_1-rmse:0.16581 +[35] validation_0-rmse:0.19492 validation_1-rmse:0.16409 +[36] validation_0-rmse:0.19347 validation_1-rmse:0.16229 +[37] validation_0-rmse:0.19225 validation_1-rmse:0.16064 +[38] validation_0-rmse:0.19083 validation_1-rmse:0.15877 +[39] validation_0-rmse:0.18921 validation_1-rmse:0.15720 +[40] validation_0-rmse:0.18766 validation_1-rmse:0.15572 +[41] validation_0-rmse:0.18652 validation_1-rmse:0.15414 +[42] validation_0-rmse:0.18519 validation_1-rmse:0.15277 +[43] validation_0-rmse:0.18396 validation_1-rmse:0.15125 +[44] validation_0-rmse:0.18264 validation_1-rmse:0.14968 +[45] validation_0-rmse:0.18134 validation_1-rmse:0.14841 +[46] validation_0-rmse:0.18026 validation_1-rmse:0.14717 +[47] validation_0-rmse:0.17900 validation_1-rmse:0.14594 +[48] validation_0-rmse:0.17815 validation_1-rmse:0.14460 +[49] validation_0-rmse:0.17713 validation_1-rmse:0.14344 +[50] validation_0-rmse:0.17609 validation_1-rmse:0.14232 +[51] validation_0-rmse:0.17502 validation_1-rmse:0.14112 +[52] validation_0-rmse:0.17414 validation_1-rmse:0.13991 +[53] validation_0-rmse:0.17317 validation_1-rmse:0.13889 +[54] validation_0-rmse:0.17267 validation_1-rmse:0.13770 +[55] validation_0-rmse:0.17175 validation_1-rmse:0.13665 +[56] validation_0-rmse:0.17087 validation_1-rmse:0.13573 +[57] validation_0-rmse:0.17001 validation_1-rmse:0.13483 +[58] validation_0-rmse:0.16920 validation_1-rmse:0.13384 +[59] validation_0-rmse:0.16869 validation_1-rmse:0.13280 +[60] validation_0-rmse:0.16790 validation_1-rmse:0.13189 +[61] validation_0-rmse:0.16689 validation_1-rmse:0.13093 +[62] validation_0-rmse:0.16600 validation_1-rmse:0.13007 +[63] validation_0-rmse:0.16548 validation_1-rmse:0.12921 +[64] validation_0-rmse:0.16482 validation_1-rmse:0.12837 +[65] validation_0-rmse:0.16397 validation_1-rmse:0.12747 +[66] validation_0-rmse:0.16316 validation_1-rmse:0.12669 +[67] validation_0-rmse:0.16267 validation_1-rmse:0.12587 +[68] validation_0-rmse:0.16204 validation_1-rmse:0.12501 +[69] validation_0-rmse:0.16159 validation_1-rmse:0.12422 +[70] validation_0-rmse:0.16090 validation_1-rmse:0.12354 +[71] validation_0-rmse:0.16026 validation_1-rmse:0.12282 +[72] validation_0-rmse:0.15986 validation_1-rmse:0.12206 +[73] validation_0-rmse:0.15919 validation_1-rmse:0.12129 +[74] validation_0-rmse:0.15875 validation_1-rmse:0.12061 +[75] validation_0-rmse:0.15829 validation_1-rmse:0.11966 +[76] validation_0-rmse:0.15790 validation_1-rmse:0.11864 +[77] validation_0-rmse:0.15732 validation_1-rmse:0.11802 +[78] validation_0-rmse:0.15696 validation_1-rmse:0.11739 +[79] validation_0-rmse:0.15615 validation_1-rmse:0.11660 +[80] validation_0-rmse:0.15556 validation_1-rmse:0.11593 +[81] validation_0-rmse:0.15516 validation_1-rmse:0.11531 +[82] validation_0-rmse:0.15466 validation_1-rmse:0.11437 +[83] validation_0-rmse:0.15422 validation_1-rmse:0.11383 +[84] validation_0-rmse:0.15382 validation_1-rmse:0.11332 +[85] validation_0-rmse:0.15350 validation_1-rmse:0.11244 +[86] validation_0-rmse:0.15310 validation_1-rmse:0.11180 +[87] validation_0-rmse:0.15277 validation_1-rmse:0.11119 +[88] validation_0-rmse:0.15228 validation_1-rmse:0.11060 +[89] validation_0-rmse:0.15192 validation_1-rmse:0.11011 +[90] validation_0-rmse:0.15144 validation_1-rmse:0.10956 +[91] validation_0-rmse:0.15092 validation_1-rmse:0.10913 +[92] validation_0-rmse:0.15058 validation_1-rmse:0.10847 +[93] validation_0-rmse:0.15017 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.14984 validation_1-rmse:0.10702 +[95] validation_0-rmse:0.14967 validation_1-rmse:0.10629 +[96] validation_0-rmse:0.14914 validation_1-rmse:0.10587 +[97] validation_0-rmse:0.14882 validation_1-rmse:0.10545 +[98] validation_0-rmse:0.14853 validation_1-rmse:0.10454 +[99] validation_0-rmse:0.14837 validation_1-rmse:0.10398 +2025-04-29 01:57:30,474 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.12 secs) -------------------- +2025-04-29 01:57:30,475 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:31,077 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:57:31,096 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +0 33825 +1 32946 +2025-04-29 01:57:31,097 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 36730 +1.0 30041 +2025-04-29 01:57:31,105 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-01-01 00:00:00 up to 2025-04-20 00:00:00 (109 days). +2025-04-29 01:57:31,109 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,111 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:31,193 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,195 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:33,776 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_01-57-33.meta.json" +Result for strategy FreqaiExampleStrategy + BACKTESTING REPORT +┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ BTC/USDT │ 38 │ -0.39 │ -22.029 │ -2.2 │ 22:13:00 │ 5 32 1 13.2 │ +│ SOL/USDT │ 44 │ -1.94 │ -128.236 │ -12.82 │ 16:35:00 │ 12 26 6 27.3 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + LEFT OPEN TRADES REPORT +┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │ +└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + ENTER TAG STATS +┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ long │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + EXIT REASON STATS +┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ roi │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ trailing_stop_loss │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + MIXED TAG STATS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ('long', 'roi') │ │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ ('long', 'trailing_stop_loss') │ │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + SUMMARY METRICS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Metric ┃ Value ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ Backtesting from │ 2025-01-01 00:00:00 │ +│ Backtesting to │ 2025-04-20 00:00:00 │ +│ Trading Mode │ Spot │ +│ Max open trades │ 2 │ +│ │ │ +│ Total/Daily Avg Trades │ 82 / 0.75 │ +│ Starting balance │ 1000 USDT │ +│ Final balance │ 849.735 USDT │ +│ Absolute profit │ -150.265 USDT │ +│ Total profit % │ -15.03% │ +│ CAGR % │ -42.03% │ +│ Sortino │ -252.56 │ +│ Sharpe │ -4.15 │ +│ Calmar │ -17.48 │ +│ SQN │ -2.60 │ +│ Profit factor │ 0.05 │ +│ Expectancy (Ratio) │ -1.83 (-0.79) │ +│ Avg. daily profit % │ -0.14% │ +│ Avg. stake amount │ 150 USDT │ +│ Total trade volume │ 24523.15 USDT │ +│ │ │ +│ Best Pair │ BTC/USDT -2.20% │ +│ Worst Pair │ SOL/USDT -12.82% │ +│ Best trade │ SOL/USDT 0.90% │ +│ Worst trade │ SOL/USDT -15.19% │ +│ Best day │ 1.76 USDT │ +│ Worst day │ -22.827 USDT │ +│ Days win/draw/lose │ 14 / 80 / 7 │ +│ Avg. Duration Winners │ 0:55:00 │ +│ Avg. Duration Loser │ 2 days, 18:13:00 │ +│ Max Consecutive Wins / Loss │ 2 / 16 │ +│ Rejected Entry signals │ 0 │ +│ Entry/Exit Timeouts │ 0 / 0 │ +│ │ │ +│ Min balance │ 849.735 USDT │ +│ Max balance │ 1000.508 USDT │ +│ Max % of account underwater │ 15.07% │ +│ Absolute Drawdown (Account) │ 15.07% │ +│ Absolute Drawdown │ 150.773 USDT │ +│ Drawdown high │ 0.508 USDT │ +│ Drawdown low │ -150.265 USDT │ +│ Drawdown Start │ 2025-01-06 19:48:00 │ +│ Drawdown End │ 2025-04-06 23:15:00 │ +│ Market change │ -26.79% │ +└─────────────────────────────┴─────────────────────┘ + +Backtested 2025-01-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2 + STRATEGY SUMMARY +┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ +│ FreqaiExampleStrategy │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ 150.773 USDT 15.07% │ +└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴──────────────────────┘ diff --git a/freqtrade/abc.py b/freqtrade/abc.py new file mode 100644 index 00000000..a1d8d2c5 --- /dev/null +++ b/freqtrade/abc.py @@ -0,0 +1,336 @@ +import logging +import numpy as np +from functools import reduce +import talib.abstract as ta +from pandas import DataFrame +from technical import qtpylib +from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter + +logger = logging.getLogger(__name__) + +class FreqaiExampleStrategy(IStrategy): + # 移除硬编码的 minimal_roi 和 stoploss，改为动态适配 + minimal_roi = {} # 将在 populate_indicators 中动态生成 + stoploss = 0.0 # 将在 populate_indicators 中动态设置 + trailing_stop = True + process_only_new_candles = True + use_exit_signal = True + startup_candle_count: int = 40 + can_short = False + + # 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化 + buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True) + sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True) + + # 为 Hyperopt 优化添加 ROI 和 stoploss 参数 + roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True) + roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True) + roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True) + stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True) + + # FreqAI 配置 + freqai_info = { + "model": "CatboostClassifier", # 与config保持一致 + "feature_parameters": { + "include_timeframes": ["3m", "15m", "1h"], # 与config一致 + "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对 + "label_period_candles": 20, # 与config一致 + "include_shifted_candles": 2, # 与config一致 + }, + "data_split_parameters": { + "test_size": 0.2, + "shuffle": True, # 启用shuffle + }, + "model_training_parameters": { + "n_estimators": 100, # 减少树的数量 + "learning_rate": 0.1, # 提高学习率 + "max_depth": 6, # 限制树深度 + "subsample": 0.8, # 添加子采样 + "colsample_bytree": 0.8, # 添加特征采样 + "objective": "reg:squarederror", + "eval_metric": "rmse", + "early_stopping_rounds": 20, + "verbose": 0, + }, + "data_kitchen": { + "feature_parameters": { + "DI_threshold": 1.5, # 降低异常值过滤阈值 + "use_DBSCAN_to_remove_outliers": False # 禁用DBSCAN + } + } + } + + plot_config = { + "main_plot": {}, + "subplots": { + "&-buy_rsi": {"&-buy_rsi": {"color": "green"}}, + "&-sell_rsi": {"&-sell_rsi": {"color": "red"}}, + "&-stoploss": {"&-stoploss": {"color": "purple"}}, + "&-roi_0": {"&-roi_0": {"color": "orange"}}, + "do_predict": {"do_predict": {"color": "brown"}}, + }, + } + + def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame: + # 保留关键的技术指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 确保 MACD 列被正确计算并保留 + try: + macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe["macd"] = macd["macd"] + dataframe["macdsignal"] = macd["macdsignal"] + except Exception as e: + logger.error(f"计算 MACD 列时出错：{str(e)}") + dataframe["macd"] = np.nan + dataframe["macdsignal"] = np.nan + + # 检查 MACD 列是否存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 确保 MACD 列存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 保留布林带相关特征 + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + + # 保留成交量相关特征 + dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() + + # 数据清理 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill().fillna(0) + + logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}") + return dataframe + + def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-pct-change"] = dataframe["close"].pct_change() + dataframe["%-raw_volume"] = dataframe["volume"] + dataframe["%-raw_price"] = dataframe["close"] +# 数据清理逻辑 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0) + dataframe[col] = dataframe[col].ffill() + dataframe[col] = dataframe[col].fillna(0) + + # 检查是否仍有无效值 + if dataframe[col].isna().any() or np.isinf(dataframe[col]).any(): + logger.warning(f"列 {col} 仍包含无效值，已填充为默认值") + dataframe[col] = dataframe[col].fillna(0) + return dataframe + + def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek + dataframe["%-hour_of_day"] = dataframe["date"].dt.hour + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + return dataframe + + def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}") + if "close" not in dataframe.columns: + logger.error("数据框缺少必要的 'close' 列") + raise ValueError("数据框缺少必要的 'close' 列") + + try: + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + + # 定义目标变量为未来价格变化百分比（连续值） + dataframe["up_or_down"] = ( + dataframe["close"].shift(-label_period) - dataframe["close"] + ) / dataframe["close"] + + # 数据清理：处理 NaN 和 Inf 值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 确保目标变量是二维数组 + if dataframe["up_or_down"].ndim == 1: + dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1) + + # 检查并处理 NaN 或无限值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 生成 %-volatility 特征 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + + # 确保 &-buy_rsi 列的值计算正确 + dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 数据清理 + for col in ["&-buy_rsi", "up_or_down", "%-volatility"]: + # 使用直接操作避免链式赋值 + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill') + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值 + if dataframe[col].isna().any(): + logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值") + + except Exception as e: + logger.error(f"创建 FreqAI 目标失败：{str(e)}") + raise + + # Log the shape of the target variable for debugging + logger.info(f"目标列形状：{dataframe['up_or_down'].shape}") + logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}") + return dataframe + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + logger.info(f"处理交易对：{metadata['pair']}") + dataframe = self.freqai.start(dataframe, metadata, self) + + # 计算传统指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) + + # 生成 up_or_down 信号（非 FreqAI 目标） + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + # 使用未来价格变化方向生成 up_or_down 信号 + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + dataframe["up_or_down"] = np.where( + dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0 + ) + + # 动态设置参数 + if "&-buy_rsi" in dataframe.columns: + # 派生其他目标 + dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + # Ensure proper calculation and handle potential NaN values + dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1) + dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0) + + # Additional check to ensure no NaN values remain + for col in ["&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 仍包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0) + + # 简化动态参数生成逻辑 + # 放松 buy_rsi 和 sell_rsi 的生成逻辑 + # 计算 buy_rsi_pred 并清理 NaN 值 + dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50) + dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median()) + + # 计算 sell_rsi_pred 并清理 NaN 值 + dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20 + dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median()) + + # 计算 stoploss_pred 并清理 NaN 值 + dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25) + dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean()) + + # 计算 roi_0_pred 并清理 NaN 值 + dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2) + dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean()) + + # 检查预测值 + for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) + + # 更保守的止损和止盈设置 + dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2) + dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3) + + # 设置策略级参数 + self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1]) + self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1]) +# 更保守的止损设置 + self.stoploss = -0.15 # 固定止损 15% + self.minimal_roi = { + 0: float(self.roi_0.value), + 15: float(self.roi_15.value), + 30: float(self.roi_30.value), + 60: 0 + } +# 更保守的追踪止损设置 + self.trailing_stop_positive = 0.05 # 追踪止损触发点 + self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量 + + logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, " + f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}") + + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + + logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}") + logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}") + + return dataframe + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: +# 改进卖出信号条件 + exit_long_conditions = [ + (df["rsi"] > df["sell_rsi_pred"]), # RSI 高于卖出阈值 + (df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值 + (df["close"] < df["bb_middleband"]) # 价格低于布林带中轨 + ] + if exit_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, exit_long_conditions), + "exit_long" + ] = 1 + return df + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + # 改进买入信号条件 + # 检查 MACD 列是否存在 + if "macd" not in df.columns or "macdsignal" not in df.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。") + + try: + macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9) + df["macd"] = macd["macd"] + df["macdsignal"] = macd["macdsignal"] + logger.info("MACD 列已成功重新计算。") + except Exception as e: + logger.error(f"重新计算 MACD 列时出错：{str(e)}") + raise ValueError("DataFrame 缺少必要的 MACD 列且无法重新计算。") + + enter_long_conditions = [ + (df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值 + (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20% + (df["close"] > df["bb_middleband"]) # 价格高于布林带中轨 + ] + + # 如果 MACD 列存在，则添加 MACD 金叉条件 + if "macd" in df.columns and "macdsignal" in df.columns: + enter_long_conditions.append((df["macd"] > df["macdsignal"])) + + # 确保模型预测为买入 + enter_long_conditions.append((df["do_predict"] == 1)) + if enter_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_long_conditions), + ["enter_long", "enter_tag"] + ] = (1, "long") + return df + def confirm_trade_entry( + self, pair: str, order_type: str, amount: float, rate: float, + time_in_force: str, current_time, entry_tag, side: str, **kwargs + ) -> bool: + df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = df.iloc[-1].squeeze() + if side == "long": + if rate > (last_candle["close"] * (1 + 0.0025)): + return False + return True diff --git a/freqtrade/new.py b/freqtrade/new.py new file mode 100644 index 00000000..6c92c207 --- /dev/null +++ b/freqtrade/new.py @@ -0,0 +1,295 @@ +import logging +import numpy as np +from functools import reduce +import talib.abstract as ta +from pandas import DataFrame +from technical import qtpylib +from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter + +logger = logging.getLogger(__name__) + +class FreqaiExampleStrategy(IStrategy): + # 移除硬编码的 minimal_roi 和 stoploss，改为动态适配 + minimal_roi = {} # 将在 populate_indicators 中动态生成 + stoploss = 0.0 # 将在 populate_indicators 中动态设置 + trailing_stop = True + process_only_new_candles = True + use_exit_signal = True + startup_candle_count: int = 40 + can_short = False + + # 可训练参数（用于 Hyperopt） + buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True) + sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True) + + roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True) + roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True) + roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True) + + stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True) + + # FreqAI 配置 + freqai_info = { + "model": "CatboostClassifier", + "feature_parameters": { + "include_timeframes": ["3m", "15m", "1h"], # 与config一致 + "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对 + "label_period_candles": 20, # 与config一致 + "include_shifted_candles": 2, # 与config一致 + }, + "data_split_parameters": { + "test_size": 0.2, + "shuffle": True, + }, + "model_training_parameters": { + "n_estimators": 100, # 减少树的数量 + "learning_rate": 0.1, # 提高学习率 + "max_depth": 6, # 限制树深度 + "subsample": 0.8, # 添加子采样 + "colsample_bytree": 0.8, # 添加特征采样 + "objective": "reg:squarederror", + "eval_metric": "rmse", + "early_stopping_rounds": 20, + "verbose": 0, + }, + "data_kitchen": { + "feature_parameters": { + "DI_threshold": 1.5, + "use_DBSCAN_to_remove_outliers": False + } + } + } + + plot_config = { + "main_plot": {}, + "subplots": { + "&-buy_rsi": {"&-buy_rsi": {"color": "green"}}, + "&-sell_rsi": {"&-sell_rsi": {"color": "red"}}, + "&-stoploss": {"&-stoploss": {"color": "purple"}}, + "&-roi_0": {"&-roi_0": {"color": "orange"}}, + "do_predict": {"do_predict": {"color": "brown"}}, + }, + } + + def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame: + # RSI 计算 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + + # MACD 计算并容错 + try: + macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe["macd"] = macd["macd"] + dataframe["macdsignal"] = macd["macdsignal"] + except Exception as e: + logger.error(f"MACD 计算失败: {e}") + dataframe["macd"] = np.nan + dataframe["macdsignal"] = np.nan + + # 检查 MACD 列是否存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 确保 MACD 列存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 保留布林带相关特征 + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + + # 成交量均线 + dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() + + # 清理无穷大值 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill().fillna(0) + + return dataframe + + def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-pct-change"] = dataframe["close"].pct_change() + dataframe["%-raw_volume"] = dataframe["volume"] + dataframe["%-raw_price"] = dataframe["close"] + +# 数据清理逻辑 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0) + dataframe[col] = dataframe[col].ffill() + dataframe[col] = dataframe[col].fillna(0) + + # 检查是否仍有无效值 + if dataframe[col].isna().any() or np.isinf(dataframe[col]).any(): + logger.warning(f"列 {col} 仍包含无效值，已填充为默认值") + dataframe[col] = dataframe[col].fillna(0) + return dataframe + + def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek + dataframe["%-hour_of_day"] = dataframe["date"].dt.hour + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + return dataframe + + def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}") + if "close" not in dataframe.columns: + logger.error("数据框缺少必要的 'close' 列") + raise ValueError("数据框缺少必要的 'close' 列") + + try: + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + + # 定义目标变量为未来价格变化百分比（连续值） + dataframe["up_or_down"] = ( + dataframe["close"].shift(-label_period) - dataframe["close"] + ) / dataframe["close"] + + # 数据清理：处理 NaN 和 Inf 值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 确保目标变量是二维数组 + if dataframe["up_or_down"].ndim == 1: + dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1) + + # 检查并处理 NaN 或无限值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 生成 %-volatility 特征 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + + # 确保 &-buy_rsi 列的值计算正确 + dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 数据清理 + for col in ["&-buy_rsi", "up_or_down", "%-volatility"]: + # 使用直接操作避免链式赋值 + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill') + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值 + if dataframe[col].isna().any(): + logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值") + + except Exception as e: + logger.error(f"创建 FreqAI 目标失败：{str(e)}") + raise + + return dataframe + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + logger.info(f"处理交易对：{metadata['pair']}") + dataframe = self.freqai.start(dataframe, metadata, self) + + # 计算传统指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) + + # 生成 up_or_down 信号（非 FreqAI 目标） + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + # 使用未来价格变化方向生成 up_or_down 信号 + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + dataframe["up_or_down"] = np.where( + dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0 + ) + + # 动态设置参数 + if "&-buy_rsi" in dataframe.columns: + # 派生其他目标 + dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + # Ensure proper calculation and handle potential NaN values + dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1) + dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0) + + # Additional check to ensure no NaN values remain + for col in ["&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 仍包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0) + + # 简化动态参数生成逻辑 + # 放松 buy_rsi 和 sell_rsi 的生成逻辑 + # 计算 buy_rsi_pred 并清理 NaN 值 + dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50) + dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median()) + + # 计算 sell_rsi_pred 并清理 NaN 值 + dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20 + dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median()) + + # 计算 stoploss_pred 并清理 NaN 值 + dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25) + dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean()) + + # 计算 roi_0_pred 并清理 NaN 值 + dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2) + dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean()) + + # 检查预测值 + for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) + + # 更保守的止损和止盈设置 + dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2) + dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3) + + # 设置策略级参数 + self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1]) + self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1]) +# 更保守的止损设置 + self.stoploss = -0.15 # 固定止损 15% + self.minimal_roi = { + 0: float(self.roi_0.value), + 15: float(self.roi_15.value), + 30: float(self.roi_30.value), + 60: 0 + } +# 更保守的追踪止损设置 + self.trailing_stop_positive = 0.05 # 追踪止损触发点 + self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量 + + logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, " + f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}") + + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + + logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}") + logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}") + + return dataframe + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: +# 改进卖出信号条件 + exit_long_conditions = [ + (df["rsi"] > df["sell_rsi_pred"]), # RSI 高于卖出阈值 + (df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值 + (df["close"] < df["bb_middleband"]) # 价格低于布林带中轨 + df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 + return df + + def confirm_trade_entry( + self, pair: str, order_type: str, amount: float, rate: float, + time_in_force: str, current_time, entry_tag, side: str, **kwargs + ) -> bool: + df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = df.iloc[-1] + if side == "long": + if rate > (last_candle["close"] * 1.0025): # 价格超过最新价 0.25% 则拒绝下单 + return False + return True diff --git a/freqtrade/old.py b/freqtrade/old.py new file mode 100644 index 00000000..a1d8d2c5 --- /dev/null +++ b/freqtrade/old.py @@ -0,0 +1,336 @@ +import logging +import numpy as np +from functools import reduce +import talib.abstract as ta +from pandas import DataFrame +from technical import qtpylib +from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter + +logger = logging.getLogger(__name__) + +class FreqaiExampleStrategy(IStrategy): + # 移除硬编码的 minimal_roi 和 stoploss，改为动态适配 + minimal_roi = {} # 将在 populate_indicators 中动态生成 + stoploss = 0.0 # 将在 populate_indicators 中动态设置 + trailing_stop = True + process_only_new_candles = True + use_exit_signal = True + startup_candle_count: int = 40 + can_short = False + + # 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化 + buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True) + sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True) + + # 为 Hyperopt 优化添加 ROI 和 stoploss 参数 + roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True) + roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True) + roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True) + stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True) + + # FreqAI 配置 + freqai_info = { + "model": "CatboostClassifier", # 与config保持一致 + "feature_parameters": { + "include_timeframes": ["3m", "15m", "1h"], # 与config一致 + "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对 + "label_period_candles": 20, # 与config一致 + "include_shifted_candles": 2, # 与config一致 + }, + "data_split_parameters": { + "test_size": 0.2, + "shuffle": True, # 启用shuffle + }, + "model_training_parameters": { + "n_estimators": 100, # 减少树的数量 + "learning_rate": 0.1, # 提高学习率 + "max_depth": 6, # 限制树深度 + "subsample": 0.8, # 添加子采样 + "colsample_bytree": 0.8, # 添加特征采样 + "objective": "reg:squarederror", + "eval_metric": "rmse", + "early_stopping_rounds": 20, + "verbose": 0, + }, + "data_kitchen": { + "feature_parameters": { + "DI_threshold": 1.5, # 降低异常值过滤阈值 + "use_DBSCAN_to_remove_outliers": False # 禁用DBSCAN + } + } + } + + plot_config = { + "main_plot": {}, + "subplots": { + "&-buy_rsi": {"&-buy_rsi": {"color": "green"}}, + "&-sell_rsi": {"&-sell_rsi": {"color": "red"}}, + "&-stoploss": {"&-stoploss": {"color": "purple"}}, + "&-roi_0": {"&-roi_0": {"color": "orange"}}, + "do_predict": {"do_predict": {"color": "brown"}}, + }, + } + + def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame: + # 保留关键的技术指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 确保 MACD 列被正确计算并保留 + try: + macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe["macd"] = macd["macd"] + dataframe["macdsignal"] = macd["macdsignal"] + except Exception as e: + logger.error(f"计算 MACD 列时出错：{str(e)}") + dataframe["macd"] = np.nan + dataframe["macdsignal"] = np.nan + + # 检查 MACD 列是否存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 确保 MACD 列存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 保留布林带相关特征 + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + + # 保留成交量相关特征 + dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() + + # 数据清理 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill().fillna(0) + + logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}") + return dataframe + + def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-pct-change"] = dataframe["close"].pct_change() + dataframe["%-raw_volume"] = dataframe["volume"] + dataframe["%-raw_price"] = dataframe["close"] +# 数据清理逻辑 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0) + dataframe[col] = dataframe[col].ffill() + dataframe[col] = dataframe[col].fillna(0) + + # 检查是否仍有无效值 + if dataframe[col].isna().any() or np.isinf(dataframe[col]).any(): + logger.warning(f"列 {col} 仍包含无效值，已填充为默认值") + dataframe[col] = dataframe[col].fillna(0) + return dataframe + + def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek + dataframe["%-hour_of_day"] = dataframe["date"].dt.hour + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + return dataframe + + def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}") + if "close" not in dataframe.columns: + logger.error("数据框缺少必要的 'close' 列") + raise ValueError("数据框缺少必要的 'close' 列") + + try: + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + + # 定义目标变量为未来价格变化百分比（连续值） + dataframe["up_or_down"] = ( + dataframe["close"].shift(-label_period) - dataframe["close"] + ) / dataframe["close"] + + # 数据清理：处理 NaN 和 Inf 值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 确保目标变量是二维数组 + if dataframe["up_or_down"].ndim == 1: + dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1) + + # 检查并处理 NaN 或无限值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 生成 %-volatility 特征 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + + # 确保 &-buy_rsi 列的值计算正确 + dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 数据清理 + for col in ["&-buy_rsi", "up_or_down", "%-volatility"]: + # 使用直接操作避免链式赋值 + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill') + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值 + if dataframe[col].isna().any(): + logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值") + + except Exception as e: + logger.error(f"创建 FreqAI 目标失败：{str(e)}") + raise + + # Log the shape of the target variable for debugging + logger.info(f"目标列形状：{dataframe['up_or_down'].shape}") + logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}") + return dataframe + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + logger.info(f"处理交易对：{metadata['pair']}") + dataframe = self.freqai.start(dataframe, metadata, self) + + # 计算传统指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) + + # 生成 up_or_down 信号（非 FreqAI 目标） + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + # 使用未来价格变化方向生成 up_or_down 信号 + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + dataframe["up_or_down"] = np.where( + dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0 + ) + + # 动态设置参数 + if "&-buy_rsi" in dataframe.columns: + # 派生其他目标 + dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + # Ensure proper calculation and handle potential NaN values + dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1) + dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0) + + # Additional check to ensure no NaN values remain + for col in ["&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 仍包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0) + + # 简化动态参数生成逻辑 + # 放松 buy_rsi 和 sell_rsi 的生成逻辑 + # 计算 buy_rsi_pred 并清理 NaN 值 + dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50) + dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median()) + + # 计算 sell_rsi_pred 并清理 NaN 值 + dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20 + dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median()) + + # 计算 stoploss_pred 并清理 NaN 值 + dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25) + dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean()) + + # 计算 roi_0_pred 并清理 NaN 值 + dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2) + dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean()) + + # 检查预测值 + for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) + + # 更保守的止损和止盈设置 + dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2) + dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3) + + # 设置策略级参数 + self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1]) + self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1]) +# 更保守的止损设置 + self.stoploss = -0.15 # 固定止损 15% + self.minimal_roi = { + 0: float(self.roi_0.value), + 15: float(self.roi_15.value), + 30: float(self.roi_30.value), + 60: 0 + } +# 更保守的追踪止损设置 + self.trailing_stop_positive = 0.05 # 追踪止损触发点 + self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量 + + logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, " + f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}") + + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + + logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}") + logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}") + + return dataframe + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: +# 改进卖出信号条件 + exit_long_conditions = [ + (df["rsi"] > df["sell_rsi_pred"]), # RSI 高于卖出阈值 + (df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值 + (df["close"] < df["bb_middleband"]) # 价格低于布林带中轨 + ] + if exit_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, exit_long_conditions), + "exit_long" + ] = 1 + return df + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + # 改进买入信号条件 + # 检查 MACD 列是否存在 + if "macd" not in df.columns or "macdsignal" not in df.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。") + + try: + macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9) + df["macd"] = macd["macd"] + df["macdsignal"] = macd["macdsignal"] + logger.info("MACD 列已成功重新计算。") + except Exception as e: + logger.error(f"重新计算 MACD 列时出错：{str(e)}") + raise ValueError("DataFrame 缺少必要的 MACD 列且无法重新计算。") + + enter_long_conditions = [ + (df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值 + (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20% + (df["close"] > df["bb_middleband"]) # 价格高于布林带中轨 + ] + + # 如果 MACD 列存在，则添加 MACD 金叉条件 + if "macd" in df.columns and "macdsignal" in df.columns: + enter_long_conditions.append((df["macd"] > df["macdsignal"])) + + # 确保模型预测为买入 + enter_long_conditions.append((df["do_predict"] == 1)) + if enter_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_long_conditions), + ["enter_long", "enter_tag"] + ] = (1, "long") + return df + def confirm_trade_entry( + self, pair: str, order_type: str, amount: float, rate: float, + time_in_force: str, current_time, entry_tag, side: str, **kwargs + ) -> bool: + df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = df.iloc[-1].squeeze() + if side == "long": + if rate > (last_candle["close"] * (1 + 0.0025)): + return False + return True diff --git a/freqtrade/templates/MyDynamicStrategy.py b/freqtrade/templates/MyDynamicStrategy.py new file mode 100644 index 00000000..2b99187a --- /dev/null +++ b/freqtrade/templates/MyDynamicStrategy.py @@ -0,0 +1,185 @@ +from freqtrade.strategy import IStrategy +import talib.abstract as ta +import pandas as pd +import numpy as np +import logging +from technical import qtpylib +from functools import reduce +from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter + +class MyDynamicStrategy(IStrategy): + + # --- 参数空间 --- + buy_rsi = IntParameter(10, 50, default=30) + sell_rsi = IntParameter(50, 90, default=70) + roi_0 = DecimalParameter(0.01, 0.2, default=0.03) + roi_1 = DecimalParameter(0.005, 0.1, default=0.015) + stoploss_param = DecimalParameter(-0.3, -0.1, default=-0.15) + + trailing_stop = True + trailing_stop_positive = 0.05 + trailing_stop_positive_offset = 0.1 + can_short = False + + process_only_new_candles = True + use_exit_signal = True + stoploss = -0.15 + minimal_roi = {"0": 0.03, "30": 0.01, "60": 0} + + # --- Plotting config --- + plot_config = { + "main_plot": {}, + "subplots": { + "RSI Buy Threshold": { + "&-buy_rsi": {"color": "green"} + }, + "ROI and Stoploss": { + "&-roi_0": {"color": "orange"}, + "&-stoploss": {"color": "red"} + } + } + } + + # --- FreqAI 配置 --- + freqai_info = { + "model": "CatboostClassifier", + "feature_parameters": { + "include_timeframes": ["5m", "1h"], + "indicator_periods_candles": [10, 20, 50], + "include_corr_pairlist": ["BTC/USDT"], + "target_classifier": "value", + "label_period_candles": 20, + }, + "training_settings": { + "train_period_days": 30, + "startup_candle_count": 200 + } + } + + def feature_engineering_expand_all(self, dataframe, period, **kwargs): + df = dataframe.copy() + df[f'rsi_{period}'] = ta.RSI(df, timeperiod=period) + df[f'sma_diff_{period}'] = df['close'] - ta.SMA(df, timeperiod=period) + df[f'macd_{period}'], _, _ = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9) + df[f'stoch_rsi_{period}'] = ta.STOCHRSI(df, timeperiod=period) + df[f'cci_{period}'] = ta.CCI(df, timeperiod=period) + df[f'willr_{period}'] = ta.WILLR(df, timeperiod=period) + df[f'atr_{period}'] = ta.ATR(df, timeperiod=period) + df[f'price_change_rate_{period}'] = df['close'].pct_change(period) + df[f'volatility_{period}'] = df['close'].pct_change().rolling(window=period).std() + return df + + def set_freqai_targets(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + # 使用短期和长期均线交叉作为目标标签 + short_ma = ta.SMA(dataframe, timeperiod=10) + long_ma = ta.SMA(dataframe, timeperiod=50) + dataframe['target'] = np.where(short_ma > long_ma, 2, + np.where(short_ma < long_ma, 0, 1)) + return dataframe + + def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + # 示例：使用简单未来N周期涨跌作为目标变量 + # 使用短期均线趋势代替未来价格 + # 计算短期和长期均线 + short_ma = ta.SMA(dataframe, timeperiod=10) + long_ma = ta.SMA(dataframe, timeperiod=50) + dataframe['short_ma'] = short_ma + dataframe['long_ma'] = long_ma + + # 计算 RSI 和其他动态参数 + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + dataframe['&-buy_rsi'] = self.buy_rsi.value + dataframe['&-sell_rsi'] = self.sell_rsi.value + dataframe['&-roi_0'] = self.roi_0.value + dataframe['&-stoploss'] = self.stoploss_param.value + + # 添加调试日志 + logging.info(f"Feature columns after feature engineering: {list(dataframe.columns)}") + + # 使用短期和长期均线交叉作为目标标签 + dataframe['target'] = np.where(short_ma > long_ma, 2, + np.where(short_ma < long_ma, 1, 0)) + + # 动态设置 minimal_roi + # 平滑处理 ROI 参数 + # 基于波动率动态调整 ROI 参数 + # 使用指数加权移动平均 (EWMA) 计算波动率 + volatility = dataframe['close'].pct_change().ewm(span=20, adjust=False).std().mean() + roi_0_dynamic = max(0.01, min(0.2, self.roi_0.value * (1 + volatility))) + roi_1_dynamic = max(0.005, min(0.1, self.roi_1.value * (1 + volatility))) + self.minimal_roi = { + 0: roi_0_dynamic, + 30: roi_1_dynamic, + 60: 0 + } + # 动态调整止损距离 + volatility_multiplier = max(1.5, min(3.0, 2.0 + volatility)) # 波动率倍数 + self.stoploss = -0.15 * volatility_multiplier + + # 计算 Bollinger Bands 并添加到 dataframe + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe['bollinger_upper'] = bollinger['upper'] + dataframe['bollinger_mid'] = bollinger['mid'] + dataframe['bollinger_lower'] = bollinger['lower'] + + # 计算 MACD 并添加到 dataframe + macd, macdsignal, _ = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe['macd'] = macd + dataframe['macdsignal'] = macdsignal + + # 添加调试日志 + logging.info(f"RSI condition: {(dataframe['rsi'] < dataframe['&-buy_rsi']).sum()}") + logging.info(f"Volume condition: {(dataframe['volume'] > dataframe['volume'].rolling(window=20).mean() * 1.05).sum()}") + logging.info(f"MACD condition: {((dataframe['close'] <= dataframe['bollinger_lower'] * 1.01) & (dataframe['macd'] > dataframe['macdsignal'])).sum()}") + + + # 添加 ADX 趋势过滤器 + dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) + is_strong_trend = dataframe['adx'].iloc[-1] > 25 + # MACD 穿越信号条件 + (dataframe["close"] < dataframe['bollinger_lower']) & (dataframe['macd'] > dataframe['macdsignal']), + + # 基于趋势强度动态调整追踪止损 + trend_strength = (dataframe['short_ma'] - dataframe['long_ma']).mean() + if is_strong_trend: + self.trailing_stop_positive = max(0.01, min(0.1, abs(trend_strength) * 0.3)) + self.trailing_stop_positive_offset = max(0.01, min(0.2, abs(trend_strength) * 0.6)) + else: + self.trailing_stop_positive = 0.05 + self.trailing_stop_positive_offset = 0.1 + trend_strength = (dataframe['short_ma'] - dataframe['long_ma']).mean() + self.trailing_stop_positive = max(0.01, min(0.1, abs(trend_strength) * 0.3)) + + return dataframe + def populate_entry_trend(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame: + # 增加成交量过滤和 Bollinger Bands 信号 + # 计算 MACD + macd, macdsignal, _ = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9) + df['macd'] = macd + df['macdsignal'] = macdsignal + + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df), window=20, stds=2) + conditions = [ + (df["rsi"] < df["&-buy_rsi"]), # RSI 低于买入阈值 + (df["volume"] > df["volume"].rolling(window=20).mean() * 1.1), # 成交量增长超过 10% + (df["close"] < df['bollinger_lower']) & (df['macd'] > df['macdsignal']), # MACD 穿越信号 + ] + df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 + return df + + def populate_exit_trend(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame: + # 增加 Bollinger Bands 中轨信号 + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df), window=20, stds=2) + exit_long_conditions = [ + (df["rsi"] > df["&-sell_rsi"]), + (df["close"] > df['bollinger_mid']) # Bollinger Bands 中轨信号 + ] + df.loc[reduce(lambda x, y: x & y, exit_long_conditions), 'exit_long'] = 1 + return df + def confirm_trade_entry(self, pair, order_type, amount, rate, time_in_force, current_time, entry_tag, side, **kwargs): + df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = df.iloc[-1] + if rate > last_candle["close"] * 1.0025: + return False + return True + diff --git a/freqtrade/templates/optima.py b/freqtrade/templates/optima.py new file mode 100644 index 00000000..d2a4c241 --- /dev/null +++ b/freqtrade/templates/optima.py @@ -0,0 +1,69 @@ +from freqtrade.strategy.interface import IStrategy +import pandas as pd +import numpy as np +import talib as ta +import logging +import datetime +from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter + +logger = logging.getLogger(__name__) + +class MyOptimizedStrategy(IStrategy): + # --- Hyperoptables --- + buy_rsi = IntParameter(20, 50, default=30, space="buy") + sell_rsi = IntParameter(50, 80, default=70, space="sell") + + # --- FreqAI 相关 --- + minimal_roi = {"0": 0.05, "30": 0.02, "60": 0} + stoploss = -0.1 + trailing_stop = True + trailing_stop_positive = 0.03 + trailing_stop_positive_offset = 0.05 + + def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + dataframe["up_or_down"] = np.where( + dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0 + ) + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 添加 volume_ma + dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() + + # Bollinger Bands + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + + return dataframe + + def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + conditions = [ + (dataframe['do_predict'] == 1), + (dataframe['rsi'] < dataframe['&-buy_rsi_pred']), + (dataframe['close'] < dataframe['bb_lowerband']) + ] + dataframe.loc[reduce(np.logical_and, conditions), 'enter_long'] = 1 + return dataframe + + def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: + conditions = [ + (dataframe['do_predict'] == 1), + (dataframe['rsi'] > dataframe['&-sell_rsi_pred']) + ] + dataframe.loc[reduce(np.logical_and, conditions), 'exit_long'] = 1 + return dataframe + + def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, + current_profit: float, **kwargs) -> float: + dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_row = dataframe.iloc[-1] + if "&-stoploss" in last_row: + return float(last_row["&-stoploss"]) + return self.stoploss + diff --git a/freqtrade_utf8.log b/freqtrade_utf8.log new file mode 100644 index 00000000..4aaafe69 --- /dev/null +++ b/freqtrade_utf8.log @@ -0,0 +1,3066 @@ +Creating freqtrade_freqtrade_run ... +Creating freqtrade_freqtrade_run ... done +2025-04-29 01:54:55,246 - freqtrade - INFO - freqtrade 2025.3 +2025-04-29 01:54:55,464 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-04-29 01:54:56,878 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-04-29 01:54:56,879 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ... +2025-04-29 01:54:56,881 - freqtrade.loggers - INFO - Enabling colorized output. +2025-04-29 01:54:56,881 - root - INFO - Logfile configured +2025-04-29 01:54:56,882 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-04-29 01:54:56,882 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250101-20250420 ... +2025-04-29 01:54:56,907 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250101-20250420 +2025-04-29 01:54:56,909 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-04-29 01:54:56,910 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-04-29 01:54:56,916 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-04-29 01:54:56,916 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-04-29 01:54:56,917 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:56,919 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-04-29 01:54:56,919 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-04-29 01:54:56,925 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-04-29 01:54:56,931 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-04-29 01:54:59,471 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-04-29 01:54:59,491 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-04-29 01:54:59,491 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +'minutes'}. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +'stoploss_on_exchange_interval': 60} +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-04-29 01:54:59,502 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:59,505 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-04-29 01:54:59,512 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-04-29 01:54:59,512 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:54:59,513 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Loading data from 2024-12-01 21:30:00 up to 2025-04-20 00:00:00 (139 days). +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-04-29 01:54:59,673 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-04-29 01:55:01,274 - matplotlib.font_manager - INFO - generated new fontManager +2025-04-29 01:55:01,489 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-04-29 01:55:01,490 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-04-29 01:55:01,493 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-04-29 01:55:01,498 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-04-29 01:55:01,500 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:55:01,501 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:55:01,502 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1735689600_prediction.feather +2025-04-29 01:55:01,602 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,603 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,705 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:01,706 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,814 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:55:01,815 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,942 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,943 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,037 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:02,038 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,113 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,118 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:55:02,121 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.152831 +1 0.003285 50.152831 +2 0.001898 50.152831 +3 0.000484 50.152831 +4 0.001688 50.152831 +2025-04-29 01:55:02,123 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,129 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:02,130 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:02,134 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1735689600/cb_btc_1735689600 +2025-04-29 01:55:02,135 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:02,151 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:02,152 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:55:07,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:07,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24624 validation_1-rmse:0.26036 +[1] validation_0-rmse:0.24176 validation_1-rmse:0.25460 +[2] validation_0-rmse:0.23782 validation_1-rmse:0.24904 +[3] validation_0-rmse:0.23408 validation_1-rmse:0.24381 +[4] validation_0-rmse:0.23057 validation_1-rmse:0.23882 +[5] validation_0-rmse:0.22701 validation_1-rmse:0.23409 +[6] validation_0-rmse:0.22400 validation_1-rmse:0.22962 +[7] validation_0-rmse:0.22088 validation_1-rmse:0.22533 +[8] validation_0-rmse:0.21817 validation_1-rmse:0.22130 +[9] validation_0-rmse:0.21491 validation_1-rmse:0.21740 +[10] validation_0-rmse:0.21265 validation_1-rmse:0.21347 +[11] validation_0-rmse:0.20982 validation_1-rmse:0.20978 +[12] validation_0-rmse:0.20747 validation_1-rmse:0.20640 +[13] validation_0-rmse:0.20512 validation_1-rmse:0.20299 +[14] validation_0-rmse:0.20280 validation_1-rmse:0.19966 +[15] validation_0-rmse:0.20012 validation_1-rmse:0.19656 +[16] validation_0-rmse:0.19785 validation_1-rmse:0.19346 +[17] validation_0-rmse:0.19572 validation_1-rmse:0.19054 +[18] validation_0-rmse:0.19400 validation_1-rmse:0.18759 +[19] validation_0-rmse:0.19164 validation_1-rmse:0.18488 +[20] validation_0-rmse:0.18956 validation_1-rmse:0.18205 +[21] validation_0-rmse:0.18746 validation_1-rmse:0.17951 +[22] validation_0-rmse:0.18593 validation_1-rmse:0.17696 +[23] validation_0-rmse:0.18395 validation_1-rmse:0.17465 +[24] validation_0-rmse:0.18249 validation_1-rmse:0.17217 +[25] validation_0-rmse:0.18084 validation_1-rmse:0.16993 +[26] validation_0-rmse:0.17928 validation_1-rmse:0.16771 +[27] validation_0-rmse:0.17776 validation_1-rmse:0.16571 +[28] validation_0-rmse:0.17652 validation_1-rmse:0.16356 +[29] validation_0-rmse:0.17499 validation_1-rmse:0.16166 +[30] validation_0-rmse:0.17371 validation_1-rmse:0.15983 +[31] validation_0-rmse:0.17243 validation_1-rmse:0.15792 +[32] validation_0-rmse:0.17110 validation_1-rmse:0.15628 +[33] validation_0-rmse:0.16996 validation_1-rmse:0.15433 +[34] validation_0-rmse:0.16884 validation_1-rmse:0.15277 +[35] validation_0-rmse:0.16785 validation_1-rmse:0.15090 +[36] validation_0-rmse:0.16682 validation_1-rmse:0.14942 +[37] validation_0-rmse:0.16559 validation_1-rmse:0.14774 +[38] validation_0-rmse:0.16459 validation_1-rmse:0.14628 +[39] validation_0-rmse:0.16356 validation_1-rmse:0.14466 +[40] validation_0-rmse:0.16250 validation_1-rmse:0.14330 +[41] validation_0-rmse:0.16153 validation_1-rmse:0.14201 +[42] validation_0-rmse:0.16059 validation_1-rmse:0.14075 +[43] validation_0-rmse:0.15986 validation_1-rmse:0.13938 +[44] validation_0-rmse:0.15908 validation_1-rmse:0.13822 +[45] validation_0-rmse:0.15810 validation_1-rmse:0.13687 +[46] validation_0-rmse:0.15733 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.15655 validation_1-rmse:0.13458 +[48] validation_0-rmse:0.15580 validation_1-rmse:0.13355 +[49] validation_0-rmse:0.15512 validation_1-rmse:0.13228 +[50] validation_0-rmse:0.15434 validation_1-rmse:0.13121 +[51] validation_0-rmse:0.15363 validation_1-rmse:0.13030 +[52] validation_0-rmse:0.15294 validation_1-rmse:0.12937 +[53] validation_0-rmse:0.15243 validation_1-rmse:0.12818 +[54] validation_0-rmse:0.15170 validation_1-rmse:0.12720 +[55] validation_0-rmse:0.15096 validation_1-rmse:0.12632 +[56] validation_0-rmse:0.15035 validation_1-rmse:0.12538 +[57] validation_0-rmse:0.14977 validation_1-rmse:0.12453 +[58] validation_0-rmse:0.14914 validation_1-rmse:0.12363 +[59] validation_0-rmse:0.14867 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.14819 validation_1-rmse:0.12183 +[61] validation_0-rmse:0.14763 validation_1-rmse:0.12108 +[62] validation_0-rmse:0.14706 validation_1-rmse:0.12035 +[63] validation_0-rmse:0.14648 validation_1-rmse:0.11946 +[64] validation_0-rmse:0.14601 validation_1-rmse:0.11876 +[65] validation_0-rmse:0.14553 validation_1-rmse:0.11808 +[66] validation_0-rmse:0.14506 validation_1-rmse:0.11742 +[67] validation_0-rmse:0.14469 validation_1-rmse:0.11671 +[68] validation_0-rmse:0.14422 validation_1-rmse:0.11604 +[69] validation_0-rmse:0.14381 validation_1-rmse:0.11543 +[70] validation_0-rmse:0.14337 validation_1-rmse:0.11485 +[71] validation_0-rmse:0.14294 validation_1-rmse:0.11398 +[72] validation_0-rmse:0.14260 validation_1-rmse:0.11335 +[73] validation_0-rmse:0.14223 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14190 validation_1-rmse:0.11225 +[75] validation_0-rmse:0.14144 validation_1-rmse:0.11143 +[76] validation_0-rmse:0.14098 validation_1-rmse:0.11052 +[77] validation_0-rmse:0.14062 validation_1-rmse:0.10998 +[78] validation_0-rmse:0.14029 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.13993 validation_1-rmse:0.10888 +[80] validation_0-rmse:0.13958 validation_1-rmse:0.10839 +[81] validation_0-rmse:0.13918 validation_1-rmse:0.10767 +[82] validation_0-rmse:0.13897 validation_1-rmse:0.10720 +[83] validation_0-rmse:0.13864 validation_1-rmse:0.10669 +[84] validation_0-rmse:0.13836 validation_1-rmse:0.10620 +[85] validation_0-rmse:0.13810 validation_1-rmse:0.10573 +[86] validation_0-rmse:0.13782 validation_1-rmse:0.10526 +[87] validation_0-rmse:0.13756 validation_1-rmse:0.10458 +[88] validation_0-rmse:0.13736 validation_1-rmse:0.10420 +[89] validation_0-rmse:0.13708 validation_1-rmse:0.10383 +[90] validation_0-rmse:0.13685 validation_1-rmse:0.10343 +[91] validation_0-rmse:0.13658 validation_1-rmse:0.10298 +[92] validation_0-rmse:0.13646 validation_1-rmse:0.10231 +[93] validation_0-rmse:0.13615 validation_1-rmse:0.10190 +[94] validation_0-rmse:0.13589 validation_1-rmse:0.10154 +[95] validation_0-rmse:0.13572 validation_1-rmse:0.10095 +[96] validation_0-rmse:0.13550 validation_1-rmse:0.10058 +[97] validation_0-rmse:0.13530 validation_1-rmse:0.10026 +[98] validation_0-rmse:0.13513 validation_1-rmse:0.09995 +[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950 +2025-04-29 01:55:08,221 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.09 secs) -------------------- +2025-04-29 01:55:08,222 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:08,903 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:55:08,904 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1736553600_prediction.feather +2025-04-29 01:55:08,907 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,912 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:08,914 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:08,917 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,924 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:08,925 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:08,929 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1736553600/cb_btc_1736553600 +2025-04-29 01:55:08,930 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:08,946 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:08,947 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:55:13,908 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:55:13,911 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:13,912 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26037 validation_1-rmse:0.25324 +[1] validation_0-rmse:0.25572 validation_1-rmse:0.24787 +[2] validation_0-rmse:0.25117 validation_1-rmse:0.24281 +[3] validation_0-rmse:0.24697 validation_1-rmse:0.23802 +[4] validation_0-rmse:0.24328 validation_1-rmse:0.23332 +[5] validation_0-rmse:0.23939 validation_1-rmse:0.22905 +[6] validation_0-rmse:0.23522 validation_1-rmse:0.22484 +[7] validation_0-rmse:0.23148 validation_1-rmse:0.22085 +[8] validation_0-rmse:0.22873 validation_1-rmse:0.21697 +[9] validation_0-rmse:0.22519 validation_1-rmse:0.21317 +[10] validation_0-rmse:0.22206 validation_1-rmse:0.20963 +[11] validation_0-rmse:0.21866 validation_1-rmse:0.20626 +[12] validation_0-rmse:0.21563 validation_1-rmse:0.20296 +[13] validation_0-rmse:0.21313 validation_1-rmse:0.19956 +[14] validation_0-rmse:0.21062 validation_1-rmse:0.19636 +[15] validation_0-rmse:0.20808 validation_1-rmse:0.19339 +[16] validation_0-rmse:0.20570 validation_1-rmse:0.19058 +[17] validation_0-rmse:0.20318 validation_1-rmse:0.18781 +[18] validation_0-rmse:0.20113 validation_1-rmse:0.18518 +[19] validation_0-rmse:0.19934 validation_1-rmse:0.18248 +[20] validation_0-rmse:0.19735 validation_1-rmse:0.18006 +[21] validation_0-rmse:0.19541 validation_1-rmse:0.17744 +[22] validation_0-rmse:0.19336 validation_1-rmse:0.17517 +[23] validation_0-rmse:0.19145 validation_1-rmse:0.17301 +[24] validation_0-rmse:0.18989 validation_1-rmse:0.17058 +[25] validation_0-rmse:0.18782 validation_1-rmse:0.16854 +[26] validation_0-rmse:0.18634 validation_1-rmse:0.16625 +[27] validation_0-rmse:0.18471 validation_1-rmse:0.16430 +[28] validation_0-rmse:0.18312 validation_1-rmse:0.16236 +[29] validation_0-rmse:0.18157 validation_1-rmse:0.16053 +[30] validation_0-rmse:0.17991 validation_1-rmse:0.15849 +[31] validation_0-rmse:0.17839 validation_1-rmse:0.15677 +[32] validation_0-rmse:0.17693 validation_1-rmse:0.15498 +[33] validation_0-rmse:0.17574 validation_1-rmse:0.15336 +[34] validation_0-rmse:0.17469 validation_1-rmse:0.15168 +[35] validation_0-rmse:0.17352 validation_1-rmse:0.15015 +[36] validation_0-rmse:0.17228 validation_1-rmse:0.14868 +[37] validation_0-rmse:0.17127 validation_1-rmse:0.14692 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.14553 +[39] validation_0-rmse:0.16926 validation_1-rmse:0.14420 +[40] validation_0-rmse:0.16821 validation_1-rmse:0.14297 +[41] validation_0-rmse:0.16740 validation_1-rmse:0.14144 +[42] validation_0-rmse:0.16647 validation_1-rmse:0.14020 +[43] validation_0-rmse:0.16548 validation_1-rmse:0.13903 +[44] validation_0-rmse:0.16440 validation_1-rmse:0.13765 +[45] validation_0-rmse:0.16353 validation_1-rmse:0.13652 +[46] validation_0-rmse:0.16269 validation_1-rmse:0.13522 +[47] validation_0-rmse:0.16193 validation_1-rmse:0.13419 +[48] validation_0-rmse:0.16114 validation_1-rmse:0.13311 +[49] validation_0-rmse:0.16043 validation_1-rmse:0.13214 +[50] validation_0-rmse:0.15971 validation_1-rmse:0.13090 +[51] validation_0-rmse:0.15909 validation_1-rmse:0.12992 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.12899 +[53] validation_0-rmse:0.15763 validation_1-rmse:0.12809 +[54] validation_0-rmse:0.15697 validation_1-rmse:0.12724 +[55] validation_0-rmse:0.15631 validation_1-rmse:0.12637 +[56] validation_0-rmse:0.15553 validation_1-rmse:0.12535 +[57] validation_0-rmse:0.15494 validation_1-rmse:0.12456 +[58] validation_0-rmse:0.15452 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.15396 validation_1-rmse:0.12273 +[60] validation_0-rmse:0.15334 validation_1-rmse:0.12196 +[61] validation_0-rmse:0.15274 validation_1-rmse:0.12123 +[62] validation_0-rmse:0.15221 validation_1-rmse:0.12048 +[63] validation_0-rmse:0.15176 validation_1-rmse:0.11953 +[64] validation_0-rmse:0.15133 validation_1-rmse:0.11887 +[65] validation_0-rmse:0.15080 validation_1-rmse:0.11796 +[66] validation_0-rmse:0.15035 validation_1-rmse:0.11734 +[67] validation_0-rmse:0.14995 validation_1-rmse:0.11667 +[68] validation_0-rmse:0.14954 validation_1-rmse:0.11616 +[69] validation_0-rmse:0.14916 validation_1-rmse:0.11535 +[70] validation_0-rmse:0.14887 validation_1-rmse:0.11469 +[71] validation_0-rmse:0.14854 validation_1-rmse:0.11408 +[72] validation_0-rmse:0.14811 validation_1-rmse:0.11334 +[73] validation_0-rmse:0.14766 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14738 validation_1-rmse:0.11231 +[75] validation_0-rmse:0.14697 validation_1-rmse:0.11184 +[76] validation_0-rmse:0.14663 validation_1-rmse:0.11108 +[77] validation_0-rmse:0.14635 validation_1-rmse:0.11058 +[78] validation_0-rmse:0.14591 validation_1-rmse:0.10984 +[79] validation_0-rmse:0.14561 validation_1-rmse:0.10929 +[80] validation_0-rmse:0.14529 validation_1-rmse:0.10875 +[81] validation_0-rmse:0.14510 validation_1-rmse:0.10826 +[82] validation_0-rmse:0.14471 validation_1-rmse:0.10772 +[83] validation_0-rmse:0.14444 validation_1-rmse:0.10725 +[84] validation_0-rmse:0.14420 validation_1-rmse:0.10652 +[85] validation_0-rmse:0.14393 validation_1-rmse:0.10608 +[86] validation_0-rmse:0.14371 validation_1-rmse:0.10567 +[87] validation_0-rmse:0.14342 validation_1-rmse:0.10528 +[88] validation_0-rmse:0.14314 validation_1-rmse:0.10483 +[89] validation_0-rmse:0.14307 validation_1-rmse:0.10439 +[90] validation_0-rmse:0.14273 validation_1-rmse:0.10395 +[91] validation_0-rmse:0.14237 validation_1-rmse:0.10353 +[92] validation_0-rmse:0.14210 validation_1-rmse:0.10318 +[93] validation_0-rmse:0.14186 validation_1-rmse:0.10279 +[94] validation_0-rmse:0.14175 validation_1-rmse:0.10234 +[95] validation_0-rmse:0.14153 validation_1-rmse:0.10204 +[96] validation_0-rmse:0.14142 validation_1-rmse:0.10160 +[97] validation_0-rmse:0.14124 validation_1-rmse:0.10126 +[98] validation_0-rmse:0.14102 validation_1-rmse:0.10068 +[99] validation_0-rmse:0.14079 validation_1-rmse:0.10036 +2025-04-29 01:55:14,692 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.76 secs) -------------------- +2025-04-29 01:55:14,693 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:15,250 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:55:15,250 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1737417600_prediction.feather +2025-04-29 01:55:15,254 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,261 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:15,262 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:15,268 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,275 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:15,276 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:15,281 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1737417600/cb_btc_1737417600 +2025-04-29 01:55:15,281 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:15,297 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:15,298 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:55:20,324 - datasieve.pipeline - INFO - DI tossed 1622 predictions for being too far from training data. +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25769 validation_1-rmse:0.25549 +[1] validation_0-rmse:0.25314 validation_1-rmse:0.24986 +[2] validation_0-rmse:0.24864 validation_1-rmse:0.24456 +[3] validation_0-rmse:0.24486 validation_1-rmse:0.23955 +[4] validation_0-rmse:0.24144 validation_1-rmse:0.23480 +[5] validation_0-rmse:0.23803 validation_1-rmse:0.23024 +[6] validation_0-rmse:0.23468 validation_1-rmse:0.22599 +[7] validation_0-rmse:0.23134 validation_1-rmse:0.22162 +[8] validation_0-rmse:0.22843 validation_1-rmse:0.21773 +[9] validation_0-rmse:0.22560 validation_1-rmse:0.21396 +[10] validation_0-rmse:0.22402 validation_1-rmse:0.21023 +[11] validation_0-rmse:0.22155 validation_1-rmse:0.20680 +[12] validation_0-rmse:0.21899 validation_1-rmse:0.20342 +[13] validation_0-rmse:0.21654 validation_1-rmse:0.20029 +[14] validation_0-rmse:0.21431 validation_1-rmse:0.19719 +[15] validation_0-rmse:0.21282 validation_1-rmse:0.19411 +[16] validation_0-rmse:0.21076 validation_1-rmse:0.19117 +[17] validation_0-rmse:0.20882 validation_1-rmse:0.18835 +[18] validation_0-rmse:0.20695 validation_1-rmse:0.18547 +[19] validation_0-rmse:0.20538 validation_1-rmse:0.18292 +[20] validation_0-rmse:0.20345 validation_1-rmse:0.18038 +[21] validation_0-rmse:0.20148 validation_1-rmse:0.17799 +[22] validation_0-rmse:0.19991 validation_1-rmse:0.17569 +[23] validation_0-rmse:0.19832 validation_1-rmse:0.17350 +[24] validation_0-rmse:0.19658 validation_1-rmse:0.17096 +[25] validation_0-rmse:0.19474 validation_1-rmse:0.16879 +[26] validation_0-rmse:0.19292 validation_1-rmse:0.16665 +[27] validation_0-rmse:0.19134 validation_1-rmse:0.16470 +[28] validation_0-rmse:0.19034 validation_1-rmse:0.16253 +[29] validation_0-rmse:0.18882 validation_1-rmse:0.16068 +[30] validation_0-rmse:0.18736 validation_1-rmse:0.15892 +[31] validation_0-rmse:0.18605 validation_1-rmse:0.15690 +[32] validation_0-rmse:0.18481 validation_1-rmse:0.15521 +[33] validation_0-rmse:0.18346 validation_1-rmse:0.15356 +[34] validation_0-rmse:0.18222 validation_1-rmse:0.15188 +[35] validation_0-rmse:0.18095 validation_1-rmse:0.15028 +[36] validation_0-rmse:0.18015 validation_1-rmse:0.14857 +[37] validation_0-rmse:0.17915 validation_1-rmse:0.14713 +[38] validation_0-rmse:0.17817 validation_1-rmse:0.14573 +[39] validation_0-rmse:0.17723 validation_1-rmse:0.14437 +[40] validation_0-rmse:0.17619 validation_1-rmse:0.14308 +[41] validation_0-rmse:0.17509 validation_1-rmse:0.14176 +[42] validation_0-rmse:0.17407 validation_1-rmse:0.14047 +[43] validation_0-rmse:0.17340 validation_1-rmse:0.13921 +[44] validation_0-rmse:0.17245 validation_1-rmse:0.13806 +[45] validation_0-rmse:0.17212 validation_1-rmse:0.13685 +[46] validation_0-rmse:0.17133 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.17064 validation_1-rmse:0.13451 +[48] validation_0-rmse:0.17004 validation_1-rmse:0.13331 +[49] validation_0-rmse:0.16941 validation_1-rmse:0.13222 +[50] validation_0-rmse:0.16858 validation_1-rmse:0.13123 +[51] validation_0-rmse:0.16786 validation_1-rmse:0.13007 +[52] validation_0-rmse:0.16718 validation_1-rmse:0.12912 +[53] validation_0-rmse:0.16651 validation_1-rmse:0.12806 +[54] validation_0-rmse:0.16592 validation_1-rmse:0.12709 +[55] validation_0-rmse:0.16542 validation_1-rmse:0.12604 +[56] validation_0-rmse:0.16479 validation_1-rmse:0.12523 +[57] validation_0-rmse:0.16426 validation_1-rmse:0.12439 +[58] validation_0-rmse:0.16363 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.16325 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.16289 validation_1-rmse:0.12173 +[61] validation_0-rmse:0.16226 validation_1-rmse:0.12099 +[62] validation_0-rmse:0.16176 validation_1-rmse:0.12010 +[63] validation_0-rmse:0.16144 validation_1-rmse:0.11936 +[64] validation_0-rmse:0.16088 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.16030 validation_1-rmse:0.11786 +[66] validation_0-rmse:0.15991 validation_1-rmse:0.11714 +[67] validation_0-rmse:0.15947 validation_1-rmse:0.11640 +[68] validation_0-rmse:0.15912 validation_1-rmse:0.11574 +[69] validation_0-rmse:0.15874 validation_1-rmse:0.11507 +[70] validation_0-rmse:0.15837 validation_1-rmse:0.11430 +[71] validation_0-rmse:0.15798 validation_1-rmse:0.11365 +[72] validation_0-rmse:0.15763 validation_1-rmse:0.11305 +[73] validation_0-rmse:0.15713 validation_1-rmse:0.11250 +[74] validation_0-rmse:0.15648 validation_1-rmse:0.11177 +[75] validation_0-rmse:0.15619 validation_1-rmse:0.11122 +[76] validation_0-rmse:0.15593 validation_1-rmse:0.11066 +[77] validation_0-rmse:0.15562 validation_1-rmse:0.11007 +[78] validation_0-rmse:0.15519 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.15500 validation_1-rmse:0.10883 +[80] validation_0-rmse:0.15461 validation_1-rmse:0.10835 +[81] validation_0-rmse:0.15417 validation_1-rmse:0.10780 +[82] validation_0-rmse:0.15393 validation_1-rmse:0.10742 +[83] validation_0-rmse:0.15395 validation_1-rmse:0.10634 +[84] validation_0-rmse:0.15359 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.15315 validation_1-rmse:0.10539 +[86] validation_0-rmse:0.15315 validation_1-rmse:0.10440 +[87] validation_0-rmse:0.15278 validation_1-rmse:0.10400 +[88] validation_0-rmse:0.15239 validation_1-rmse:0.10353 +[89] validation_0-rmse:0.15200 validation_1-rmse:0.10310 +[90] validation_0-rmse:0.15182 validation_1-rmse:0.10245 +[91] validation_0-rmse:0.15175 validation_1-rmse:0.10182 +[92] validation_0-rmse:0.15139 validation_1-rmse:0.10138 +[93] validation_0-rmse:0.15105 validation_1-rmse:0.10095 +[94] validation_0-rmse:0.15091 validation_1-rmse:0.10056 +[95] validation_0-rmse:0.15088 validation_1-rmse:0.09964 +[96] validation_0-rmse:0.15065 validation_1-rmse:0.09927 +[97] validation_0-rmse:0.15036 validation_1-rmse:0.09888 +[98] validation_0-rmse:0.15021 validation_1-rmse:0.09852 +[99] validation_0-rmse:0.15004 validation_1-rmse:0.09815 +2025-04-29 01:55:21,007 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.73 secs) -------------------- +2025-04-29 01:55:21,008 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:21,504 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:55:21,505 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1738281600_prediction.feather +2025-04-29 01:55:21,510 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,516 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:21,517 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:21,522 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,528 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:21,529 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:21,533 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1738281600/cb_btc_1738281600 +2025-04-29 01:55:21,534 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:21,550 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:21,550 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:55:26,605 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:26,606 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25046 validation_1-rmse:0.26128 +[1] validation_0-rmse:0.24588 validation_1-rmse:0.25570 +[2] validation_0-rmse:0.24156 validation_1-rmse:0.25047 +[3] validation_0-rmse:0.23757 validation_1-rmse:0.24551 +[4] validation_0-rmse:0.23411 validation_1-rmse:0.24075 +[5] validation_0-rmse:0.23029 validation_1-rmse:0.23637 +[6] validation_0-rmse:0.22707 validation_1-rmse:0.23199 +[7] validation_0-rmse:0.22405 validation_1-rmse:0.22801 +[8] validation_0-rmse:0.22083 validation_1-rmse:0.22420 +[9] validation_0-rmse:0.21768 validation_1-rmse:0.22038 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.21674 +[11] validation_0-rmse:0.21187 validation_1-rmse:0.21322 +[12] validation_0-rmse:0.20911 validation_1-rmse:0.20996 +[13] validation_0-rmse:0.20669 validation_1-rmse:0.20679 +[14] validation_0-rmse:0.20441 validation_1-rmse:0.20366 +[15] validation_0-rmse:0.20250 validation_1-rmse:0.20054 +[16] validation_0-rmse:0.20017 validation_1-rmse:0.19757 +[17] validation_0-rmse:0.19804 validation_1-rmse:0.19490 +[18] validation_0-rmse:0.19618 validation_1-rmse:0.19221 +[19] validation_0-rmse:0.19404 validation_1-rmse:0.18954 +[20] validation_0-rmse:0.19209 validation_1-rmse:0.18666 +[21] validation_0-rmse:0.19014 validation_1-rmse:0.18430 +[22] validation_0-rmse:0.18845 validation_1-rmse:0.18197 +[23] validation_0-rmse:0.18653 validation_1-rmse:0.17972 +[24] validation_0-rmse:0.18468 validation_1-rmse:0.17722 +[25] validation_0-rmse:0.18325 validation_1-rmse:0.17491 +[26] validation_0-rmse:0.18152 validation_1-rmse:0.17284 +[27] validation_0-rmse:0.17999 validation_1-rmse:0.17092 +[28] validation_0-rmse:0.17846 validation_1-rmse:0.16892 +[29] validation_0-rmse:0.17696 validation_1-rmse:0.16709 +[30] validation_0-rmse:0.17558 validation_1-rmse:0.16510 +[31] validation_0-rmse:0.17418 validation_1-rmse:0.16335 +[32] validation_0-rmse:0.17293 validation_1-rmse:0.16161 +[33] validation_0-rmse:0.17159 validation_1-rmse:0.16003 +[34] validation_0-rmse:0.17030 validation_1-rmse:0.15831 +[35] validation_0-rmse:0.16907 validation_1-rmse:0.15681 +[36] validation_0-rmse:0.16796 validation_1-rmse:0.15513 +[37] validation_0-rmse:0.16690 validation_1-rmse:0.15349 +[38] validation_0-rmse:0.16580 validation_1-rmse:0.15204 +[39] validation_0-rmse:0.16492 validation_1-rmse:0.15050 +[40] validation_0-rmse:0.16383 validation_1-rmse:0.14918 +[41] validation_0-rmse:0.16281 validation_1-rmse:0.14788 +[42] validation_0-rmse:0.16176 validation_1-rmse:0.14660 +[43] validation_0-rmse:0.16082 validation_1-rmse:0.14516 +[44] validation_0-rmse:0.15990 validation_1-rmse:0.14395 +[45] validation_0-rmse:0.15891 validation_1-rmse:0.14281 +[46] validation_0-rmse:0.15797 validation_1-rmse:0.14168 +[47] validation_0-rmse:0.15712 validation_1-rmse:0.14040 +[48] validation_0-rmse:0.15632 validation_1-rmse:0.13933 +[49] validation_0-rmse:0.15542 validation_1-rmse:0.13821 +[50] validation_0-rmse:0.15458 validation_1-rmse:0.13705 +[51] validation_0-rmse:0.15404 validation_1-rmse:0.13583 +[52] validation_0-rmse:0.15334 validation_1-rmse:0.13483 +[53] validation_0-rmse:0.15256 validation_1-rmse:0.13387 +[54] validation_0-rmse:0.15190 validation_1-rmse:0.13290 +[55] validation_0-rmse:0.15122 validation_1-rmse:0.13174 +[56] validation_0-rmse:0.15065 validation_1-rmse:0.13080 +[57] validation_0-rmse:0.15006 validation_1-rmse:0.12993 +[58] validation_0-rmse:0.14955 validation_1-rmse:0.12897 +[59] validation_0-rmse:0.14893 validation_1-rmse:0.12814 +[60] validation_0-rmse:0.14843 validation_1-rmse:0.12735 +[61] validation_0-rmse:0.14789 validation_1-rmse:0.12642 +[62] validation_0-rmse:0.14718 validation_1-rmse:0.12561 +[63] validation_0-rmse:0.14659 validation_1-rmse:0.12486 +[64] validation_0-rmse:0.14600 validation_1-rmse:0.12397 +[65] validation_0-rmse:0.14547 validation_1-rmse:0.12324 +[66] validation_0-rmse:0.14499 validation_1-rmse:0.12255 +[67] validation_0-rmse:0.14451 validation_1-rmse:0.12188 +[68] validation_0-rmse:0.14393 validation_1-rmse:0.12114 +[69] validation_0-rmse:0.14346 validation_1-rmse:0.12048 +[70] validation_0-rmse:0.14293 validation_1-rmse:0.11974 +[71] validation_0-rmse:0.14256 validation_1-rmse:0.11893 +[72] validation_0-rmse:0.14212 validation_1-rmse:0.11830 +[73] validation_0-rmse:0.14177 validation_1-rmse:0.11748 +[74] validation_0-rmse:0.14134 validation_1-rmse:0.11686 +[75] validation_0-rmse:0.14101 validation_1-rmse:0.11609 +[76] validation_0-rmse:0.14060 validation_1-rmse:0.11536 +[77] validation_0-rmse:0.14020 validation_1-rmse:0.11484 +[78] validation_0-rmse:0.13983 validation_1-rmse:0.11412 +[79] validation_0-rmse:0.13951 validation_1-rmse:0.11357 +[80] validation_0-rmse:0.13928 validation_1-rmse:0.11273 +[81] validation_0-rmse:0.13889 validation_1-rmse:0.11221 +[82] validation_0-rmse:0.13855 validation_1-rmse:0.11166 +[83] validation_0-rmse:0.13824 validation_1-rmse:0.11114 +[84] validation_0-rmse:0.13808 validation_1-rmse:0.11050 +[85] validation_0-rmse:0.13767 validation_1-rmse:0.10998 +[86] validation_0-rmse:0.13731 validation_1-rmse:0.10947 +[87] validation_0-rmse:0.13716 validation_1-rmse:0.10876 +[88] validation_0-rmse:0.13678 validation_1-rmse:0.10832 +[89] validation_0-rmse:0.13659 validation_1-rmse:0.10782 +[90] validation_0-rmse:0.13629 validation_1-rmse:0.10736 +[91] validation_0-rmse:0.13600 validation_1-rmse:0.10662 +[92] validation_0-rmse:0.13577 validation_1-rmse:0.10613 +[93] validation_0-rmse:0.13541 validation_1-rmse:0.10565 +[94] validation_0-rmse:0.13534 validation_1-rmse:0.10501 +[95] validation_0-rmse:0.13511 validation_1-rmse:0.10453 +[96] validation_0-rmse:0.13483 validation_1-rmse:0.10401 +[97] validation_0-rmse:0.13455 validation_1-rmse:0.10362 +[98] validation_0-rmse:0.13425 validation_1-rmse:0.10323 +[99] validation_0-rmse:0.13402 validation_1-rmse:0.10289 +2025-04-29 01:55:27,556 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.02 secs) -------------------- +2025-04-29 01:55:27,557 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:28,076 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:55:28,077 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1739145600_prediction.feather +2025-04-29 01:55:28,081 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,088 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:28,089 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:28,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,100 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:28,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:28,106 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1739145600/cb_btc_1739145600 +2025-04-29 01:55:28,107 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:28,123 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:28,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:55:33,123 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:33,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26428 validation_1-rmse:0.27464 +[1] validation_0-rmse:0.25911 validation_1-rmse:0.26865 +[2] validation_0-rmse:0.25427 validation_1-rmse:0.26296 +[3] validation_0-rmse:0.24970 validation_1-rmse:0.25748 +[4] validation_0-rmse:0.24525 validation_1-rmse:0.25222 +[5] validation_0-rmse:0.24140 validation_1-rmse:0.24725 +[6] validation_0-rmse:0.23748 validation_1-rmse:0.24264 +[7] validation_0-rmse:0.23368 validation_1-rmse:0.23792 +[8] validation_0-rmse:0.23022 validation_1-rmse:0.23363 +[9] validation_0-rmse:0.22695 validation_1-rmse:0.22945 +[10] validation_0-rmse:0.22381 validation_1-rmse:0.22543 +[11] validation_0-rmse:0.22105 validation_1-rmse:0.22154 +[12] validation_0-rmse:0.21818 validation_1-rmse:0.21797 +[13] validation_0-rmse:0.21526 validation_1-rmse:0.21430 +[14] validation_0-rmse:0.21284 validation_1-rmse:0.21101 +[15] validation_0-rmse:0.21034 validation_1-rmse:0.20769 +[16] validation_0-rmse:0.20802 validation_1-rmse:0.20438 +[17] validation_0-rmse:0.20590 validation_1-rmse:0.20136 +[18] validation_0-rmse:0.20386 validation_1-rmse:0.19837 +[19] validation_0-rmse:0.20219 validation_1-rmse:0.19549 +[20] validation_0-rmse:0.20037 validation_1-rmse:0.19283 +[21] validation_0-rmse:0.19826 validation_1-rmse:0.19005 +[22] validation_0-rmse:0.19657 validation_1-rmse:0.18750 +[23] validation_0-rmse:0.19525 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.19373 validation_1-rmse:0.18267 +[25] validation_0-rmse:0.19197 validation_1-rmse:0.18037 +[26] validation_0-rmse:0.19063 validation_1-rmse:0.17799 +[27] validation_0-rmse:0.18897 validation_1-rmse:0.17587 +[28] validation_0-rmse:0.18765 validation_1-rmse:0.17382 +[29] validation_0-rmse:0.18608 validation_1-rmse:0.17185 +[30] validation_0-rmse:0.18456 validation_1-rmse:0.16992 +[31] validation_0-rmse:0.18340 validation_1-rmse:0.16793 +[32] validation_0-rmse:0.18206 validation_1-rmse:0.16616 +[33] validation_0-rmse:0.18077 validation_1-rmse:0.16437 +[34] validation_0-rmse:0.17960 validation_1-rmse:0.16270 +[35] validation_0-rmse:0.17857 validation_1-rmse:0.16105 +[36] validation_0-rmse:0.17748 validation_1-rmse:0.15925 +[37] validation_0-rmse:0.17649 validation_1-rmse:0.15762 +[38] validation_0-rmse:0.17540 validation_1-rmse:0.15611 +[39] validation_0-rmse:0.17427 validation_1-rmse:0.15469 +[40] validation_0-rmse:0.17312 validation_1-rmse:0.15301 +[41] validation_0-rmse:0.17217 validation_1-rmse:0.15169 +[42] validation_0-rmse:0.17119 validation_1-rmse:0.15037 +[43] validation_0-rmse:0.17030 validation_1-rmse:0.14910 +[44] validation_0-rmse:0.16939 validation_1-rmse:0.14786 +[45] validation_0-rmse:0.16851 validation_1-rmse:0.14660 +[46] validation_0-rmse:0.16793 validation_1-rmse:0.14518 +[47] validation_0-rmse:0.16760 validation_1-rmse:0.14365 +[48] validation_0-rmse:0.16674 validation_1-rmse:0.14258 +[49] validation_0-rmse:0.16588 validation_1-rmse:0.14152 +[50] validation_0-rmse:0.16505 validation_1-rmse:0.14051 +[51] validation_0-rmse:0.16437 validation_1-rmse:0.13919 +[52] validation_0-rmse:0.16361 validation_1-rmse:0.13818 +[53] validation_0-rmse:0.16290 validation_1-rmse:0.13715 +[54] validation_0-rmse:0.16217 validation_1-rmse:0.13621 +[55] validation_0-rmse:0.16207 validation_1-rmse:0.13493 +[56] validation_0-rmse:0.16153 validation_1-rmse:0.13395 +[57] validation_0-rmse:0.16077 validation_1-rmse:0.13302 +[58] validation_0-rmse:0.16021 validation_1-rmse:0.13218 +[59] validation_0-rmse:0.15972 validation_1-rmse:0.13117 +[60] validation_0-rmse:0.15954 validation_1-rmse:0.13003 +[61] validation_0-rmse:0.15896 validation_1-rmse:0.12926 +[62] validation_0-rmse:0.15849 validation_1-rmse:0.12848 +[63] validation_0-rmse:0.15801 validation_1-rmse:0.12770 +[64] validation_0-rmse:0.15737 validation_1-rmse:0.12678 +[65] validation_0-rmse:0.15736 validation_1-rmse:0.12578 +[66] validation_0-rmse:0.15684 validation_1-rmse:0.12506 +[67] validation_0-rmse:0.15638 validation_1-rmse:0.12437 +[68] validation_0-rmse:0.15618 validation_1-rmse:0.12336 +[69] validation_0-rmse:0.15581 validation_1-rmse:0.12269 +[70] validation_0-rmse:0.15537 validation_1-rmse:0.12205 +[71] validation_0-rmse:0.15534 validation_1-rmse:0.12117 +[72] validation_0-rmse:0.15485 validation_1-rmse:0.12049 +[73] validation_0-rmse:0.15465 validation_1-rmse:0.11968 +[74] validation_0-rmse:0.15430 validation_1-rmse:0.11906 +[75] validation_0-rmse:0.15386 validation_1-rmse:0.11840 +[76] validation_0-rmse:0.15353 validation_1-rmse:0.11781 +[77] validation_0-rmse:0.15354 validation_1-rmse:0.11697 +[78] validation_0-rmse:0.15325 validation_1-rmse:0.11630 +[79] validation_0-rmse:0.15282 validation_1-rmse:0.11572 +[80] validation_0-rmse:0.15239 validation_1-rmse:0.11514 +[81] validation_0-rmse:0.15226 validation_1-rmse:0.11431 +[82] validation_0-rmse:0.15189 validation_1-rmse:0.11381 +[83] validation_0-rmse:0.15171 validation_1-rmse:0.11316 +[84] validation_0-rmse:0.15136 validation_1-rmse:0.11270 +[85] validation_0-rmse:0.15112 validation_1-rmse:0.11212 +[86] validation_0-rmse:0.15112 validation_1-rmse:0.11140 +[87] validation_0-rmse:0.15074 validation_1-rmse:0.11094 +[88] validation_0-rmse:0.15048 validation_1-rmse:0.11035 +[89] validation_0-rmse:0.15026 validation_1-rmse:0.10983 +[90] validation_0-rmse:0.14989 validation_1-rmse:0.10938 +[91] validation_0-rmse:0.14955 validation_1-rmse:0.10893 +[92] validation_0-rmse:0.14955 validation_1-rmse:0.10815 +[93] validation_0-rmse:0.14933 validation_1-rmse:0.10765 +[94] validation_0-rmse:0.14908 validation_1-rmse:0.10711 +[95] validation_0-rmse:0.14889 validation_1-rmse:0.10668 +[96] validation_0-rmse:0.14853 validation_1-rmse:0.10627 +[97] validation_0-rmse:0.14853 validation_1-rmse:0.10553 +[98] validation_0-rmse:0.14835 validation_1-rmse:0.10513 +[99] validation_0-rmse:0.14818 validation_1-rmse:0.10475 +2025-04-29 01:55:33,929 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.82 secs) -------------------- +2025-04-29 01:55:33,930 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:34,433 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:55:34,434 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740009600_prediction.feather +2025-04-29 01:55:34,440 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,447 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:34,448 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:34,453 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,459 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:34,461 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:34,465 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740009600/cb_btc_1740009600 +2025-04-29 01:55:34,466 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:34,482 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:34,483 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:55:39,369 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:39,370 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27166 validation_1-rmse:0.27726 +[1] validation_0-rmse:0.26708 validation_1-rmse:0.27112 +[2] validation_0-rmse:0.26297 validation_1-rmse:0.26523 +[3] validation_0-rmse:0.25865 validation_1-rmse:0.25959 +[4] validation_0-rmse:0.25494 validation_1-rmse:0.25419 +[5] validation_0-rmse:0.25100 validation_1-rmse:0.24913 +[6] validation_0-rmse:0.24763 validation_1-rmse:0.24437 +[7] validation_0-rmse:0.24441 validation_1-rmse:0.23970 +[8] validation_0-rmse:0.24110 validation_1-rmse:0.23527 +[9] validation_0-rmse:0.23801 validation_1-rmse:0.23102 +[10] validation_0-rmse:0.23492 validation_1-rmse:0.22691 +[11] validation_0-rmse:0.23229 validation_1-rmse:0.22297 +[12] validation_0-rmse:0.22956 validation_1-rmse:0.21923 +[13] validation_0-rmse:0.22707 validation_1-rmse:0.21564 +[14] validation_0-rmse:0.22482 validation_1-rmse:0.21221 +[15] validation_0-rmse:0.22237 validation_1-rmse:0.20891 +[16] validation_0-rmse:0.22030 validation_1-rmse:0.20557 +[17] validation_0-rmse:0.21784 validation_1-rmse:0.20243 +[18] validation_0-rmse:0.21591 validation_1-rmse:0.19949 +[19] validation_0-rmse:0.21399 validation_1-rmse:0.19664 +[20] validation_0-rmse:0.21182 validation_1-rmse:0.19378 +[21] validation_0-rmse:0.20992 validation_1-rmse:0.19110 +[22] validation_0-rmse:0.20821 validation_1-rmse:0.18850 +[23] validation_0-rmse:0.20621 validation_1-rmse:0.18597 +[24] validation_0-rmse:0.20490 validation_1-rmse:0.18353 +[25] validation_0-rmse:0.20318 validation_1-rmse:0.18126 +[26] validation_0-rmse:0.20168 validation_1-rmse:0.17896 +[27] validation_0-rmse:0.19992 validation_1-rmse:0.17679 +[28] validation_0-rmse:0.19865 validation_1-rmse:0.17458 +[29] validation_0-rmse:0.19722 validation_1-rmse:0.17257 +[30] validation_0-rmse:0.19571 validation_1-rmse:0.17039 +[31] validation_0-rmse:0.19429 validation_1-rmse:0.16855 +[32] validation_0-rmse:0.19285 validation_1-rmse:0.16664 +[33] validation_0-rmse:0.19141 validation_1-rmse:0.16488 +[34] validation_0-rmse:0.19022 validation_1-rmse:0.16312 +[35] validation_0-rmse:0.18904 validation_1-rmse:0.16145 +[36] validation_0-rmse:0.18832 validation_1-rmse:0.15973 +[37] validation_0-rmse:0.18723 validation_1-rmse:0.15815 +[38] validation_0-rmse:0.18610 validation_1-rmse:0.15653 +[39] validation_0-rmse:0.18504 validation_1-rmse:0.15503 +[40] validation_0-rmse:0.18402 validation_1-rmse:0.15358 +[41] validation_0-rmse:0.18333 validation_1-rmse:0.15193 +[42] validation_0-rmse:0.18213 validation_1-rmse:0.15058 +[43] validation_0-rmse:0.18176 validation_1-rmse:0.14922 +[44] validation_0-rmse:0.18093 validation_1-rmse:0.14792 +[45] validation_0-rmse:0.18017 validation_1-rmse:0.14667 +[46] validation_0-rmse:0.17928 validation_1-rmse:0.14537 +[47] validation_0-rmse:0.17858 validation_1-rmse:0.14420 +[48] validation_0-rmse:0.17770 validation_1-rmse:0.14306 +[49] validation_0-rmse:0.17695 validation_1-rmse:0.14199 +[50] validation_0-rmse:0.17613 validation_1-rmse:0.14094 +[51] validation_0-rmse:0.17545 validation_1-rmse:0.13979 +[52] validation_0-rmse:0.17490 validation_1-rmse:0.13874 +[53] validation_0-rmse:0.17452 validation_1-rmse:0.13755 +[54] validation_0-rmse:0.17383 validation_1-rmse:0.13663 +[55] validation_0-rmse:0.17327 validation_1-rmse:0.13568 +[56] validation_0-rmse:0.17255 validation_1-rmse:0.13477 +[57] validation_0-rmse:0.17192 validation_1-rmse:0.13382 +[58] validation_0-rmse:0.17138 validation_1-rmse:0.13277 +[59] validation_0-rmse:0.17074 validation_1-rmse:0.13188 +[60] validation_0-rmse:0.17026 validation_1-rmse:0.13089 +[61] validation_0-rmse:0.16969 validation_1-rmse:0.13010 +[62] validation_0-rmse:0.16932 validation_1-rmse:0.12904 +[63] validation_0-rmse:0.16888 validation_1-rmse:0.12818 +[64] validation_0-rmse:0.16849 validation_1-rmse:0.12745 +[65] validation_0-rmse:0.16802 validation_1-rmse:0.12639 +[66] validation_0-rmse:0.16747 validation_1-rmse:0.12567 +[67] validation_0-rmse:0.16710 validation_1-rmse:0.12496 +[68] validation_0-rmse:0.16672 validation_1-rmse:0.12426 +[69] validation_0-rmse:0.16635 validation_1-rmse:0.12331 +[70] validation_0-rmse:0.16597 validation_1-rmse:0.12267 +[71] validation_0-rmse:0.16554 validation_1-rmse:0.12196 +[72] validation_0-rmse:0.16522 validation_1-rmse:0.12121 +[73] validation_0-rmse:0.16481 validation_1-rmse:0.12054 +[74] validation_0-rmse:0.16442 validation_1-rmse:0.11996 +[75] validation_0-rmse:0.16409 validation_1-rmse:0.11939 +[76] validation_0-rmse:0.16375 validation_1-rmse:0.11878 +[77] validation_0-rmse:0.16275 validation_1-rmse:0.11753 +[78] validation_0-rmse:0.16248 validation_1-rmse:0.11692 +[79] validation_0-rmse:0.16215 validation_1-rmse:0.11619 +[80] validation_0-rmse:0.16187 validation_1-rmse:0.11564 +[81] validation_0-rmse:0.16150 validation_1-rmse:0.11493 +[82] validation_0-rmse:0.16123 validation_1-rmse:0.11438 +[83] validation_0-rmse:0.16109 validation_1-rmse:0.11358 +[84] validation_0-rmse:0.16065 validation_1-rmse:0.11304 +[85] validation_0-rmse:0.16038 validation_1-rmse:0.11256 +[86] validation_0-rmse:0.16022 validation_1-rmse:0.11205 +[87] validation_0-rmse:0.16007 validation_1-rmse:0.11158 +[88] validation_0-rmse:0.15945 validation_1-rmse:0.11054 +[89] validation_0-rmse:0.15912 validation_1-rmse:0.11008 +[90] validation_0-rmse:0.15894 validation_1-rmse:0.10937 +[91] validation_0-rmse:0.15868 validation_1-rmse:0.10886 +[92] validation_0-rmse:0.15845 validation_1-rmse:0.10844 +[93] validation_0-rmse:0.15817 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.15789 validation_1-rmse:0.10758 +[95] validation_0-rmse:0.15772 validation_1-rmse:0.10721 +[96] validation_0-rmse:0.15763 validation_1-rmse:0.10676 +[97] validation_0-rmse:0.15751 validation_1-rmse:0.10609 +[98] validation_0-rmse:0.15731 validation_1-rmse:0.10574 +[99] validation_0-rmse:0.15738 validation_1-rmse:0.10531 +2025-04-29 01:55:40,266 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.80 secs) -------------------- +2025-04-29 01:55:40,267 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:40,801 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:55:40,802 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740873600_prediction.feather +2025-04-29 01:55:40,807 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,814 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:40,816 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:40,821 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,827 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:40,829 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:40,833 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740873600/cb_btc_1740873600 +2025-04-29 01:55:40,834 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:40,849 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:40,850 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:55:45,643 - datasieve.pipeline - INFO - DI tossed 2275 predictions for being too far from training data. +2025-04-29 01:55:45,646 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:45,647 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27618 validation_1-rmse:0.28955 +[1] validation_0-rmse:0.27005 validation_1-rmse:0.28323 +[2] validation_0-rmse:0.26414 validation_1-rmse:0.27722 +[3] validation_0-rmse:0.25897 validation_1-rmse:0.27161 +[4] validation_0-rmse:0.25425 validation_1-rmse:0.26622 +[5] validation_0-rmse:0.24886 validation_1-rmse:0.26100 +[6] validation_0-rmse:0.24522 validation_1-rmse:0.25606 +[7] validation_0-rmse:0.24137 validation_1-rmse:0.25132 +[8] validation_0-rmse:0.23765 validation_1-rmse:0.24687 +[9] validation_0-rmse:0.23323 validation_1-rmse:0.24254 +[10] validation_0-rmse:0.22900 validation_1-rmse:0.23827 +[11] validation_0-rmse:0.22588 validation_1-rmse:0.23450 +[12] validation_0-rmse:0.22228 validation_1-rmse:0.23055 +[13] validation_0-rmse:0.21872 validation_1-rmse:0.22698 +[14] validation_0-rmse:0.21492 validation_1-rmse:0.22348 +[15] validation_0-rmse:0.21329 validation_1-rmse:0.22011 +[16] validation_0-rmse:0.21024 validation_1-rmse:0.21686 +[17] validation_0-rmse:0.20823 validation_1-rmse:0.21380 +[18] validation_0-rmse:0.20544 validation_1-rmse:0.21075 +[19] validation_0-rmse:0.20415 validation_1-rmse:0.20787 +[20] validation_0-rmse:0.20143 validation_1-rmse:0.20515 +[21] validation_0-rmse:0.19917 validation_1-rmse:0.20247 +[22] validation_0-rmse:0.19745 validation_1-rmse:0.19994 +[23] validation_0-rmse:0.19508 validation_1-rmse:0.19746 +[24] validation_0-rmse:0.19300 validation_1-rmse:0.19490 +[25] validation_0-rmse:0.19085 validation_1-rmse:0.19254 +[26] validation_0-rmse:0.18898 validation_1-rmse:0.19031 +[27] validation_0-rmse:0.18720 validation_1-rmse:0.18794 +[28] validation_0-rmse:0.18503 validation_1-rmse:0.18584 +[29] validation_0-rmse:0.18314 validation_1-rmse:0.18382 +[30] validation_0-rmse:0.18132 validation_1-rmse:0.18164 +[31] validation_0-rmse:0.17984 validation_1-rmse:0.17967 +[32] validation_0-rmse:0.17818 validation_1-rmse:0.17779 +[33] validation_0-rmse:0.17637 validation_1-rmse:0.17572 +[34] validation_0-rmse:0.17473 validation_1-rmse:0.17399 +[35] validation_0-rmse:0.17338 validation_1-rmse:0.17229 +[36] validation_0-rmse:0.17253 validation_1-rmse:0.17055 +[37] validation_0-rmse:0.17149 validation_1-rmse:0.16883 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.16730 +[39] validation_0-rmse:0.16950 validation_1-rmse:0.16556 +[40] validation_0-rmse:0.16815 validation_1-rmse:0.16412 +[41] validation_0-rmse:0.16704 validation_1-rmse:0.16268 +[42] validation_0-rmse:0.16617 validation_1-rmse:0.16128 +[43] validation_0-rmse:0.16542 validation_1-rmse:0.15970 +[44] validation_0-rmse:0.16438 validation_1-rmse:0.15840 +[45] validation_0-rmse:0.16356 validation_1-rmse:0.15692 +[46] validation_0-rmse:0.16239 validation_1-rmse:0.15574 +[47] validation_0-rmse:0.16153 validation_1-rmse:0.15456 +[48] validation_0-rmse:0.16076 validation_1-rmse:0.15314 +[49] validation_0-rmse:0.15998 validation_1-rmse:0.15201 +[50] validation_0-rmse:0.15946 validation_1-rmse:0.15084 +[51] validation_0-rmse:0.15891 validation_1-rmse:0.14954 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.14847 +[53] validation_0-rmse:0.15764 validation_1-rmse:0.14722 +[54] validation_0-rmse:0.15707 validation_1-rmse:0.14623 +[55] validation_0-rmse:0.15653 validation_1-rmse:0.14527 +[56] validation_0-rmse:0.15583 validation_1-rmse:0.14434 +[57] validation_0-rmse:0.15549 validation_1-rmse:0.14329 +[58] validation_0-rmse:0.15507 validation_1-rmse:0.14241 +[59] validation_0-rmse:0.15468 validation_1-rmse:0.14053 +[60] validation_0-rmse:0.15398 validation_1-rmse:0.13968 +[61] validation_0-rmse:0.15390 validation_1-rmse:0.13864 +[62] validation_0-rmse:0.15360 validation_1-rmse:0.13783 +[63] validation_0-rmse:0.15368 validation_1-rmse:0.13704 +[64] validation_0-rmse:0.15338 validation_1-rmse:0.13624 +[65] validation_0-rmse:0.15273 validation_1-rmse:0.13551 +[66] validation_0-rmse:0.15238 validation_1-rmse:0.13451 +[67] validation_0-rmse:0.15212 validation_1-rmse:0.13290 +[68] validation_0-rmse:0.15191 validation_1-rmse:0.13217 +[69] validation_0-rmse:0.15138 validation_1-rmse:0.13143 +[70] validation_0-rmse:0.15090 validation_1-rmse:0.13071 +[71] validation_0-rmse:0.15082 validation_1-rmse:0.13001 +[72] validation_0-rmse:0.14988 validation_1-rmse:0.12847 +[73] validation_0-rmse:0.14953 validation_1-rmse:0.12783 +[74] validation_0-rmse:0.14924 validation_1-rmse:0.12709 +[75] validation_0-rmse:0.14926 validation_1-rmse:0.12578 +[76] validation_0-rmse:0.14903 validation_1-rmse:0.12499 +[77] validation_0-rmse:0.14851 validation_1-rmse:0.12435 +[78] validation_0-rmse:0.14808 validation_1-rmse:0.12368 +[79] validation_0-rmse:0.14768 validation_1-rmse:0.12305 +[80] validation_0-rmse:0.14741 validation_1-rmse:0.12217 +[81] validation_0-rmse:0.14712 validation_1-rmse:0.12165 +[82] validation_0-rmse:0.14696 validation_1-rmse:0.12110 +[83] validation_0-rmse:0.14686 validation_1-rmse:0.12045 +[84] validation_0-rmse:0.14648 validation_1-rmse:0.11984 +[85] validation_0-rmse:0.14623 validation_1-rmse:0.11923 +[86] validation_0-rmse:0.14606 validation_1-rmse:0.11869 +[87] validation_0-rmse:0.14583 validation_1-rmse:0.11754 +[88] validation_0-rmse:0.14572 validation_1-rmse:0.11710 +[89] validation_0-rmse:0.14537 validation_1-rmse:0.11660 +[90] validation_0-rmse:0.14510 validation_1-rmse:0.11614 +[91] validation_0-rmse:0.14516 validation_1-rmse:0.11514 +[92] validation_0-rmse:0.14480 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.14475 validation_1-rmse:0.11414 +[94] validation_0-rmse:0.14443 validation_1-rmse:0.11374 +[95] validation_0-rmse:0.14409 validation_1-rmse:0.11331 +[96] validation_0-rmse:0.14391 validation_1-rmse:0.11240 +[97] validation_0-rmse:0.14303 validation_1-rmse:0.11154 +[98] validation_0-rmse:0.14274 validation_1-rmse:0.11114 +[99] validation_0-rmse:0.14246 validation_1-rmse:0.11071 +2025-04-29 01:55:46,544 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.71 secs) -------------------- +2025-04-29 01:55:46,544 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:47,092 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:55:47,092 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1741737600_prediction.feather +2025-04-29 01:55:47,100 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,107 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:47,109 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:47,115 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,122 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:47,123 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:47,128 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1741737600/cb_btc_1741737600 +2025-04-29 01:55:47,129 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:47,145 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:47,145 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:55:51,987 - datasieve.pipeline - INFO - DI tossed 18 predictions for being too far from training data. +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26738 validation_1-rmse:0.26816 +[1] validation_0-rmse:0.26268 validation_1-rmse:0.26258 +[2] validation_0-rmse:0.25808 validation_1-rmse:0.25725 +[3] validation_0-rmse:0.25395 validation_1-rmse:0.25212 +[4] validation_0-rmse:0.24987 validation_1-rmse:0.24723 +[5] validation_0-rmse:0.24633 validation_1-rmse:0.24263 +[6] validation_0-rmse:0.24308 validation_1-rmse:0.23814 +[7] validation_0-rmse:0.23959 validation_1-rmse:0.23402 +[8] validation_0-rmse:0.23612 validation_1-rmse:0.22977 +[9] validation_0-rmse:0.23322 validation_1-rmse:0.22577 +[10] validation_0-rmse:0.23012 validation_1-rmse:0.22207 +[11] validation_0-rmse:0.22730 validation_1-rmse:0.21843 +[12] validation_0-rmse:0.22453 validation_1-rmse:0.21489 +[13] validation_0-rmse:0.22236 validation_1-rmse:0.21145 +[14] validation_0-rmse:0.22000 validation_1-rmse:0.20841 +[15] validation_0-rmse:0.21744 validation_1-rmse:0.20529 +[16] validation_0-rmse:0.21556 validation_1-rmse:0.20225 +[17] validation_0-rmse:0.21331 validation_1-rmse:0.19932 +[18] validation_0-rmse:0.21171 validation_1-rmse:0.19643 +[19] validation_0-rmse:0.21051 validation_1-rmse:0.19382 +[20] validation_0-rmse:0.20880 validation_1-rmse:0.19128 +[21] validation_0-rmse:0.20711 validation_1-rmse:0.18854 +[22] validation_0-rmse:0.20538 validation_1-rmse:0.18612 +[23] validation_0-rmse:0.20350 validation_1-rmse:0.18381 +[24] validation_0-rmse:0.20234 validation_1-rmse:0.18144 +[25] validation_0-rmse:0.20081 validation_1-rmse:0.17917 +[26] validation_0-rmse:0.19918 validation_1-rmse:0.17714 +[27] validation_0-rmse:0.19804 validation_1-rmse:0.17496 +[28] validation_0-rmse:0.19662 validation_1-rmse:0.17304 +[29] validation_0-rmse:0.19580 validation_1-rmse:0.17082 +[30] validation_0-rmse:0.19454 validation_1-rmse:0.16901 +[31] validation_0-rmse:0.19331 validation_1-rmse:0.16691 +[32] validation_0-rmse:0.19234 validation_1-rmse:0.16517 +[33] validation_0-rmse:0.19118 validation_1-rmse:0.16354 +[34] validation_0-rmse:0.19024 validation_1-rmse:0.16175 +[35] validation_0-rmse:0.18915 validation_1-rmse:0.16020 +[36] validation_0-rmse:0.18823 validation_1-rmse:0.15865 +[37] validation_0-rmse:0.18756 validation_1-rmse:0.15712 +[38] validation_0-rmse:0.18698 validation_1-rmse:0.15541 +[39] validation_0-rmse:0.18643 validation_1-rmse:0.15395 +[40] validation_0-rmse:0.18562 validation_1-rmse:0.15265 +[41] validation_0-rmse:0.18516 validation_1-rmse:0.15124 +[42] validation_0-rmse:0.18421 validation_1-rmse:0.14979 +[43] validation_0-rmse:0.18360 validation_1-rmse:0.14850 +[44] validation_0-rmse:0.18275 validation_1-rmse:0.14733 +[45] validation_0-rmse:0.18253 validation_1-rmse:0.14597 +[46] validation_0-rmse:0.18183 validation_1-rmse:0.14470 +[47] validation_0-rmse:0.18111 validation_1-rmse:0.14361 +[48] validation_0-rmse:0.18060 validation_1-rmse:0.14243 +[49] validation_0-rmse:0.18001 validation_1-rmse:0.14134 +[50] validation_0-rmse:0.17953 validation_1-rmse:0.14030 +[51] validation_0-rmse:0.17899 validation_1-rmse:0.13927 +[52] validation_0-rmse:0.17830 validation_1-rmse:0.13817 +[53] validation_0-rmse:0.17770 validation_1-rmse:0.13720 +[54] validation_0-rmse:0.17702 validation_1-rmse:0.13629 +[55] validation_0-rmse:0.17650 validation_1-rmse:0.13531 +[56] validation_0-rmse:0.17625 validation_1-rmse:0.13440 +[57] validation_0-rmse:0.17580 validation_1-rmse:0.13352 +[58] validation_0-rmse:0.17530 validation_1-rmse:0.13268 +[59] validation_0-rmse:0.17486 validation_1-rmse:0.13166 +[60] validation_0-rmse:0.17438 validation_1-rmse:0.13071 +[61] validation_0-rmse:0.17387 validation_1-rmse:0.12991 +[62] validation_0-rmse:0.17356 validation_1-rmse:0.12914 +[63] validation_0-rmse:0.17311 validation_1-rmse:0.12839 +[64] validation_0-rmse:0.17265 validation_1-rmse:0.12767 +[65] validation_0-rmse:0.17209 validation_1-rmse:0.12682 +[66] validation_0-rmse:0.17197 validation_1-rmse:0.12595 +[67] validation_0-rmse:0.17157 validation_1-rmse:0.12506 +[68] validation_0-rmse:0.17131 validation_1-rmse:0.12439 +[69] validation_0-rmse:0.17088 validation_1-rmse:0.12371 +[70] validation_0-rmse:0.17038 validation_1-rmse:0.12298 +[71] validation_0-rmse:0.17009 validation_1-rmse:0.12235 +[72] validation_0-rmse:0.16979 validation_1-rmse:0.12172 +[73] validation_0-rmse:0.16934 validation_1-rmse:0.12118 +[74] validation_0-rmse:0.16902 validation_1-rmse:0.12050 +[75] validation_0-rmse:0.16881 validation_1-rmse:0.11988 +[76] validation_0-rmse:0.16846 validation_1-rmse:0.11928 +[77] validation_0-rmse:0.16809 validation_1-rmse:0.11846 +[78] validation_0-rmse:0.16774 validation_1-rmse:0.11791 +[79] validation_0-rmse:0.16745 validation_1-rmse:0.11738 +[80] validation_0-rmse:0.16717 validation_1-rmse:0.11683 +[81] validation_0-rmse:0.16702 validation_1-rmse:0.11599 +[82] validation_0-rmse:0.16677 validation_1-rmse:0.11535 +[83] validation_0-rmse:0.16649 validation_1-rmse:0.11468 +[84] validation_0-rmse:0.16605 validation_1-rmse:0.11415 +[85] validation_0-rmse:0.16591 validation_1-rmse:0.11350 +[86] validation_0-rmse:0.16560 validation_1-rmse:0.11303 +[87] validation_0-rmse:0.16531 validation_1-rmse:0.11259 +[88] validation_0-rmse:0.16504 validation_1-rmse:0.11185 +[89] validation_0-rmse:0.16485 validation_1-rmse:0.11134 +[90] validation_0-rmse:0.16463 validation_1-rmse:0.11083 +[91] validation_0-rmse:0.16436 validation_1-rmse:0.11041 +[92] validation_0-rmse:0.16412 validation_1-rmse:0.10988 +[93] validation_0-rmse:0.16388 validation_1-rmse:0.10942 +[94] validation_0-rmse:0.16391 validation_1-rmse:0.10881 +[95] validation_0-rmse:0.16357 validation_1-rmse:0.10838 +[96] validation_0-rmse:0.16358 validation_1-rmse:0.10796 +[97] validation_0-rmse:0.16338 validation_1-rmse:0.10756 +[98] validation_0-rmse:0.16339 validation_1-rmse:0.10688 +[99] validation_0-rmse:0.16321 validation_1-rmse:0.10649 +2025-04-29 01:55:52,741 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.61 secs) -------------------- +2025-04-29 01:55:52,742 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:53,285 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:55:53,286 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1742601600_prediction.feather +2025-04-29 01:55:53,291 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,298 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:53,300 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:53,309 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,316 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:53,318 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:53,322 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1742601600/cb_btc_1742601600 +2025-04-29 01:55:53,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:53,339 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:53,340 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:55:58,184 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:58,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26992 validation_1-rmse:0.26756 +[1] validation_0-rmse:0.26551 validation_1-rmse:0.26201 +[2] validation_0-rmse:0.26111 validation_1-rmse:0.25656 +[3] validation_0-rmse:0.25690 validation_1-rmse:0.25154 +[4] validation_0-rmse:0.25291 validation_1-rmse:0.24683 +[5] validation_0-rmse:0.24933 validation_1-rmse:0.24228 +[6] validation_0-rmse:0.24598 validation_1-rmse:0.23796 +[7] validation_0-rmse:0.24252 validation_1-rmse:0.23392 +[8] validation_0-rmse:0.23953 validation_1-rmse:0.22978 +[9] validation_0-rmse:0.23634 validation_1-rmse:0.22592 +[10] validation_0-rmse:0.23330 validation_1-rmse:0.22229 +[11] validation_0-rmse:0.23059 validation_1-rmse:0.21875 +[12] validation_0-rmse:0.22799 validation_1-rmse:0.21546 +[13] validation_0-rmse:0.22565 validation_1-rmse:0.21212 +[14] validation_0-rmse:0.22329 validation_1-rmse:0.20904 +[15] validation_0-rmse:0.22111 validation_1-rmse:0.20604 +[16] validation_0-rmse:0.21894 validation_1-rmse:0.20318 +[17] validation_0-rmse:0.21715 validation_1-rmse:0.20021 +[18] validation_0-rmse:0.21499 validation_1-rmse:0.19735 +[19] validation_0-rmse:0.21283 validation_1-rmse:0.19480 +[20] validation_0-rmse:0.21109 validation_1-rmse:0.19209 +[21] validation_0-rmse:0.20904 validation_1-rmse:0.18969 +[22] validation_0-rmse:0.20762 validation_1-rmse:0.18718 +[23] validation_0-rmse:0.20580 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.20434 validation_1-rmse:0.18262 +[25] validation_0-rmse:0.20267 validation_1-rmse:0.18048 +[26] validation_0-rmse:0.20106 validation_1-rmse:0.17844 +[27] validation_0-rmse:0.19945 validation_1-rmse:0.17647 +[28] validation_0-rmse:0.19813 validation_1-rmse:0.17443 +[29] validation_0-rmse:0.19669 validation_1-rmse:0.17264 +[30] validation_0-rmse:0.19541 validation_1-rmse:0.17054 +[31] validation_0-rmse:0.19401 validation_1-rmse:0.16881 +[32] validation_0-rmse:0.19263 validation_1-rmse:0.16719 +[33] validation_0-rmse:0.19134 validation_1-rmse:0.16560 +[34] validation_0-rmse:0.18996 validation_1-rmse:0.16365 +[35] validation_0-rmse:0.18864 validation_1-rmse:0.16211 +[36] validation_0-rmse:0.18752 validation_1-rmse:0.16069 +[37] validation_0-rmse:0.18652 validation_1-rmse:0.15898 +[38] validation_0-rmse:0.18540 validation_1-rmse:0.15751 +[39] validation_0-rmse:0.18429 validation_1-rmse:0.15616 +[40] validation_0-rmse:0.18317 validation_1-rmse:0.15475 +[41] validation_0-rmse:0.18215 validation_1-rmse:0.15324 +[42] validation_0-rmse:0.18119 validation_1-rmse:0.15199 +[43] validation_0-rmse:0.18008 validation_1-rmse:0.15057 +[44] validation_0-rmse:0.17926 validation_1-rmse:0.14942 +[45] validation_0-rmse:0.17841 validation_1-rmse:0.14813 +[46] validation_0-rmse:0.17755 validation_1-rmse:0.14700 +[47] validation_0-rmse:0.17672 validation_1-rmse:0.14572 +[48] validation_0-rmse:0.17586 validation_1-rmse:0.14466 +[49] validation_0-rmse:0.17511 validation_1-rmse:0.14354 +[50] validation_0-rmse:0.17440 validation_1-rmse:0.14236 +[51] validation_0-rmse:0.17354 validation_1-rmse:0.14130 +[52] validation_0-rmse:0.17281 validation_1-rmse:0.14035 +[53] validation_0-rmse:0.17210 validation_1-rmse:0.13942 +[54] validation_0-rmse:0.17136 validation_1-rmse:0.13843 +[55] validation_0-rmse:0.17045 validation_1-rmse:0.13715 +[56] validation_0-rmse:0.16971 validation_1-rmse:0.13629 +[57] validation_0-rmse:0.16900 validation_1-rmse:0.13511 +[58] validation_0-rmse:0.16834 validation_1-rmse:0.13426 +[59] validation_0-rmse:0.16763 validation_1-rmse:0.13323 +[60] validation_0-rmse:0.16702 validation_1-rmse:0.13242 +[61] validation_0-rmse:0.16639 validation_1-rmse:0.13164 +[62] validation_0-rmse:0.16586 validation_1-rmse:0.13079 +[63] validation_0-rmse:0.16527 validation_1-rmse:0.13006 +[64] validation_0-rmse:0.16458 validation_1-rmse:0.12914 +[65] validation_0-rmse:0.16396 validation_1-rmse:0.12841 +[66] validation_0-rmse:0.16332 validation_1-rmse:0.12742 +[67] validation_0-rmse:0.16290 validation_1-rmse:0.12665 +[68] validation_0-rmse:0.16248 validation_1-rmse:0.12584 +[69] validation_0-rmse:0.16192 validation_1-rmse:0.12503 +[70] validation_0-rmse:0.16128 validation_1-rmse:0.12435 +[71] validation_0-rmse:0.16078 validation_1-rmse:0.12371 +[72] validation_0-rmse:0.16032 validation_1-rmse:0.12311 +[73] validation_0-rmse:0.15998 validation_1-rmse:0.12241 +[74] validation_0-rmse:0.15959 validation_1-rmse:0.12184 +[75] validation_0-rmse:0.15922 validation_1-rmse:0.12121 +[76] validation_0-rmse:0.15877 validation_1-rmse:0.12064 +[77] validation_0-rmse:0.15830 validation_1-rmse:0.11981 +[78] validation_0-rmse:0.15791 validation_1-rmse:0.11927 +[79] validation_0-rmse:0.15751 validation_1-rmse:0.11859 +[80] validation_0-rmse:0.15716 validation_1-rmse:0.11795 +[81] validation_0-rmse:0.15680 validation_1-rmse:0.11740 +[82] validation_0-rmse:0.15624 validation_1-rmse:0.11683 +[83] validation_0-rmse:0.15578 validation_1-rmse:0.11632 +[84] validation_0-rmse:0.15553 validation_1-rmse:0.11586 +[85] validation_0-rmse:0.15471 validation_1-rmse:0.11513 +[86] validation_0-rmse:0.15444 validation_1-rmse:0.11465 +[87] validation_0-rmse:0.15417 validation_1-rmse:0.11406 +[88] validation_0-rmse:0.15387 validation_1-rmse:0.11359 +[89] validation_0-rmse:0.15359 validation_1-rmse:0.11319 +[90] validation_0-rmse:0.15332 validation_1-rmse:0.11269 +[91] validation_0-rmse:0.15301 validation_1-rmse:0.11221 +[92] validation_0-rmse:0.15258 validation_1-rmse:0.11176 +[93] validation_0-rmse:0.15231 validation_1-rmse:0.11135 +[94] validation_0-rmse:0.15202 validation_1-rmse:0.11093 +[95] validation_0-rmse:0.15185 validation_1-rmse:0.11041 +[96] validation_0-rmse:0.15173 validation_1-rmse:0.11000 +[97] validation_0-rmse:0.15150 validation_1-rmse:0.10961 +[98] validation_0-rmse:0.15114 validation_1-rmse:0.10917 +[99] validation_0-rmse:0.15096 validation_1-rmse:0.10882 +2025-04-29 01:55:59,097 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:55:59,098 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:59,706 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:55:59,706 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather +2025-04-29 01:55:59,715 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,723 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:59,725 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:59,732 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,739 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:55:59,741 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:55:59,745 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-04-29 01:55:59,746 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:59,762 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:59,762 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.28468 validation_1-rmse:0.28491 +[1] validation_0-rmse:0.27961 validation_1-rmse:0.27914 +[2] validation_0-rmse:0.27524 validation_1-rmse:0.27370 +[3] validation_0-rmse:0.27054 validation_1-rmse:0.26858 +[4] validation_0-rmse:0.26601 validation_1-rmse:0.26362 +[5] validation_0-rmse:0.26219 validation_1-rmse:0.25896 +[6] validation_0-rmse:0.25814 validation_1-rmse:0.25450 +[7] validation_0-rmse:0.25500 validation_1-rmse:0.25026 +[8] validation_0-rmse:0.25145 validation_1-rmse:0.24602 +[9] validation_0-rmse:0.24843 validation_1-rmse:0.24213 +[10] validation_0-rmse:0.24527 validation_1-rmse:0.23824 +[11] validation_0-rmse:0.24238 validation_1-rmse:0.23440 +[12] validation_0-rmse:0.23940 validation_1-rmse:0.23096 +[13] validation_0-rmse:0.23630 validation_1-rmse:0.22764 +[14] validation_0-rmse:0.23385 validation_1-rmse:0.22440 +[15] validation_0-rmse:0.23099 validation_1-rmse:0.22128 +[16] validation_0-rmse:0.22865 validation_1-rmse:0.21801 +[17] validation_0-rmse:0.22620 validation_1-rmse:0.21515 +[18] validation_0-rmse:0.22375 validation_1-rmse:0.21210 +[19] validation_0-rmse:0.22142 validation_1-rmse:0.20925 +[20] validation_0-rmse:0.21927 validation_1-rmse:0.20663 +[21] validation_0-rmse:0.21720 validation_1-rmse:0.20416 +[22] validation_0-rmse:0.21528 validation_1-rmse:0.20170 +[23] validation_0-rmse:0.21330 validation_1-rmse:0.19913 +[24] validation_0-rmse:0.21136 validation_1-rmse:0.19693 +[25] validation_0-rmse:0.21002 validation_1-rmse:0.19438 +[26] validation_0-rmse:0.20807 validation_1-rmse:0.19222 +[27] validation_0-rmse:0.20636 validation_1-rmse:0.19016 +[28] validation_0-rmse:0.20439 validation_1-rmse:0.18763 +[29] validation_0-rmse:0.20276 validation_1-rmse:0.18559 +[30] validation_0-rmse:0.20114 validation_1-rmse:0.18380 +[31] validation_0-rmse:0.19965 validation_1-rmse:0.18163 +[32] validation_0-rmse:0.19833 validation_1-rmse:0.17955 +[33] validation_0-rmse:0.19688 validation_1-rmse:0.17782 +[34] validation_0-rmse:0.19558 validation_1-rmse:0.17614 +[35] validation_0-rmse:0.19420 validation_1-rmse:0.17451 +[36] validation_0-rmse:0.19297 validation_1-rmse:0.17293 +[37] validation_0-rmse:0.19169 validation_1-rmse:0.17111 +[38] validation_0-rmse:0.19038 validation_1-rmse:0.16943 +[39] validation_0-rmse:0.18941 validation_1-rmse:0.16798 +[40] validation_0-rmse:0.18828 validation_1-rmse:0.16657 +[41] validation_0-rmse:0.18724 validation_1-rmse:0.16485 +[42] validation_0-rmse:0.18620 validation_1-rmse:0.16347 +[43] validation_0-rmse:0.18525 validation_1-rmse:0.16204 +[44] validation_0-rmse:0.18429 validation_1-rmse:0.16073 +[45] validation_0-rmse:0.18324 validation_1-rmse:0.15951 +[46] validation_0-rmse:0.18250 validation_1-rmse:0.15797 +[47] validation_0-rmse:0.18157 validation_1-rmse:0.15682 +[48] validation_0-rmse:0.18069 validation_1-rmse:0.15566 +[49] validation_0-rmse:0.18002 validation_1-rmse:0.15440 +[50] validation_0-rmse:0.17914 validation_1-rmse:0.15322 +[51] validation_0-rmse:0.17842 validation_1-rmse:0.15220 +[52] validation_0-rmse:0.17756 validation_1-rmse:0.15107 +[53] validation_0-rmse:0.17668 validation_1-rmse:0.15007 +[54] validation_0-rmse:0.17596 validation_1-rmse:0.14866 +[55] validation_0-rmse:0.17525 validation_1-rmse:0.14775 +[56] validation_0-rmse:0.17467 validation_1-rmse:0.14653 +[57] validation_0-rmse:0.17390 validation_1-rmse:0.14564 +[58] validation_0-rmse:0.17326 validation_1-rmse:0.14478 +[59] validation_0-rmse:0.17273 validation_1-rmse:0.14356 +[60] validation_0-rmse:0.17218 validation_1-rmse:0.14269 +[61] validation_0-rmse:0.17157 validation_1-rmse:0.14186 +[62] validation_0-rmse:0.17120 validation_1-rmse:0.14083 +[63] validation_0-rmse:0.17069 validation_1-rmse:0.14002 +[64] validation_0-rmse:0.17012 validation_1-rmse:0.13912 +[65] validation_0-rmse:0.16942 validation_1-rmse:0.13834 +[66] validation_0-rmse:0.16914 validation_1-rmse:0.13720 +[67] validation_0-rmse:0.16856 validation_1-rmse:0.13648 +[68] validation_0-rmse:0.16800 validation_1-rmse:0.13569 +[69] validation_0-rmse:0.16796 validation_1-rmse:0.13472 +[70] validation_0-rmse:0.16737 validation_1-rmse:0.13405 +[71] validation_0-rmse:0.16686 validation_1-rmse:0.13342 +[72] validation_0-rmse:0.16639 validation_1-rmse:0.13270 +[73] validation_0-rmse:0.16648 validation_1-rmse:0.13149 +[74] validation_0-rmse:0.16609 validation_1-rmse:0.13086 +[75] validation_0-rmse:0.16560 validation_1-rmse:0.13025 +[76] validation_0-rmse:0.16530 validation_1-rmse:0.12925 +[77] validation_0-rmse:0.16492 validation_1-rmse:0.12824 +[78] validation_0-rmse:0.16451 validation_1-rmse:0.12770 +[79] validation_0-rmse:0.16414 validation_1-rmse:0.12710 +[80] validation_0-rmse:0.16377 validation_1-rmse:0.12654 +[81] validation_0-rmse:0.16338 validation_1-rmse:0.12595 +[82] validation_0-rmse:0.16317 validation_1-rmse:0.12490 +[83] validation_0-rmse:0.16249 validation_1-rmse:0.12361 +[84] validation_0-rmse:0.16217 validation_1-rmse:0.12307 +[85] validation_0-rmse:0.16178 validation_1-rmse:0.12255 +[86] validation_0-rmse:0.16149 validation_1-rmse:0.12206 +[87] validation_0-rmse:0.16113 validation_1-rmse:0.12155 +[88] validation_0-rmse:0.16049 validation_1-rmse:0.12061 +[89] validation_0-rmse:0.16008 validation_1-rmse:0.11990 +[90] validation_0-rmse:0.15955 validation_1-rmse:0.11882 +[91] validation_0-rmse:0.15927 validation_1-rmse:0.11842 +[92] validation_0-rmse:0.15891 validation_1-rmse:0.11796 +[93] validation_0-rmse:0.15880 validation_1-rmse:0.11730 +[94] validation_0-rmse:0.15829 validation_1-rmse:0.11631 +[95] validation_0-rmse:0.15809 validation_1-rmse:0.11584 +[96] validation_0-rmse:0.15778 validation_1-rmse:0.11544 +[97] validation_0-rmse:0.15763 validation_1-rmse:0.11504 +[98] validation_0-rmse:0.15724 validation_1-rmse:0.11438 +[99] validation_0-rmse:0.15694 validation_1-rmse:0.11396 +2025-04-29 01:56:05,520 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:56:05,521 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:06,027 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:56:06,027 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather +2025-04-29 01:56:06,037 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,045 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:56:06,046 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:56:06,057 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,064 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:56:06,065 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.093162 +1 0.003285 50.093162 +2 0.001898 50.093162 +3 0.000484 50.093162 +4 0.001688 50.093162 +2025-04-29 01:56:06,070 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-04-29 01:56:06,071 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:56:06,087 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:06,088 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:56:10,904 - datasieve.pipeline - INFO - DI tossed 2001 predictions for being too far from training data. +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.32950 validation_1-rmse:0.29220 +[1] validation_0-rmse:0.32402 validation_1-rmse:0.28580 +[2] validation_0-rmse:0.31922 validation_1-rmse:0.27974 +[3] validation_0-rmse:0.31450 validation_1-rmse:0.27409 +[4] validation_0-rmse:0.30969 validation_1-rmse:0.26866 +[5] validation_0-rmse:0.30585 validation_1-rmse:0.26346 +[6] validation_0-rmse:0.30202 validation_1-rmse:0.25855 +[7] validation_0-rmse:0.29888 validation_1-rmse:0.25375 +[8] validation_0-rmse:0.29520 validation_1-rmse:0.24919 +[9] validation_0-rmse:0.29164 validation_1-rmse:0.24487 +[10] validation_0-rmse:0.28843 validation_1-rmse:0.24072 +[11] validation_0-rmse:0.28514 validation_1-rmse:0.23667 +[12] validation_0-rmse:0.28114 validation_1-rmse:0.23279 +[13] validation_0-rmse:0.27740 validation_1-rmse:0.22909 +[14] validation_0-rmse:0.27421 validation_1-rmse:0.22543 +[15] validation_0-rmse:0.27115 validation_1-rmse:0.22210 +[16] validation_0-rmse:0.26820 validation_1-rmse:0.21859 +[17] validation_0-rmse:0.26549 validation_1-rmse:0.21528 +[18] validation_0-rmse:0.26254 validation_1-rmse:0.21226 +[19] validation_0-rmse:0.25967 validation_1-rmse:0.20927 +[20] validation_0-rmse:0.25735 validation_1-rmse:0.20641 +[21] validation_0-rmse:0.25470 validation_1-rmse:0.20366 +[22] validation_0-rmse:0.25265 validation_1-rmse:0.20073 +[23] validation_0-rmse:0.25054 validation_1-rmse:0.19819 +[24] validation_0-rmse:0.24806 validation_1-rmse:0.19573 +[25] validation_0-rmse:0.24570 validation_1-rmse:0.19304 +[26] validation_0-rmse:0.24361 validation_1-rmse:0.19076 +[27] validation_0-rmse:0.24148 validation_1-rmse:0.18853 +[28] validation_0-rmse:0.24014 validation_1-rmse:0.18621 +[29] validation_0-rmse:0.23792 validation_1-rmse:0.18410 +[30] validation_0-rmse:0.23603 validation_1-rmse:0.18203 +[31] validation_0-rmse:0.23421 validation_1-rmse:0.17990 +[32] validation_0-rmse:0.23264 validation_1-rmse:0.17800 +[33] validation_0-rmse:0.23087 validation_1-rmse:0.17616 +[34] validation_0-rmse:0.22949 validation_1-rmse:0.17427 +[35] validation_0-rmse:0.22857 validation_1-rmse:0.17234 +[36] validation_0-rmse:0.22690 validation_1-rmse:0.17065 +[37] validation_0-rmse:0.22566 validation_1-rmse:0.16898 +[38] validation_0-rmse:0.22462 validation_1-rmse:0.16738 +[39] validation_0-rmse:0.22376 validation_1-rmse:0.16567 +[40] validation_0-rmse:0.22232 validation_1-rmse:0.16410 +[41] validation_0-rmse:0.22105 validation_1-rmse:0.16265 +[42] validation_0-rmse:0.22006 validation_1-rmse:0.16111 +[43] validation_0-rmse:0.21847 validation_1-rmse:0.15976 +[44] validation_0-rmse:0.21782 validation_1-rmse:0.15824 +[45] validation_0-rmse:0.21641 validation_1-rmse:0.15686 +[46] validation_0-rmse:0.21552 validation_1-rmse:0.15554 +[47] validation_0-rmse:0.21459 validation_1-rmse:0.15417 +[48] validation_0-rmse:0.21339 validation_1-rmse:0.15293 +[49] validation_0-rmse:0.21255 validation_1-rmse:0.15176 +[50] validation_0-rmse:0.21192 validation_1-rmse:0.15047 +[51] validation_0-rmse:0.21115 validation_1-rmse:0.14910 +[52] validation_0-rmse:0.21072 validation_1-rmse:0.14774 +[53] validation_0-rmse:0.20992 validation_1-rmse:0.14670 +[54] validation_0-rmse:0.20839 validation_1-rmse:0.14541 +[55] validation_0-rmse:0.20753 validation_1-rmse:0.14442 +[56] validation_0-rmse:0.20648 validation_1-rmse:0.14328 +[57] validation_0-rmse:0.20564 validation_1-rmse:0.14229 +[58] validation_0-rmse:0.20473 validation_1-rmse:0.14137 +[59] validation_0-rmse:0.20418 validation_1-rmse:0.14011 +[60] validation_0-rmse:0.20341 validation_1-rmse:0.13923 +[61] validation_0-rmse:0.20258 validation_1-rmse:0.13839 +[62] validation_0-rmse:0.20230 validation_1-rmse:0.13723 +[63] validation_0-rmse:0.20075 validation_1-rmse:0.13546 +[64] validation_0-rmse:0.20007 validation_1-rmse:0.13467 +[65] validation_0-rmse:0.19937 validation_1-rmse:0.13387 +[66] validation_0-rmse:0.19875 validation_1-rmse:0.13296 +[67] validation_0-rmse:0.19709 validation_1-rmse:0.13137 +[68] validation_0-rmse:0.19675 validation_1-rmse:0.13042 +[69] validation_0-rmse:0.19617 validation_1-rmse:0.12968 +[70] validation_0-rmse:0.19560 validation_1-rmse:0.12900 +[71] validation_0-rmse:0.19492 validation_1-rmse:0.12834 +[72] validation_0-rmse:0.19319 validation_1-rmse:0.12681 +[73] validation_0-rmse:0.19272 validation_1-rmse:0.12612 +[74] validation_0-rmse:0.19230 validation_1-rmse:0.12535 +[75] validation_0-rmse:0.19170 validation_1-rmse:0.12474 +[76] validation_0-rmse:0.19058 validation_1-rmse:0.12338 +[77] validation_0-rmse:0.19010 validation_1-rmse:0.12279 +[78] validation_0-rmse:0.18961 validation_1-rmse:0.12223 +[79] validation_0-rmse:0.18960 validation_1-rmse:0.12156 +[80] validation_0-rmse:0.18882 validation_1-rmse:0.12038 +[81] validation_0-rmse:0.18819 validation_1-rmse:0.11975 +[82] validation_0-rmse:0.18789 validation_1-rmse:0.11916 +[83] validation_0-rmse:0.18738 validation_1-rmse:0.11864 +[84] validation_0-rmse:0.18718 validation_1-rmse:0.11801 +[85] validation_0-rmse:0.18600 validation_1-rmse:0.11698 +[86] validation_0-rmse:0.18572 validation_1-rmse:0.11653 +[87] validation_0-rmse:0.18534 validation_1-rmse:0.11603 +[88] validation_0-rmse:0.18478 validation_1-rmse:0.11508 +[89] validation_0-rmse:0.18430 validation_1-rmse:0.11459 +[90] validation_0-rmse:0.18447 validation_1-rmse:0.11396 +[91] validation_0-rmse:0.18424 validation_1-rmse:0.11352 +[92] validation_0-rmse:0.18367 validation_1-rmse:0.11307 +[93] validation_0-rmse:0.18333 validation_1-rmse:0.11265 +[94] validation_0-rmse:0.18313 validation_1-rmse:0.11216 +[95] validation_0-rmse:0.18275 validation_1-rmse:0.11157 +[96] validation_0-rmse:0.18275 validation_1-rmse:0.11106 +[97] validation_0-rmse:0.18248 validation_1-rmse:0.11068 +[98] validation_0-rmse:0.18220 validation_1-rmse:0.11033 +[99] validation_0-rmse:0.18198 validation_1-rmse:0.10994 +2025-04-29 01:56:11,705 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.63 secs) -------------------- +2025-04-29 01:56:11,706 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:12,255 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:56:12,275 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +1 33535 +0 33236 +2025-04-29 01:56:12,276 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 35773 +1.0 30998 +2025-04-29 01:56:12,279 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-04-29 01:56:12,281 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:56:12,282 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:56:12,283 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1735689600_prediction.feather +2025-04-29 01:56:12,334 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:56:12,335 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,518 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:56:12,519 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:13,040 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,046 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:56:13,047 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.58814 +1 0.003044 49.58814 +2 0.000465 49.58814 +3 -0.000380 49.58814 +4 0.002829 49.58814 +2025-04-29 01:56:13,052 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,057 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:13,059 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:13,066 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1735689600/cb_sol_1735689600 +2025-04-29 01:56:13,066 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:13,095 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:13,096 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30164 validation_1-rmse:0.29585 +[1] validation_0-rmse:0.29609 validation_1-rmse:0.28921 +[2] validation_0-rmse:0.29103 validation_1-rmse:0.28298 +[3] validation_0-rmse:0.28604 validation_1-rmse:0.27706 +[4] validation_0-rmse:0.28108 validation_1-rmse:0.27129 +[5] validation_0-rmse:0.27670 validation_1-rmse:0.26609 +[6] validation_0-rmse:0.27234 validation_1-rmse:0.26092 +[7] validation_0-rmse:0.26874 validation_1-rmse:0.25593 +[8] validation_0-rmse:0.26461 validation_1-rmse:0.25118 +[9] validation_0-rmse:0.26074 validation_1-rmse:0.24677 +[10] validation_0-rmse:0.25745 validation_1-rmse:0.24239 +[11] validation_0-rmse:0.25460 validation_1-rmse:0.23832 +[12] validation_0-rmse:0.25121 validation_1-rmse:0.23441 +[13] validation_0-rmse:0.24825 validation_1-rmse:0.23068 +[14] validation_0-rmse:0.24580 validation_1-rmse:0.22694 +[15] validation_0-rmse:0.24286 validation_1-rmse:0.22346 +[16] validation_0-rmse:0.24051 validation_1-rmse:0.22006 +[17] validation_0-rmse:0.23821 validation_1-rmse:0.21690 +[18] validation_0-rmse:0.23549 validation_1-rmse:0.21383 +[19] validation_0-rmse:0.23335 validation_1-rmse:0.21087 +[20] validation_0-rmse:0.23089 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22918 validation_1-rmse:0.20505 +[22] validation_0-rmse:0.22716 validation_1-rmse:0.20240 +[23] validation_0-rmse:0.22562 validation_1-rmse:0.19981 +[24] validation_0-rmse:0.22385 validation_1-rmse:0.19723 +[25] validation_0-rmse:0.22201 validation_1-rmse:0.19473 +[26] validation_0-rmse:0.22016 validation_1-rmse:0.19245 +[27] validation_0-rmse:0.21834 validation_1-rmse:0.19024 +[28] validation_0-rmse:0.21671 validation_1-rmse:0.18789 +[29] validation_0-rmse:0.21493 validation_1-rmse:0.18579 +[30] validation_0-rmse:0.21385 validation_1-rmse:0.18351 +[31] validation_0-rmse:0.21216 validation_1-rmse:0.18156 +[32] validation_0-rmse:0.21088 validation_1-rmse:0.17941 +[33] validation_0-rmse:0.20953 validation_1-rmse:0.17754 +[34] validation_0-rmse:0.20805 validation_1-rmse:0.17575 +[35] validation_0-rmse:0.20648 validation_1-rmse:0.17399 +[36] validation_0-rmse:0.20515 validation_1-rmse:0.17220 +[37] validation_0-rmse:0.20382 validation_1-rmse:0.17031 +[38] validation_0-rmse:0.20257 validation_1-rmse:0.16871 +[39] validation_0-rmse:0.20125 validation_1-rmse:0.16718 +[40] validation_0-rmse:0.20005 validation_1-rmse:0.16574 +[41] validation_0-rmse:0.19885 validation_1-rmse:0.16415 +[42] validation_0-rmse:0.19789 validation_1-rmse:0.16270 +[43] validation_0-rmse:0.19680 validation_1-rmse:0.16130 +[44] validation_0-rmse:0.19564 validation_1-rmse:0.15993 +[45] validation_0-rmse:0.19480 validation_1-rmse:0.15854 +[46] validation_0-rmse:0.19376 validation_1-rmse:0.15728 +[47] validation_0-rmse:0.19290 validation_1-rmse:0.15568 +[48] validation_0-rmse:0.19223 validation_1-rmse:0.15445 +[49] validation_0-rmse:0.19129 validation_1-rmse:0.15330 +[50] validation_0-rmse:0.19035 validation_1-rmse:0.15194 +[51] validation_0-rmse:0.18948 validation_1-rmse:0.15082 +[52] validation_0-rmse:0.18882 validation_1-rmse:0.14945 +[53] validation_0-rmse:0.18801 validation_1-rmse:0.14840 +[54] validation_0-rmse:0.18707 validation_1-rmse:0.14736 +[55] validation_0-rmse:0.18637 validation_1-rmse:0.14635 +[56] validation_0-rmse:0.18571 validation_1-rmse:0.14542 +[57] validation_0-rmse:0.18497 validation_1-rmse:0.14413 +[58] validation_0-rmse:0.18443 validation_1-rmse:0.14297 +[59] validation_0-rmse:0.18375 validation_1-rmse:0.14203 +[60] validation_0-rmse:0.18319 validation_1-rmse:0.14111 +[61] validation_0-rmse:0.18266 validation_1-rmse:0.14030 +[62] validation_0-rmse:0.18185 validation_1-rmse:0.13914 +[63] validation_0-rmse:0.18145 validation_1-rmse:0.13831 +[64] validation_0-rmse:0.18135 validation_1-rmse:0.13720 +[65] validation_0-rmse:0.18075 validation_1-rmse:0.13643 +[66] validation_0-rmse:0.18020 validation_1-rmse:0.13560 +[67] validation_0-rmse:0.17951 validation_1-rmse:0.13485 +[68] validation_0-rmse:0.17888 validation_1-rmse:0.13414 +[69] validation_0-rmse:0.17850 validation_1-rmse:0.13343 +[70] validation_0-rmse:0.17798 validation_1-rmse:0.13224 +[71] validation_0-rmse:0.17751 validation_1-rmse:0.13133 +[72] validation_0-rmse:0.17711 validation_1-rmse:0.13062 +[73] validation_0-rmse:0.17701 validation_1-rmse:0.12966 +[74] validation_0-rmse:0.17648 validation_1-rmse:0.12872 +[75] validation_0-rmse:0.17611 validation_1-rmse:0.12806 +[76] validation_0-rmse:0.17573 validation_1-rmse:0.12732 +[77] validation_0-rmse:0.17528 validation_1-rmse:0.12664 +[78] validation_0-rmse:0.17478 validation_1-rmse:0.12605 +[79] validation_0-rmse:0.17432 validation_1-rmse:0.12518 +[80] validation_0-rmse:0.17391 validation_1-rmse:0.12466 +[81] validation_0-rmse:0.17358 validation_1-rmse:0.12398 +[82] validation_0-rmse:0.17315 validation_1-rmse:0.12342 +[83] validation_0-rmse:0.17260 validation_1-rmse:0.12276 +[84] validation_0-rmse:0.17220 validation_1-rmse:0.12222 +[85] validation_0-rmse:0.17182 validation_1-rmse:0.12176 +[86] validation_0-rmse:0.17152 validation_1-rmse:0.12124 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12046 +[88] validation_0-rmse:0.17085 validation_1-rmse:0.11974 +[89] validation_0-rmse:0.17053 validation_1-rmse:0.11930 +[90] validation_0-rmse:0.17018 validation_1-rmse:0.11888 +[91] validation_0-rmse:0.17011 validation_1-rmse:0.11810 +[92] validation_0-rmse:0.16980 validation_1-rmse:0.11762 +[93] validation_0-rmse:0.16956 validation_1-rmse:0.11689 +[94] validation_0-rmse:0.16923 validation_1-rmse:0.11641 +[95] validation_0-rmse:0.16912 validation_1-rmse:0.11579 +[96] validation_0-rmse:0.16878 validation_1-rmse:0.11530 +[97] validation_0-rmse:0.16857 validation_1-rmse:0.11489 +[98] validation_0-rmse:0.16824 validation_1-rmse:0.11442 +[99] validation_0-rmse:0.16824 validation_1-rmse:0.11403 +2025-04-29 01:56:19,586 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.52 secs) -------------------- +2025-04-29 01:56:19,587 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:20,174 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:56:20,175 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1736553600_prediction.feather +2025-04-29 01:56:20,179 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,185 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:20,186 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:20,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,197 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:20,199 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:20,204 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1736553600/cb_sol_1736553600 +2025-04-29 01:56:20,205 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:20,227 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:20,228 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:56:25,109 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29597 validation_1-rmse:0.29016 +[1] validation_0-rmse:0.29075 validation_1-rmse:0.28391 +[2] validation_0-rmse:0.28602 validation_1-rmse:0.27798 +[3] validation_0-rmse:0.28062 validation_1-rmse:0.27213 +[4] validation_0-rmse:0.27647 validation_1-rmse:0.26682 +[5] validation_0-rmse:0.27188 validation_1-rmse:0.26144 +[6] validation_0-rmse:0.26781 validation_1-rmse:0.25655 +[7] validation_0-rmse:0.26412 validation_1-rmse:0.25180 +[8] validation_0-rmse:0.25994 validation_1-rmse:0.24709 +[9] validation_0-rmse:0.25649 validation_1-rmse:0.24277 +[10] validation_0-rmse:0.25332 validation_1-rmse:0.23850 +[11] validation_0-rmse:0.24999 validation_1-rmse:0.23452 +[12] validation_0-rmse:0.24687 validation_1-rmse:0.23072 +[13] validation_0-rmse:0.24432 validation_1-rmse:0.22694 +[14] validation_0-rmse:0.24128 validation_1-rmse:0.22341 +[15] validation_0-rmse:0.23869 validation_1-rmse:0.21969 +[16] validation_0-rmse:0.23628 validation_1-rmse:0.21635 +[17] validation_0-rmse:0.23354 validation_1-rmse:0.21326 +[18] validation_0-rmse:0.23123 validation_1-rmse:0.21007 +[19] validation_0-rmse:0.22919 validation_1-rmse:0.20707 +[20] validation_0-rmse:0.22705 validation_1-rmse:0.20418 +[21] validation_0-rmse:0.22505 validation_1-rmse:0.20149 +[22] validation_0-rmse:0.22285 validation_1-rmse:0.19887 +[23] validation_0-rmse:0.22084 validation_1-rmse:0.19631 +[24] validation_0-rmse:0.21877 validation_1-rmse:0.19389 +[25] validation_0-rmse:0.21748 validation_1-rmse:0.19133 +[26] validation_0-rmse:0.21557 validation_1-rmse:0.18870 +[27] validation_0-rmse:0.21374 validation_1-rmse:0.18648 +[28] validation_0-rmse:0.21183 validation_1-rmse:0.18432 +[29] validation_0-rmse:0.21047 validation_1-rmse:0.18209 +[30] validation_0-rmse:0.20873 validation_1-rmse:0.17990 +[31] validation_0-rmse:0.20717 validation_1-rmse:0.17795 +[32] validation_0-rmse:0.20564 validation_1-rmse:0.17599 +[33] validation_0-rmse:0.20428 validation_1-rmse:0.17421 +[34] validation_0-rmse:0.20290 validation_1-rmse:0.17229 +[35] validation_0-rmse:0.20161 validation_1-rmse:0.17047 +[36] validation_0-rmse:0.20018 validation_1-rmse:0.16878 +[37] validation_0-rmse:0.19923 validation_1-rmse:0.16688 +[38] validation_0-rmse:0.19796 validation_1-rmse:0.16534 +[39] validation_0-rmse:0.19668 validation_1-rmse:0.16355 +[40] validation_0-rmse:0.19543 validation_1-rmse:0.16204 +[41] validation_0-rmse:0.19441 validation_1-rmse:0.16062 +[42] validation_0-rmse:0.19344 validation_1-rmse:0.15910 +[43] validation_0-rmse:0.19256 validation_1-rmse:0.15759 +[44] validation_0-rmse:0.19154 validation_1-rmse:0.15625 +[45] validation_0-rmse:0.19048 validation_1-rmse:0.15494 +[46] validation_0-rmse:0.18937 validation_1-rmse:0.15366 +[47] validation_0-rmse:0.18865 validation_1-rmse:0.15236 +[48] validation_0-rmse:0.18784 validation_1-rmse:0.15112 +[49] validation_0-rmse:0.18704 validation_1-rmse:0.14998 +[50] validation_0-rmse:0.18625 validation_1-rmse:0.14874 +[51] validation_0-rmse:0.18541 validation_1-rmse:0.14763 +[52] validation_0-rmse:0.18456 validation_1-rmse:0.14659 +[53] validation_0-rmse:0.18383 validation_1-rmse:0.14530 +[54] validation_0-rmse:0.18315 validation_1-rmse:0.14420 +[55] validation_0-rmse:0.18234 validation_1-rmse:0.14321 +[56] validation_0-rmse:0.18181 validation_1-rmse:0.14206 +[57] validation_0-rmse:0.18109 validation_1-rmse:0.14106 +[58] validation_0-rmse:0.18033 validation_1-rmse:0.13996 +[59] validation_0-rmse:0.17964 validation_1-rmse:0.13905 +[60] validation_0-rmse:0.17921 validation_1-rmse:0.13820 +[61] validation_0-rmse:0.17865 validation_1-rmse:0.13731 +[62] validation_0-rmse:0.17795 validation_1-rmse:0.13648 +[63] validation_0-rmse:0.17737 validation_1-rmse:0.13559 +[64] validation_0-rmse:0.17680 validation_1-rmse:0.13483 +[65] validation_0-rmse:0.17628 validation_1-rmse:0.13408 +[66] validation_0-rmse:0.17588 validation_1-rmse:0.13303 +[67] validation_0-rmse:0.17530 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.17478 validation_1-rmse:0.13153 +[69] validation_0-rmse:0.17439 validation_1-rmse:0.13081 +[70] validation_0-rmse:0.17401 validation_1-rmse:0.12991 +[71] validation_0-rmse:0.17347 validation_1-rmse:0.12911 +[72] validation_0-rmse:0.17304 validation_1-rmse:0.12838 +[73] validation_0-rmse:0.17254 validation_1-rmse:0.12774 +[74] validation_0-rmse:0.17207 validation_1-rmse:0.12656 +[75] validation_0-rmse:0.17185 validation_1-rmse:0.12571 +[76] validation_0-rmse:0.17126 validation_1-rmse:0.12512 +[77] validation_0-rmse:0.17096 validation_1-rmse:0.12447 +[78] validation_0-rmse:0.17064 validation_1-rmse:0.12381 +[79] validation_0-rmse:0.17024 validation_1-rmse:0.12300 +[80] validation_0-rmse:0.16989 validation_1-rmse:0.12244 +[81] validation_0-rmse:0.16955 validation_1-rmse:0.12180 +[82] validation_0-rmse:0.16924 validation_1-rmse:0.12129 +[83] validation_0-rmse:0.16931 validation_1-rmse:0.12037 +[84] validation_0-rmse:0.16888 validation_1-rmse:0.11970 +[85] validation_0-rmse:0.16845 validation_1-rmse:0.11914 +[86] validation_0-rmse:0.16809 validation_1-rmse:0.11840 +[87] validation_0-rmse:0.16766 validation_1-rmse:0.11760 +[88] validation_0-rmse:0.16741 validation_1-rmse:0.11714 +[89] validation_0-rmse:0.16707 validation_1-rmse:0.11667 +[90] validation_0-rmse:0.16683 validation_1-rmse:0.11592 +[91] validation_0-rmse:0.16643 validation_1-rmse:0.11537 +[92] validation_0-rmse:0.16621 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.16611 validation_1-rmse:0.11396 +[94] validation_0-rmse:0.16587 validation_1-rmse:0.11350 +[95] validation_0-rmse:0.16563 validation_1-rmse:0.11308 +[96] validation_0-rmse:0.16535 validation_1-rmse:0.11237 +[97] validation_0-rmse:0.16487 validation_1-rmse:0.11173 +[98] validation_0-rmse:0.16461 validation_1-rmse:0.11133 +[99] validation_0-rmse:0.16437 validation_1-rmse:0.11096 +2025-04-29 01:56:26,510 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.30 secs) -------------------- +2025-04-29 01:56:26,511 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:27,072 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:56:27,073 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1737417600_prediction.feather +2025-04-29 01:56:27,079 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,085 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:27,086 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:27,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,100 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:27,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:27,108 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1737417600/cb_sol_1737417600 +2025-04-29 01:56:27,109 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:27,130 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:27,131 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:56:32,206 - datasieve.pipeline - INFO - DI tossed 1523 predictions for being too far from training data. +2025-04-29 01:56:32,209 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:32,210 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30838 validation_1-rmse:0.28356 +[1] validation_0-rmse:0.30280 validation_1-rmse:0.27752 +[2] validation_0-rmse:0.29759 validation_1-rmse:0.27179 +[3] validation_0-rmse:0.29330 validation_1-rmse:0.26614 +[4] validation_0-rmse:0.28936 validation_1-rmse:0.26091 +[5] validation_0-rmse:0.28544 validation_1-rmse:0.25581 +[6] validation_0-rmse:0.28151 validation_1-rmse:0.25102 +[7] validation_0-rmse:0.27790 validation_1-rmse:0.24636 +[8] validation_0-rmse:0.27429 validation_1-rmse:0.24196 +[9] validation_0-rmse:0.27104 validation_1-rmse:0.23770 +[10] validation_0-rmse:0.26762 validation_1-rmse:0.23356 +[11] validation_0-rmse:0.26472 validation_1-rmse:0.22966 +[12] validation_0-rmse:0.26219 validation_1-rmse:0.22601 +[13] validation_0-rmse:0.25924 validation_1-rmse:0.22234 +[14] validation_0-rmse:0.25634 validation_1-rmse:0.21888 +[15] validation_0-rmse:0.25379 validation_1-rmse:0.21545 +[16] validation_0-rmse:0.25117 validation_1-rmse:0.21221 +[17] validation_0-rmse:0.24877 validation_1-rmse:0.20902 +[18] validation_0-rmse:0.24653 validation_1-rmse:0.20604 +[19] validation_0-rmse:0.24404 validation_1-rmse:0.20315 +[20] validation_0-rmse:0.24194 validation_1-rmse:0.20032 +[21] validation_0-rmse:0.23966 validation_1-rmse:0.19765 +[22] validation_0-rmse:0.23804 validation_1-rmse:0.19481 +[23] validation_0-rmse:0.23599 validation_1-rmse:0.19230 +[24] validation_0-rmse:0.23384 validation_1-rmse:0.18993 +[25] validation_0-rmse:0.23196 validation_1-rmse:0.18756 +[26] validation_0-rmse:0.23057 validation_1-rmse:0.18506 +[27] validation_0-rmse:0.22854 validation_1-rmse:0.18283 +[28] validation_0-rmse:0.22705 validation_1-rmse:0.18071 +[29] validation_0-rmse:0.22557 validation_1-rmse:0.17851 +[30] validation_0-rmse:0.22394 validation_1-rmse:0.17644 +[31] validation_0-rmse:0.22213 validation_1-rmse:0.17452 +[32] validation_0-rmse:0.22064 validation_1-rmse:0.17267 +[33] validation_0-rmse:0.21905 validation_1-rmse:0.17084 +[34] validation_0-rmse:0.21806 validation_1-rmse:0.16880 +[35] validation_0-rmse:0.21693 validation_1-rmse:0.16700 +[36] validation_0-rmse:0.21537 validation_1-rmse:0.16520 +[37] validation_0-rmse:0.21417 validation_1-rmse:0.16362 +[38] validation_0-rmse:0.21282 validation_1-rmse:0.16204 +[39] validation_0-rmse:0.21137 validation_1-rmse:0.16047 +[40] validation_0-rmse:0.20994 validation_1-rmse:0.15897 +[41] validation_0-rmse:0.20878 validation_1-rmse:0.15747 +[42] validation_0-rmse:0.20766 validation_1-rmse:0.15604 +[43] validation_0-rmse:0.20666 validation_1-rmse:0.15444 +[44] validation_0-rmse:0.20566 validation_1-rmse:0.15316 +[45] validation_0-rmse:0.20496 validation_1-rmse:0.15162 +[46] validation_0-rmse:0.20394 validation_1-rmse:0.15038 +[47] validation_0-rmse:0.20277 validation_1-rmse:0.14909 +[48] validation_0-rmse:0.20176 validation_1-rmse:0.14793 +[49] validation_0-rmse:0.20072 validation_1-rmse:0.14681 +[50] validation_0-rmse:0.20058 validation_1-rmse:0.14528 +[51] validation_0-rmse:0.19970 validation_1-rmse:0.14419 +[52] validation_0-rmse:0.19887 validation_1-rmse:0.14284 +[53] validation_0-rmse:0.19809 validation_1-rmse:0.14182 +[54] validation_0-rmse:0.19725 validation_1-rmse:0.14076 +[55] validation_0-rmse:0.19636 validation_1-rmse:0.13981 +[56] validation_0-rmse:0.19615 validation_1-rmse:0.13853 +[57] validation_0-rmse:0.19540 validation_1-rmse:0.13757 +[58] validation_0-rmse:0.19460 validation_1-rmse:0.13664 +[59] validation_0-rmse:0.19418 validation_1-rmse:0.13553 +[60] validation_0-rmse:0.19382 validation_1-rmse:0.13445 +[61] validation_0-rmse:0.19302 validation_1-rmse:0.13363 +[62] validation_0-rmse:0.19218 validation_1-rmse:0.13270 +[63] validation_0-rmse:0.19154 validation_1-rmse:0.13183 +[64] validation_0-rmse:0.19083 validation_1-rmse:0.13105 +[65] validation_0-rmse:0.19005 validation_1-rmse:0.13008 +[66] validation_0-rmse:0.18929 validation_1-rmse:0.12932 +[67] validation_0-rmse:0.18885 validation_1-rmse:0.12851 +[68] validation_0-rmse:0.18837 validation_1-rmse:0.12781 +[69] validation_0-rmse:0.18790 validation_1-rmse:0.12711 +[70] validation_0-rmse:0.18732 validation_1-rmse:0.12617 +[71] validation_0-rmse:0.18682 validation_1-rmse:0.12552 +[72] validation_0-rmse:0.18669 validation_1-rmse:0.12448 +[73] validation_0-rmse:0.18617 validation_1-rmse:0.12382 +[74] validation_0-rmse:0.18587 validation_1-rmse:0.12322 +[75] validation_0-rmse:0.18544 validation_1-rmse:0.12261 +[76] validation_0-rmse:0.18524 validation_1-rmse:0.12162 +[77] validation_0-rmse:0.18486 validation_1-rmse:0.12098 +[78] validation_0-rmse:0.18443 validation_1-rmse:0.12021 +[79] validation_0-rmse:0.18415 validation_1-rmse:0.11963 +[80] validation_0-rmse:0.18393 validation_1-rmse:0.11866 +[81] validation_0-rmse:0.18344 validation_1-rmse:0.11809 +[82] validation_0-rmse:0.18307 validation_1-rmse:0.11748 +[83] validation_0-rmse:0.18257 validation_1-rmse:0.11699 +[84] validation_0-rmse:0.18216 validation_1-rmse:0.11643 +[85] validation_0-rmse:0.18188 validation_1-rmse:0.11595 +[86] validation_0-rmse:0.18168 validation_1-rmse:0.11502 +[87] validation_0-rmse:0.18148 validation_1-rmse:0.11451 +[88] validation_0-rmse:0.18093 validation_1-rmse:0.11378 +[89] validation_0-rmse:0.18054 validation_1-rmse:0.11332 +[90] validation_0-rmse:0.18024 validation_1-rmse:0.11285 +[91] validation_0-rmse:0.17982 validation_1-rmse:0.11241 +[92] validation_0-rmse:0.17950 validation_1-rmse:0.11185 +[93] validation_0-rmse:0.17918 validation_1-rmse:0.11123 +[94] validation_0-rmse:0.17882 validation_1-rmse:0.11072 +[95] validation_0-rmse:0.17881 validation_1-rmse:0.10986 +[96] validation_0-rmse:0.17832 validation_1-rmse:0.10941 +[97] validation_0-rmse:0.17800 validation_1-rmse:0.10897 +[98] validation_0-rmse:0.17774 validation_1-rmse:0.10859 +[99] validation_0-rmse:0.17746 validation_1-rmse:0.10819 +2025-04-29 01:56:33,558 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:33,558 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:34,118 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:56:34,119 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1738281600_prediction.feather +2025-04-29 01:56:34,124 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,130 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:34,131 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:34,137 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,143 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:34,144 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:34,149 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1738281600/cb_sol_1738281600 +2025-04-29 01:56:34,150 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:34,173 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:34,173 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29494 validation_1-rmse:0.28739 +[1] validation_0-rmse:0.28930 validation_1-rmse:0.28164 +[2] validation_0-rmse:0.28437 validation_1-rmse:0.27613 +[3] validation_0-rmse:0.27990 validation_1-rmse:0.27106 +[4] validation_0-rmse:0.27541 validation_1-rmse:0.26617 +[5] validation_0-rmse:0.27070 validation_1-rmse:0.26147 +[6] validation_0-rmse:0.26683 validation_1-rmse:0.25687 +[7] validation_0-rmse:0.26280 validation_1-rmse:0.25263 +[8] validation_0-rmse:0.25916 validation_1-rmse:0.24830 +[9] validation_0-rmse:0.25540 validation_1-rmse:0.24420 +[10] validation_0-rmse:0.25186 validation_1-rmse:0.24022 +[11] validation_0-rmse:0.24829 validation_1-rmse:0.23647 +[12] validation_0-rmse:0.24504 validation_1-rmse:0.23286 +[13] validation_0-rmse:0.24183 validation_1-rmse:0.22943 +[14] validation_0-rmse:0.23870 validation_1-rmse:0.22619 +[15] validation_0-rmse:0.23587 validation_1-rmse:0.22274 +[16] validation_0-rmse:0.23325 validation_1-rmse:0.21951 +[17] validation_0-rmse:0.23045 validation_1-rmse:0.21650 +[18] validation_0-rmse:0.22792 validation_1-rmse:0.21367 +[19] validation_0-rmse:0.22524 validation_1-rmse:0.21092 +[20] validation_0-rmse:0.22293 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22055 validation_1-rmse:0.20549 +[22] validation_0-rmse:0.21831 validation_1-rmse:0.20307 +[23] validation_0-rmse:0.21601 validation_1-rmse:0.20062 +[24] validation_0-rmse:0.21372 validation_1-rmse:0.19810 +[25] validation_0-rmse:0.21154 validation_1-rmse:0.19580 +[26] validation_0-rmse:0.20966 validation_1-rmse:0.19369 +[27] validation_0-rmse:0.20790 validation_1-rmse:0.19130 +[28] validation_0-rmse:0.20602 validation_1-rmse:0.18921 +[29] validation_0-rmse:0.20418 validation_1-rmse:0.18723 +[30] validation_0-rmse:0.20236 validation_1-rmse:0.18525 +[31] validation_0-rmse:0.20057 validation_1-rmse:0.18324 +[32] validation_0-rmse:0.19900 validation_1-rmse:0.18144 +[33] validation_0-rmse:0.19744 validation_1-rmse:0.17941 +[34] validation_0-rmse:0.19608 validation_1-rmse:0.17767 +[35] validation_0-rmse:0.19467 validation_1-rmse:0.17605 +[36] validation_0-rmse:0.19313 validation_1-rmse:0.17422 +[37] validation_0-rmse:0.19156 validation_1-rmse:0.17260 +[38] validation_0-rmse:0.19020 validation_1-rmse:0.17103 +[39] validation_0-rmse:0.18884 validation_1-rmse:0.16948 +[40] validation_0-rmse:0.18767 validation_1-rmse:0.16797 +[41] validation_0-rmse:0.18636 validation_1-rmse:0.16647 +[42] validation_0-rmse:0.18512 validation_1-rmse:0.16505 +[43] validation_0-rmse:0.18403 validation_1-rmse:0.16340 +[44] validation_0-rmse:0.18290 validation_1-rmse:0.16210 +[45] validation_0-rmse:0.18189 validation_1-rmse:0.16085 +[46] validation_0-rmse:0.18090 validation_1-rmse:0.15966 +[47] validation_0-rmse:0.17992 validation_1-rmse:0.15841 +[48] validation_0-rmse:0.17901 validation_1-rmse:0.15728 +[49] validation_0-rmse:0.17817 validation_1-rmse:0.15582 +[50] validation_0-rmse:0.17697 validation_1-rmse:0.15458 +[51] validation_0-rmse:0.17607 validation_1-rmse:0.15349 +[52] validation_0-rmse:0.17516 validation_1-rmse:0.15235 +[53] validation_0-rmse:0.17425 validation_1-rmse:0.15131 +[54] validation_0-rmse:0.17347 validation_1-rmse:0.15032 +[55] validation_0-rmse:0.17275 validation_1-rmse:0.14932 +[56] validation_0-rmse:0.17211 validation_1-rmse:0.14834 +[57] validation_0-rmse:0.17131 validation_1-rmse:0.14741 +[58] validation_0-rmse:0.17072 validation_1-rmse:0.14617 +[59] validation_0-rmse:0.16999 validation_1-rmse:0.14528 +[60] validation_0-rmse:0.16934 validation_1-rmse:0.14416 +[61] validation_0-rmse:0.16887 validation_1-rmse:0.14321 +[62] validation_0-rmse:0.16842 validation_1-rmse:0.14213 +[63] validation_0-rmse:0.16765 validation_1-rmse:0.14130 +[64] validation_0-rmse:0.16691 validation_1-rmse:0.14048 +[65] validation_0-rmse:0.16629 validation_1-rmse:0.13956 +[66] validation_0-rmse:0.16565 validation_1-rmse:0.13882 +[67] validation_0-rmse:0.16530 validation_1-rmse:0.13793 +[68] validation_0-rmse:0.16467 validation_1-rmse:0.13710 +[69] validation_0-rmse:0.16436 validation_1-rmse:0.13621 +[70] validation_0-rmse:0.16377 validation_1-rmse:0.13542 +[71] validation_0-rmse:0.16334 validation_1-rmse:0.13463 +[72] validation_0-rmse:0.16280 validation_1-rmse:0.13394 +[73] validation_0-rmse:0.16230 validation_1-rmse:0.13328 +[74] validation_0-rmse:0.16156 validation_1-rmse:0.13246 +[75] validation_0-rmse:0.16122 validation_1-rmse:0.13151 +[76] validation_0-rmse:0.16080 validation_1-rmse:0.13080 +[77] validation_0-rmse:0.16033 validation_1-rmse:0.13015 +[78] validation_0-rmse:0.15992 validation_1-rmse:0.12951 +[79] validation_0-rmse:0.15950 validation_1-rmse:0.12888 +[80] validation_0-rmse:0.15909 validation_1-rmse:0.12822 +[81] validation_0-rmse:0.15875 validation_1-rmse:0.12744 +[82] validation_0-rmse:0.15831 validation_1-rmse:0.12683 +[83] validation_0-rmse:0.15786 validation_1-rmse:0.12626 +[84] validation_0-rmse:0.15747 validation_1-rmse:0.12572 +[85] validation_0-rmse:0.15724 validation_1-rmse:0.12495 +[86] validation_0-rmse:0.15695 validation_1-rmse:0.12442 +[87] validation_0-rmse:0.15664 validation_1-rmse:0.12382 +[88] validation_0-rmse:0.15651 validation_1-rmse:0.12326 +[89] validation_0-rmse:0.15629 validation_1-rmse:0.12256 +[90] validation_0-rmse:0.15596 validation_1-rmse:0.12196 +[91] validation_0-rmse:0.15559 validation_1-rmse:0.12141 +[92] validation_0-rmse:0.15511 validation_1-rmse:0.12088 +[93] validation_0-rmse:0.15487 validation_1-rmse:0.12033 +[94] validation_0-rmse:0.15472 validation_1-rmse:0.11975 +[95] validation_0-rmse:0.15438 validation_1-rmse:0.11924 +[96] validation_0-rmse:0.15408 validation_1-rmse:0.11882 +[97] validation_0-rmse:0.15382 validation_1-rmse:0.11819 +[98] validation_0-rmse:0.15350 validation_1-rmse:0.11777 +[99] validation_0-rmse:0.15331 validation_1-rmse:0.11727 +2025-04-29 01:56:40,600 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:40,601 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:41,171 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:56:41,172 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1739145600_prediction.feather +2025-04-29 01:56:41,177 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,183 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:41,185 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:41,193 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,200 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:41,201 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:41,206 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1739145600/cb_sol_1739145600 +2025-04-29 01:56:41,207 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:41,228 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:41,229 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:56:46,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:46,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29889 validation_1-rmse:0.30153 +[1] validation_0-rmse:0.29317 validation_1-rmse:0.29483 +[2] validation_0-rmse:0.28819 validation_1-rmse:0.28860 +[3] validation_0-rmse:0.28336 validation_1-rmse:0.28273 +[4] validation_0-rmse:0.27885 validation_1-rmse:0.27694 +[5] validation_0-rmse:0.27448 validation_1-rmse:0.27155 +[6] validation_0-rmse:0.27020 validation_1-rmse:0.26634 +[7] validation_0-rmse:0.26629 validation_1-rmse:0.26134 +[8] validation_0-rmse:0.26241 validation_1-rmse:0.25653 +[9] validation_0-rmse:0.25876 validation_1-rmse:0.25192 +[10] validation_0-rmse:0.25559 validation_1-rmse:0.24747 +[11] validation_0-rmse:0.25223 validation_1-rmse:0.24337 +[12] validation_0-rmse:0.24904 validation_1-rmse:0.23934 +[13] validation_0-rmse:0.24639 validation_1-rmse:0.23548 +[14] validation_0-rmse:0.24353 validation_1-rmse:0.23187 +[15] validation_0-rmse:0.24076 validation_1-rmse:0.22837 +[16] validation_0-rmse:0.23849 validation_1-rmse:0.22484 +[17] validation_0-rmse:0.23581 validation_1-rmse:0.22147 +[18] validation_0-rmse:0.23342 validation_1-rmse:0.21814 +[19] validation_0-rmse:0.23133 validation_1-rmse:0.21509 +[20] validation_0-rmse:0.22937 validation_1-rmse:0.21187 +[21] validation_0-rmse:0.22713 validation_1-rmse:0.20902 +[22] validation_0-rmse:0.22509 validation_1-rmse:0.20631 +[23] validation_0-rmse:0.22312 validation_1-rmse:0.20373 +[24] validation_0-rmse:0.22123 validation_1-rmse:0.20076 +[25] validation_0-rmse:0.21951 validation_1-rmse:0.19837 +[26] validation_0-rmse:0.21751 validation_1-rmse:0.19562 +[27] validation_0-rmse:0.21589 validation_1-rmse:0.19309 +[28] validation_0-rmse:0.21422 validation_1-rmse:0.19091 +[29] validation_0-rmse:0.21272 validation_1-rmse:0.18879 +[30] validation_0-rmse:0.21119 validation_1-rmse:0.18660 +[31] validation_0-rmse:0.20982 validation_1-rmse:0.18468 +[32] validation_0-rmse:0.20829 validation_1-rmse:0.18239 +[33] validation_0-rmse:0.20681 validation_1-rmse:0.18048 +[34] validation_0-rmse:0.20548 validation_1-rmse:0.17869 +[35] validation_0-rmse:0.20431 validation_1-rmse:0.17665 +[36] validation_0-rmse:0.20297 validation_1-rmse:0.17483 +[37] validation_0-rmse:0.20174 validation_1-rmse:0.17311 +[38] validation_0-rmse:0.20060 validation_1-rmse:0.17153 +[39] validation_0-rmse:0.19951 validation_1-rmse:0.16958 +[40] validation_0-rmse:0.19848 validation_1-rmse:0.16805 +[41] validation_0-rmse:0.19745 validation_1-rmse:0.16652 +[42] validation_0-rmse:0.19647 validation_1-rmse:0.16509 +[43] validation_0-rmse:0.19570 validation_1-rmse:0.16325 +[44] validation_0-rmse:0.19473 validation_1-rmse:0.16187 +[45] validation_0-rmse:0.19397 validation_1-rmse:0.16012 +[46] validation_0-rmse:0.19314 validation_1-rmse:0.15887 +[47] validation_0-rmse:0.19196 validation_1-rmse:0.15723 +[48] validation_0-rmse:0.19096 validation_1-rmse:0.15595 +[49] validation_0-rmse:0.19009 validation_1-rmse:0.15468 +[50] validation_0-rmse:0.18931 validation_1-rmse:0.15355 +[51] validation_0-rmse:0.18864 validation_1-rmse:0.15207 +[52] validation_0-rmse:0.18786 validation_1-rmse:0.15101 +[53] validation_0-rmse:0.18690 validation_1-rmse:0.14960 +[54] validation_0-rmse:0.18614 validation_1-rmse:0.14859 +[55] validation_0-rmse:0.18550 validation_1-rmse:0.14756 +[56] validation_0-rmse:0.18475 validation_1-rmse:0.14647 +[57] validation_0-rmse:0.18405 validation_1-rmse:0.14545 +[58] validation_0-rmse:0.18346 validation_1-rmse:0.14415 +[59] validation_0-rmse:0.18277 validation_1-rmse:0.14321 +[60] validation_0-rmse:0.18219 validation_1-rmse:0.14221 +[61] validation_0-rmse:0.18158 validation_1-rmse:0.14129 +[62] validation_0-rmse:0.18100 validation_1-rmse:0.14043 +[63] validation_0-rmse:0.18059 validation_1-rmse:0.13920 +[64] validation_0-rmse:0.17997 validation_1-rmse:0.13842 +[65] validation_0-rmse:0.17941 validation_1-rmse:0.13754 +[66] validation_0-rmse:0.17881 validation_1-rmse:0.13652 +[67] validation_0-rmse:0.17823 validation_1-rmse:0.13576 +[68] validation_0-rmse:0.17784 validation_1-rmse:0.13468 +[69] validation_0-rmse:0.17735 validation_1-rmse:0.13396 +[70] validation_0-rmse:0.17687 validation_1-rmse:0.13311 +[71] validation_0-rmse:0.17628 validation_1-rmse:0.13225 +[72] validation_0-rmse:0.17599 validation_1-rmse:0.13154 +[73] validation_0-rmse:0.17542 validation_1-rmse:0.13080 +[74] validation_0-rmse:0.17497 validation_1-rmse:0.13013 +[75] validation_0-rmse:0.17456 validation_1-rmse:0.12954 +[76] validation_0-rmse:0.17416 validation_1-rmse:0.12864 +[77] validation_0-rmse:0.17369 validation_1-rmse:0.12802 +[78] validation_0-rmse:0.17345 validation_1-rmse:0.12735 +[79] validation_0-rmse:0.17302 validation_1-rmse:0.12672 +[80] validation_0-rmse:0.17254 validation_1-rmse:0.12609 +[81] validation_0-rmse:0.17248 validation_1-rmse:0.12527 +[82] validation_0-rmse:0.17210 validation_1-rmse:0.12470 +[83] validation_0-rmse:0.17196 validation_1-rmse:0.12398 +[84] validation_0-rmse:0.17189 validation_1-rmse:0.12334 +[85] validation_0-rmse:0.17155 validation_1-rmse:0.12280 +[86] validation_0-rmse:0.17124 validation_1-rmse:0.12230 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12178 +[88] validation_0-rmse:0.17086 validation_1-rmse:0.12118 +[89] validation_0-rmse:0.17064 validation_1-rmse:0.12049 +[90] validation_0-rmse:0.17029 validation_1-rmse:0.11993 +[91] validation_0-rmse:0.16981 validation_1-rmse:0.11942 +[92] validation_0-rmse:0.16950 validation_1-rmse:0.11894 +[93] validation_0-rmse:0.16937 validation_1-rmse:0.11833 +[94] validation_0-rmse:0.16928 validation_1-rmse:0.11786 +[95] validation_0-rmse:0.16899 validation_1-rmse:0.11735 +[96] validation_0-rmse:0.16869 validation_1-rmse:0.11693 +[97] validation_0-rmse:0.16843 validation_1-rmse:0.11650 +[98] validation_0-rmse:0.16829 validation_1-rmse:0.11591 +[99] validation_0-rmse:0.16802 validation_1-rmse:0.11547 +2025-04-29 01:56:47,778 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.57 secs) -------------------- +2025-04-29 01:56:47,779 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:48,320 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:56:48,321 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740009600_prediction.feather +2025-04-29 01:56:48,327 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,333 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:48,334 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:48,346 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,353 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:48,354 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:48,361 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740009600/cb_sol_1740009600 +2025-04-29 01:56:48,361 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:48,383 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:48,383 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:56:53,532 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:53,533 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29357 validation_1-rmse:0.28852 +[1] validation_0-rmse:0.28850 validation_1-rmse:0.28219 +[2] validation_0-rmse:0.28292 validation_1-rmse:0.27618 +[3] validation_0-rmse:0.27862 validation_1-rmse:0.27046 +[4] validation_0-rmse:0.27383 validation_1-rmse:0.26510 +[5] validation_0-rmse:0.27018 validation_1-rmse:0.25989 +[6] validation_0-rmse:0.26615 validation_1-rmse:0.25477 +[7] validation_0-rmse:0.26234 validation_1-rmse:0.25007 +[8] validation_0-rmse:0.25794 validation_1-rmse:0.24560 +[9] validation_0-rmse:0.25417 validation_1-rmse:0.24109 +[10] validation_0-rmse:0.25083 validation_1-rmse:0.23700 +[11] validation_0-rmse:0.24683 validation_1-rmse:0.23303 +[12] validation_0-rmse:0.24384 validation_1-rmse:0.22908 +[13] validation_0-rmse:0.24093 validation_1-rmse:0.22542 +[14] validation_0-rmse:0.23743 validation_1-rmse:0.22186 +[15] validation_0-rmse:0.23484 validation_1-rmse:0.21841 +[16] validation_0-rmse:0.23215 validation_1-rmse:0.21525 +[17] validation_0-rmse:0.22951 validation_1-rmse:0.21206 +[18] validation_0-rmse:0.22658 validation_1-rmse:0.20906 +[19] validation_0-rmse:0.22440 validation_1-rmse:0.20615 +[20] validation_0-rmse:0.22193 validation_1-rmse:0.20314 +[21] validation_0-rmse:0.22009 validation_1-rmse:0.20016 +[22] validation_0-rmse:0.21755 validation_1-rmse:0.19751 +[23] validation_0-rmse:0.21578 validation_1-rmse:0.19498 +[24] validation_0-rmse:0.21440 validation_1-rmse:0.19241 +[25] validation_0-rmse:0.21229 validation_1-rmse:0.19006 +[26] validation_0-rmse:0.21038 validation_1-rmse:0.18780 +[27] validation_0-rmse:0.20897 validation_1-rmse:0.18529 +[28] validation_0-rmse:0.20703 validation_1-rmse:0.18313 +[29] validation_0-rmse:0.20556 validation_1-rmse:0.18091 +[30] validation_0-rmse:0.20384 validation_1-rmse:0.17884 +[31] validation_0-rmse:0.20281 validation_1-rmse:0.17690 +[32] validation_0-rmse:0.20169 validation_1-rmse:0.17483 +[33] validation_0-rmse:0.20012 validation_1-rmse:0.17300 +[34] validation_0-rmse:0.19876 validation_1-rmse:0.17106 +[35] validation_0-rmse:0.19755 validation_1-rmse:0.16934 +[36] validation_0-rmse:0.19649 validation_1-rmse:0.16752 +[37] validation_0-rmse:0.19501 validation_1-rmse:0.16586 +[38] validation_0-rmse:0.19423 validation_1-rmse:0.16418 +[39] validation_0-rmse:0.19297 validation_1-rmse:0.16264 +[40] validation_0-rmse:0.19162 validation_1-rmse:0.16092 +[41] validation_0-rmse:0.19049 validation_1-rmse:0.15952 +[42] validation_0-rmse:0.18925 validation_1-rmse:0.15810 +[43] validation_0-rmse:0.18845 validation_1-rmse:0.15638 +[44] validation_0-rmse:0.18730 validation_1-rmse:0.15506 +[45] validation_0-rmse:0.18661 validation_1-rmse:0.15357 +[46] validation_0-rmse:0.18563 validation_1-rmse:0.15226 +[47] validation_0-rmse:0.18473 validation_1-rmse:0.15101 +[48] validation_0-rmse:0.18399 validation_1-rmse:0.14957 +[49] validation_0-rmse:0.18304 validation_1-rmse:0.14841 +[50] validation_0-rmse:0.18219 validation_1-rmse:0.14717 +[51] validation_0-rmse:0.18131 validation_1-rmse:0.14599 +[52] validation_0-rmse:0.18043 validation_1-rmse:0.14492 +[53] validation_0-rmse:0.17966 validation_1-rmse:0.14388 +[54] validation_0-rmse:0.17901 validation_1-rmse:0.14274 +[55] validation_0-rmse:0.17850 validation_1-rmse:0.14134 +[56] validation_0-rmse:0.17764 validation_1-rmse:0.14035 +[57] validation_0-rmse:0.17682 validation_1-rmse:0.13937 +[58] validation_0-rmse:0.17604 validation_1-rmse:0.13844 +[59] validation_0-rmse:0.17526 validation_1-rmse:0.13754 +[60] validation_0-rmse:0.17488 validation_1-rmse:0.13621 +[61] validation_0-rmse:0.17432 validation_1-rmse:0.13530 +[62] validation_0-rmse:0.17345 validation_1-rmse:0.13439 +[63] validation_0-rmse:0.17284 validation_1-rmse:0.13358 +[64] validation_0-rmse:0.17213 validation_1-rmse:0.13278 +[65] validation_0-rmse:0.17164 validation_1-rmse:0.13175 +[66] validation_0-rmse:0.17098 validation_1-rmse:0.13088 +[67] validation_0-rmse:0.17049 validation_1-rmse:0.13002 +[68] validation_0-rmse:0.17000 validation_1-rmse:0.12918 +[69] validation_0-rmse:0.16969 validation_1-rmse:0.12815 +[70] validation_0-rmse:0.16917 validation_1-rmse:0.12746 +[71] validation_0-rmse:0.16857 validation_1-rmse:0.12678 +[72] validation_0-rmse:0.16830 validation_1-rmse:0.12595 +[73] validation_0-rmse:0.16793 validation_1-rmse:0.12522 +[74] validation_0-rmse:0.16752 validation_1-rmse:0.12457 +[75] validation_0-rmse:0.16704 validation_1-rmse:0.12395 +[76] validation_0-rmse:0.16668 validation_1-rmse:0.12316 +[77] validation_0-rmse:0.16621 validation_1-rmse:0.12251 +[78] validation_0-rmse:0.16591 validation_1-rmse:0.12185 +[79] validation_0-rmse:0.16550 validation_1-rmse:0.12115 +[80] validation_0-rmse:0.16506 validation_1-rmse:0.12055 +[81] validation_0-rmse:0.16467 validation_1-rmse:0.12001 +[82] validation_0-rmse:0.16422 validation_1-rmse:0.11944 +[83] validation_0-rmse:0.16379 validation_1-rmse:0.11892 +[84] validation_0-rmse:0.16344 validation_1-rmse:0.11825 +[85] validation_0-rmse:0.16317 validation_1-rmse:0.11766 +[86] validation_0-rmse:0.16289 validation_1-rmse:0.11712 +[87] validation_0-rmse:0.16271 validation_1-rmse:0.11639 +[88] validation_0-rmse:0.16236 validation_1-rmse:0.11591 +[89] validation_0-rmse:0.16210 validation_1-rmse:0.11515 +[90] validation_0-rmse:0.16170 validation_1-rmse:0.11457 +[91] validation_0-rmse:0.16149 validation_1-rmse:0.11411 +[92] validation_0-rmse:0.16132 validation_1-rmse:0.11360 +[93] validation_0-rmse:0.16108 validation_1-rmse:0.11292 +[94] validation_0-rmse:0.16077 validation_1-rmse:0.11247 +[95] validation_0-rmse:0.16040 validation_1-rmse:0.11205 +[96] validation_0-rmse:0.16017 validation_1-rmse:0.11157 +[97] validation_0-rmse:0.15988 validation_1-rmse:0.11117 +[98] validation_0-rmse:0.15964 validation_1-rmse:0.11074 +[99] validation_0-rmse:0.15958 validation_1-rmse:0.11029 +2025-04-29 01:56:54,862 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.50 secs) -------------------- +2025-04-29 01:56:54,863 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:55,419 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:56:55,420 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740873600_prediction.feather +2025-04-29 01:56:55,426 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,433 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:55,435 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:55,445 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,452 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:56:55,453 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:56:55,459 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740873600/cb_sol_1740873600 +2025-04-29 01:56:55,459 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:55,481 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:55,482 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:57:00,566 - datasieve.pipeline - INFO - DI tossed 2417 predictions for being too far from training data. +2025-04-29 01:57:00,569 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:00,570 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.33058 validation_1-rmse:0.29214 +[1] validation_0-rmse:0.32414 validation_1-rmse:0.28558 +[2] validation_0-rmse:0.31832 validation_1-rmse:0.27962 +[3] validation_0-rmse:0.31280 validation_1-rmse:0.27380 +[4] validation_0-rmse:0.30679 validation_1-rmse:0.26829 +[5] validation_0-rmse:0.30182 validation_1-rmse:0.26306 +[6] validation_0-rmse:0.29686 validation_1-rmse:0.25797 +[7] validation_0-rmse:0.29291 validation_1-rmse:0.25323 +[8] validation_0-rmse:0.28868 validation_1-rmse:0.24871 +[9] validation_0-rmse:0.28559 validation_1-rmse:0.24437 +[10] validation_0-rmse:0.28262 validation_1-rmse:0.24018 +[11] validation_0-rmse:0.27890 validation_1-rmse:0.23606 +[12] validation_0-rmse:0.27663 validation_1-rmse:0.23225 +[13] validation_0-rmse:0.27306 validation_1-rmse:0.22870 +[14] validation_0-rmse:0.26948 validation_1-rmse:0.22493 +[15] validation_0-rmse:0.26663 validation_1-rmse:0.22157 +[16] validation_0-rmse:0.26427 validation_1-rmse:0.21817 +[17] validation_0-rmse:0.26195 validation_1-rmse:0.21492 +[18] validation_0-rmse:0.25856 validation_1-rmse:0.21177 +[19] validation_0-rmse:0.25606 validation_1-rmse:0.20861 +[20] validation_0-rmse:0.25343 validation_1-rmse:0.20580 +[21] validation_0-rmse:0.25243 validation_1-rmse:0.20301 +[22] validation_0-rmse:0.25066 validation_1-rmse:0.20027 +[23] validation_0-rmse:0.24864 validation_1-rmse:0.19761 +[24] validation_0-rmse:0.24630 validation_1-rmse:0.19522 +[25] validation_0-rmse:0.24491 validation_1-rmse:0.19283 +[26] validation_0-rmse:0.24339 validation_1-rmse:0.19036 +[27] validation_0-rmse:0.24108 validation_1-rmse:0.18818 +[28] validation_0-rmse:0.23976 validation_1-rmse:0.18592 +[29] validation_0-rmse:0.23882 validation_1-rmse:0.18348 +[30] validation_0-rmse:0.23676 validation_1-rmse:0.18142 +[31] validation_0-rmse:0.23520 validation_1-rmse:0.17945 +[32] validation_0-rmse:0.23395 validation_1-rmse:0.17754 +[33] validation_0-rmse:0.23229 validation_1-rmse:0.17545 +[34] validation_0-rmse:0.23073 validation_1-rmse:0.17360 +[35] validation_0-rmse:0.22951 validation_1-rmse:0.17182 +[36] validation_0-rmse:0.22806 validation_1-rmse:0.16995 +[37] validation_0-rmse:0.22713 validation_1-rmse:0.16834 +[38] validation_0-rmse:0.22541 validation_1-rmse:0.16668 +[39] validation_0-rmse:0.22393 validation_1-rmse:0.16509 +[40] validation_0-rmse:0.22282 validation_1-rmse:0.16343 +[41] validation_0-rmse:0.22168 validation_1-rmse:0.16185 +[42] validation_0-rmse:0.22085 validation_1-rmse:0.16046 +[43] validation_0-rmse:0.21991 validation_1-rmse:0.15907 +[44] validation_0-rmse:0.21833 validation_1-rmse:0.15756 +[45] validation_0-rmse:0.21710 validation_1-rmse:0.15618 +[46] validation_0-rmse:0.21619 validation_1-rmse:0.15490 +[47] validation_0-rmse:0.21518 validation_1-rmse:0.15345 +[48] validation_0-rmse:0.21402 validation_1-rmse:0.15221 +[49] validation_0-rmse:0.21305 validation_1-rmse:0.15086 +[50] validation_0-rmse:0.21229 validation_1-rmse:0.14968 +[51] validation_0-rmse:0.21119 validation_1-rmse:0.14854 +[52] validation_0-rmse:0.21019 validation_1-rmse:0.14745 +[53] validation_0-rmse:0.20924 validation_1-rmse:0.14637 +[54] validation_0-rmse:0.20982 validation_1-rmse:0.14517 +[55] validation_0-rmse:0.20888 validation_1-rmse:0.14405 +[56] validation_0-rmse:0.20806 validation_1-rmse:0.14305 +[57] validation_0-rmse:0.20822 validation_1-rmse:0.14169 +[58] validation_0-rmse:0.20741 validation_1-rmse:0.14071 +[59] validation_0-rmse:0.20663 validation_1-rmse:0.13976 +[60] validation_0-rmse:0.20602 validation_1-rmse:0.13882 +[61] validation_0-rmse:0.20523 validation_1-rmse:0.13776 +[62] validation_0-rmse:0.20558 validation_1-rmse:0.13689 +[63] validation_0-rmse:0.20501 validation_1-rmse:0.13605 +[64] validation_0-rmse:0.20348 validation_1-rmse:0.13462 +[65] validation_0-rmse:0.20273 validation_1-rmse:0.13382 +[66] validation_0-rmse:0.20203 validation_1-rmse:0.13306 +[67] validation_0-rmse:0.20166 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.20002 validation_1-rmse:0.13102 +[69] validation_0-rmse:0.19928 validation_1-rmse:0.13021 +[70] validation_0-rmse:0.19870 validation_1-rmse:0.12946 +[71] validation_0-rmse:0.19830 validation_1-rmse:0.12876 +[72] validation_0-rmse:0.19814 validation_1-rmse:0.12801 +[73] validation_0-rmse:0.19798 validation_1-rmse:0.12711 +[74] validation_0-rmse:0.19746 validation_1-rmse:0.12649 +[75] validation_0-rmse:0.19701 validation_1-rmse:0.12588 +[76] validation_0-rmse:0.19555 validation_1-rmse:0.12467 +[77] validation_0-rmse:0.19514 validation_1-rmse:0.12407 +[78] validation_0-rmse:0.19468 validation_1-rmse:0.12347 +[79] validation_0-rmse:0.19439 validation_1-rmse:0.12277 +[80] validation_0-rmse:0.19473 validation_1-rmse:0.12220 +[81] validation_0-rmse:0.19448 validation_1-rmse:0.12154 +[82] validation_0-rmse:0.19418 validation_1-rmse:0.12086 +[83] validation_0-rmse:0.19370 validation_1-rmse:0.12030 +[84] validation_0-rmse:0.19346 validation_1-rmse:0.11976 +[85] validation_0-rmse:0.19322 validation_1-rmse:0.11879 +[86] validation_0-rmse:0.19282 validation_1-rmse:0.11819 +[87] validation_0-rmse:0.19226 validation_1-rmse:0.11770 +[88] validation_0-rmse:0.19187 validation_1-rmse:0.11719 +[89] validation_0-rmse:0.19145 validation_1-rmse:0.11671 +[90] validation_0-rmse:0.19134 validation_1-rmse:0.11619 +[91] validation_0-rmse:0.19030 validation_1-rmse:0.11531 +[92] validation_0-rmse:0.18998 validation_1-rmse:0.11487 +[93] validation_0-rmse:0.18945 validation_1-rmse:0.11445 +[94] validation_0-rmse:0.18919 validation_1-rmse:0.11395 +[95] validation_0-rmse:0.18862 validation_1-rmse:0.11324 +[96] validation_0-rmse:0.18824 validation_1-rmse:0.11283 +[97] validation_0-rmse:0.18778 validation_1-rmse:0.11225 +[98] validation_0-rmse:0.18755 validation_1-rmse:0.11186 +[99] validation_0-rmse:0.18742 validation_1-rmse:0.11149 +2025-04-29 01:57:02,441 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.98 secs) -------------------- +2025-04-29 01:57:02,442 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:02,968 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:57:02,968 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1741737600_prediction.feather +2025-04-29 01:57:02,980 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:02,987 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:57:02,989 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:57:03,001 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:03,007 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:03,009 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:03,014 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1741737600/cb_sol_1741737600 +2025-04-29 01:57:03,015 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:03,042 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:03,042 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:57:08,138 - datasieve.pipeline - INFO - DI tossed 3 predictions for being too far from training data. +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25025 validation_1-rmse:0.24842 +[1] validation_0-rmse:0.24557 validation_1-rmse:0.24302 +[2] validation_0-rmse:0.24132 validation_1-rmse:0.23806 +[3] validation_0-rmse:0.23733 validation_1-rmse:0.23332 +[4] validation_0-rmse:0.23376 validation_1-rmse:0.22859 +[5] validation_0-rmse:0.23049 validation_1-rmse:0.22444 +[6] validation_0-rmse:0.22702 validation_1-rmse:0.22025 +[7] validation_0-rmse:0.22380 validation_1-rmse:0.21631 +[8] validation_0-rmse:0.22066 validation_1-rmse:0.21250 +[9] validation_0-rmse:0.21772 validation_1-rmse:0.20892 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.20536 +[11] validation_0-rmse:0.21219 validation_1-rmse:0.20211 +[12] validation_0-rmse:0.20968 validation_1-rmse:0.19897 +[13] validation_0-rmse:0.20730 validation_1-rmse:0.19597 +[14] validation_0-rmse:0.20552 validation_1-rmse:0.19281 +[15] validation_0-rmse:0.20361 validation_1-rmse:0.18992 +[16] validation_0-rmse:0.20165 validation_1-rmse:0.18721 +[17] validation_0-rmse:0.19948 validation_1-rmse:0.18463 +[18] validation_0-rmse:0.19807 validation_1-rmse:0.18193 +[19] validation_0-rmse:0.19703 validation_1-rmse:0.17939 +[20] validation_0-rmse:0.19569 validation_1-rmse:0.17683 +[21] validation_0-rmse:0.19388 validation_1-rmse:0.17462 +[22] validation_0-rmse:0.19219 validation_1-rmse:0.17240 +[23] validation_0-rmse:0.19062 validation_1-rmse:0.17026 +[24] validation_0-rmse:0.18933 validation_1-rmse:0.16813 +[25] validation_0-rmse:0.18829 validation_1-rmse:0.16598 +[26] validation_0-rmse:0.18704 validation_1-rmse:0.16411 +[27] validation_0-rmse:0.18563 validation_1-rmse:0.16221 +[28] validation_0-rmse:0.18446 validation_1-rmse:0.16034 +[29] validation_0-rmse:0.18316 validation_1-rmse:0.15841 +[30] validation_0-rmse:0.18192 validation_1-rmse:0.15674 +[31] validation_0-rmse:0.18091 validation_1-rmse:0.15479 +[32] validation_0-rmse:0.18003 validation_1-rmse:0.15312 +[33] validation_0-rmse:0.17886 validation_1-rmse:0.15150 +[34] validation_0-rmse:0.17786 validation_1-rmse:0.14997 +[35] validation_0-rmse:0.17692 validation_1-rmse:0.14855 +[36] validation_0-rmse:0.17613 validation_1-rmse:0.14709 +[37] validation_0-rmse:0.17547 validation_1-rmse:0.14549 +[38] validation_0-rmse:0.17467 validation_1-rmse:0.14404 +[39] validation_0-rmse:0.17393 validation_1-rmse:0.14267 +[40] validation_0-rmse:0.17348 validation_1-rmse:0.14118 +[41] validation_0-rmse:0.17258 validation_1-rmse:0.13993 +[42] validation_0-rmse:0.17168 validation_1-rmse:0.13871 +[43] validation_0-rmse:0.17077 validation_1-rmse:0.13757 +[44] validation_0-rmse:0.17015 validation_1-rmse:0.13621 +[45] validation_0-rmse:0.16924 validation_1-rmse:0.13509 +[46] validation_0-rmse:0.16833 validation_1-rmse:0.13401 +[47] validation_0-rmse:0.16756 validation_1-rmse:0.13297 +[48] validation_0-rmse:0.16717 validation_1-rmse:0.13198 +[49] validation_0-rmse:0.16664 validation_1-rmse:0.13081 +[50] validation_0-rmse:0.16615 validation_1-rmse:0.12979 +[51] validation_0-rmse:0.16541 validation_1-rmse:0.12879 +[52] validation_0-rmse:0.16478 validation_1-rmse:0.12767 +[53] validation_0-rmse:0.16408 validation_1-rmse:0.12675 +[54] validation_0-rmse:0.16363 validation_1-rmse:0.12571 +[55] validation_0-rmse:0.16320 validation_1-rmse:0.12485 +[56] validation_0-rmse:0.16253 validation_1-rmse:0.12398 +[57] validation_0-rmse:0.16192 validation_1-rmse:0.12307 +[58] validation_0-rmse:0.16149 validation_1-rmse:0.12229 +[59] validation_0-rmse:0.16137 validation_1-rmse:0.12128 +[60] validation_0-rmse:0.16117 validation_1-rmse:0.12045 +[61] validation_0-rmse:0.16064 validation_1-rmse:0.11966 +[62] validation_0-rmse:0.16050 validation_1-rmse:0.11890 +[63] validation_0-rmse:0.16003 validation_1-rmse:0.11809 +[64] validation_0-rmse:0.15969 validation_1-rmse:0.11739 +[65] validation_0-rmse:0.15922 validation_1-rmse:0.11661 +[66] validation_0-rmse:0.15868 validation_1-rmse:0.11577 +[67] validation_0-rmse:0.15830 validation_1-rmse:0.11509 +[68] validation_0-rmse:0.15789 validation_1-rmse:0.11446 +[69] validation_0-rmse:0.15733 validation_1-rmse:0.11372 +[70] validation_0-rmse:0.15694 validation_1-rmse:0.11307 +[71] validation_0-rmse:0.15692 validation_1-rmse:0.11224 +[72] validation_0-rmse:0.15659 validation_1-rmse:0.11166 +[73] validation_0-rmse:0.15634 validation_1-rmse:0.11111 +[74] validation_0-rmse:0.15595 validation_1-rmse:0.11056 +[75] validation_0-rmse:0.15579 validation_1-rmse:0.10985 +[76] validation_0-rmse:0.15543 validation_1-rmse:0.10903 +[77] validation_0-rmse:0.15500 validation_1-rmse:0.10848 +[78] validation_0-rmse:0.15499 validation_1-rmse:0.10778 +[79] validation_0-rmse:0.15471 validation_1-rmse:0.10721 +[80] validation_0-rmse:0.15442 validation_1-rmse:0.10666 +[81] validation_0-rmse:0.15416 validation_1-rmse:0.10608 +[82] validation_0-rmse:0.15388 validation_1-rmse:0.10560 +[83] validation_0-rmse:0.15368 validation_1-rmse:0.10498 +[84] validation_0-rmse:0.15346 validation_1-rmse:0.10449 +[85] validation_0-rmse:0.15329 validation_1-rmse:0.10392 +[86] validation_0-rmse:0.15302 validation_1-rmse:0.10347 +[87] validation_0-rmse:0.15270 validation_1-rmse:0.10303 +[88] validation_0-rmse:0.15259 validation_1-rmse:0.10258 +[89] validation_0-rmse:0.15269 validation_1-rmse:0.10204 +[90] validation_0-rmse:0.15239 validation_1-rmse:0.10159 +[91] validation_0-rmse:0.15204 validation_1-rmse:0.10116 +[92] validation_0-rmse:0.15175 validation_1-rmse:0.10070 +[93] validation_0-rmse:0.15167 validation_1-rmse:0.10017 +[94] validation_0-rmse:0.15154 validation_1-rmse:0.09982 +[95] validation_0-rmse:0.15122 validation_1-rmse:0.09932 +[96] validation_0-rmse:0.15119 validation_1-rmse:0.09880 +[97] validation_0-rmse:0.15112 validation_1-rmse:0.09842 +[98] validation_0-rmse:0.15095 validation_1-rmse:0.09807 +[99] validation_0-rmse:0.15075 validation_1-rmse:0.09770 +2025-04-29 01:57:09,614 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:09,615 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:10,150 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:57:10,151 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1742601600_prediction.feather +2025-04-29 01:57:10,159 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,167 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:10,168 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:10,181 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,188 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:10,190 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:10,195 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1742601600/cb_sol_1742601600 +2025-04-29 01:57:10,196 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:10,218 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:10,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:57:15,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:15,186 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24126 validation_1-rmse:0.24985 +[1] validation_0-rmse:0.23710 validation_1-rmse:0.24457 +[2] validation_0-rmse:0.23323 validation_1-rmse:0.23968 +[3] validation_0-rmse:0.22960 validation_1-rmse:0.23491 +[4] validation_0-rmse:0.22600 validation_1-rmse:0.23038 +[5] validation_0-rmse:0.22255 validation_1-rmse:0.22616 +[6] validation_0-rmse:0.21946 validation_1-rmse:0.22213 +[7] validation_0-rmse:0.21640 validation_1-rmse:0.21827 +[8] validation_0-rmse:0.21381 validation_1-rmse:0.21453 +[9] validation_0-rmse:0.21110 validation_1-rmse:0.21092 +[10] validation_0-rmse:0.20854 validation_1-rmse:0.20742 +[11] validation_0-rmse:0.20586 validation_1-rmse:0.20418 +[12] validation_0-rmse:0.20373 validation_1-rmse:0.20097 +[13] validation_0-rmse:0.20156 validation_1-rmse:0.19779 +[14] validation_0-rmse:0.19934 validation_1-rmse:0.19493 +[15] validation_0-rmse:0.19739 validation_1-rmse:0.19211 +[16] validation_0-rmse:0.19537 validation_1-rmse:0.18929 +[17] validation_0-rmse:0.19333 validation_1-rmse:0.18671 +[18] validation_0-rmse:0.19163 validation_1-rmse:0.18396 +[19] validation_0-rmse:0.18975 validation_1-rmse:0.18157 +[20] validation_0-rmse:0.18799 validation_1-rmse:0.17903 +[21] validation_0-rmse:0.18612 validation_1-rmse:0.17673 +[22] validation_0-rmse:0.18451 validation_1-rmse:0.17454 +[23] validation_0-rmse:0.18299 validation_1-rmse:0.17225 +[24] validation_0-rmse:0.18150 validation_1-rmse:0.17025 +[25] validation_0-rmse:0.18016 validation_1-rmse:0.16803 +[26] validation_0-rmse:0.17866 validation_1-rmse:0.16614 +[27] validation_0-rmse:0.17732 validation_1-rmse:0.16429 +[28] validation_0-rmse:0.17619 validation_1-rmse:0.16247 +[29] validation_0-rmse:0.17494 validation_1-rmse:0.16080 +[30] validation_0-rmse:0.17391 validation_1-rmse:0.15889 +[31] validation_0-rmse:0.17282 validation_1-rmse:0.15724 +[32] validation_0-rmse:0.17156 validation_1-rmse:0.15548 +[33] validation_0-rmse:0.17054 validation_1-rmse:0.15393 +[34] validation_0-rmse:0.16943 validation_1-rmse:0.15244 +[35] validation_0-rmse:0.16841 validation_1-rmse:0.15088 +[36] validation_0-rmse:0.16736 validation_1-rmse:0.14950 +[37] validation_0-rmse:0.16647 validation_1-rmse:0.14797 +[38] validation_0-rmse:0.16544 validation_1-rmse:0.14641 +[39] validation_0-rmse:0.16454 validation_1-rmse:0.14508 +[40] validation_0-rmse:0.16357 validation_1-rmse:0.14380 +[41] validation_0-rmse:0.16266 validation_1-rmse:0.14261 +[42] validation_0-rmse:0.16198 validation_1-rmse:0.14134 +[43] validation_0-rmse:0.16105 validation_1-rmse:0.14019 +[44] validation_0-rmse:0.16046 validation_1-rmse:0.13896 +[45] validation_0-rmse:0.15963 validation_1-rmse:0.13773 +[46] validation_0-rmse:0.15899 validation_1-rmse:0.13662 +[47] validation_0-rmse:0.15822 validation_1-rmse:0.13555 +[48] validation_0-rmse:0.15757 validation_1-rmse:0.13452 +[49] validation_0-rmse:0.15688 validation_1-rmse:0.13322 +[50] validation_0-rmse:0.15627 validation_1-rmse:0.13206 +[51] validation_0-rmse:0.15558 validation_1-rmse:0.13110 +[52] validation_0-rmse:0.15493 validation_1-rmse:0.13017 +[53] validation_0-rmse:0.15429 validation_1-rmse:0.12924 +[54] validation_0-rmse:0.15365 validation_1-rmse:0.12838 +[55] validation_0-rmse:0.15303 validation_1-rmse:0.12741 +[56] validation_0-rmse:0.15258 validation_1-rmse:0.12653 +[57] validation_0-rmse:0.15202 validation_1-rmse:0.12569 +[58] validation_0-rmse:0.15142 validation_1-rmse:0.12478 +[59] validation_0-rmse:0.15106 validation_1-rmse:0.12392 +[60] validation_0-rmse:0.15049 validation_1-rmse:0.12297 +[61] validation_0-rmse:0.14990 validation_1-rmse:0.12223 +[62] validation_0-rmse:0.14932 validation_1-rmse:0.12144 +[63] validation_0-rmse:0.14876 validation_1-rmse:0.12071 +[64] validation_0-rmse:0.14826 validation_1-rmse:0.12000 +[65] validation_0-rmse:0.14788 validation_1-rmse:0.11931 +[66] validation_0-rmse:0.14753 validation_1-rmse:0.11842 +[67] validation_0-rmse:0.14714 validation_1-rmse:0.11776 +[68] validation_0-rmse:0.14665 validation_1-rmse:0.11706 +[69] validation_0-rmse:0.14655 validation_1-rmse:0.11614 +[70] validation_0-rmse:0.14616 validation_1-rmse:0.11556 +[71] validation_0-rmse:0.14579 validation_1-rmse:0.11478 +[72] validation_0-rmse:0.14533 validation_1-rmse:0.11418 +[73] validation_0-rmse:0.14491 validation_1-rmse:0.11358 +[74] validation_0-rmse:0.14448 validation_1-rmse:0.11300 +[75] validation_0-rmse:0.14446 validation_1-rmse:0.11235 +[76] validation_0-rmse:0.14414 validation_1-rmse:0.11173 +[77] validation_0-rmse:0.14371 validation_1-rmse:0.11116 +[78] validation_0-rmse:0.14344 validation_1-rmse:0.11066 +[79] validation_0-rmse:0.14321 validation_1-rmse:0.10996 +[80] validation_0-rmse:0.14280 validation_1-rmse:0.10942 +[81] validation_0-rmse:0.14250 validation_1-rmse:0.10885 +[82] validation_0-rmse:0.14222 validation_1-rmse:0.10837 +[83] validation_0-rmse:0.14184 validation_1-rmse:0.10787 +[84] validation_0-rmse:0.14140 validation_1-rmse:0.10731 +[85] validation_0-rmse:0.14114 validation_1-rmse:0.10683 +[86] validation_0-rmse:0.14100 validation_1-rmse:0.10625 +[87] validation_0-rmse:0.14077 validation_1-rmse:0.10574 +[88] validation_0-rmse:0.14048 validation_1-rmse:0.10534 +[89] validation_0-rmse:0.14010 validation_1-rmse:0.10485 +[90] validation_0-rmse:0.13990 validation_1-rmse:0.10443 +[91] validation_0-rmse:0.13956 validation_1-rmse:0.10400 +[92] validation_0-rmse:0.13949 validation_1-rmse:0.10341 +[93] validation_0-rmse:0.13930 validation_1-rmse:0.10298 +[94] validation_0-rmse:0.13905 validation_1-rmse:0.10254 +[95] validation_0-rmse:0.13884 validation_1-rmse:0.10211 +[96] validation_0-rmse:0.13867 validation_1-rmse:0.10167 +[97] validation_0-rmse:0.13859 validation_1-rmse:0.10114 +[98] validation_0-rmse:0.13839 validation_1-rmse:0.10078 +[99] validation_0-rmse:0.13818 validation_1-rmse:0.10038 +2025-04-29 01:57:16,538 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.34 secs) -------------------- +2025-04-29 01:57:16,539 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:17,068 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:57:17,069 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather +2025-04-29 01:57:17,084 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,092 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:17,094 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:17,108 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,115 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:17,117 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:17,124 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 +2025-04-29 01:57:17,125 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:17,151 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:17,151 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.23717 validation_1-rmse:0.24954 +[1] validation_0-rmse:0.23311 validation_1-rmse:0.24432 +[2] validation_0-rmse:0.22894 validation_1-rmse:0.23928 +[3] validation_0-rmse:0.22483 validation_1-rmse:0.23452 +[4] validation_0-rmse:0.22141 validation_1-rmse:0.23005 +[5] validation_0-rmse:0.21750 validation_1-rmse:0.22575 +[6] validation_0-rmse:0.21419 validation_1-rmse:0.22161 +[7] validation_0-rmse:0.21074 validation_1-rmse:0.21764 +[8] validation_0-rmse:0.20776 validation_1-rmse:0.21374 +[9] validation_0-rmse:0.20479 validation_1-rmse:0.21020 +[10] validation_0-rmse:0.20193 validation_1-rmse:0.20664 +[11] validation_0-rmse:0.19915 validation_1-rmse:0.20326 +[12] validation_0-rmse:0.19683 validation_1-rmse:0.20013 +[13] validation_0-rmse:0.19459 validation_1-rmse:0.19705 +[14] validation_0-rmse:0.19243 validation_1-rmse:0.19415 +[15] validation_0-rmse:0.19013 validation_1-rmse:0.19115 +[16] validation_0-rmse:0.18828 validation_1-rmse:0.18836 +[17] validation_0-rmse:0.18621 validation_1-rmse:0.18557 +[18] validation_0-rmse:0.18402 validation_1-rmse:0.18302 +[19] validation_0-rmse:0.18198 validation_1-rmse:0.18050 +[20] validation_0-rmse:0.18015 validation_1-rmse:0.17803 +[21] validation_0-rmse:0.17857 validation_1-rmse:0.17575 +[22] validation_0-rmse:0.17681 validation_1-rmse:0.17350 +[23] validation_0-rmse:0.17537 validation_1-rmse:0.17132 +[24] validation_0-rmse:0.17377 validation_1-rmse:0.16919 +[25] validation_0-rmse:0.17225 validation_1-rmse:0.16720 +[26] validation_0-rmse:0.17072 validation_1-rmse:0.16529 +[27] validation_0-rmse:0.16931 validation_1-rmse:0.16310 +[28] validation_0-rmse:0.16784 validation_1-rmse:0.16126 +[29] validation_0-rmse:0.16650 validation_1-rmse:0.15940 +[30] validation_0-rmse:0.16512 validation_1-rmse:0.15771 +[31] validation_0-rmse:0.16392 validation_1-rmse:0.15605 +[32] validation_0-rmse:0.16287 validation_1-rmse:0.15428 +[33] validation_0-rmse:0.16159 validation_1-rmse:0.15277 +[34] validation_0-rmse:0.16033 validation_1-rmse:0.15125 +[35] validation_0-rmse:0.15910 validation_1-rmse:0.14974 +[36] validation_0-rmse:0.15821 validation_1-rmse:0.14832 +[37] validation_0-rmse:0.15733 validation_1-rmse:0.14664 +[38] validation_0-rmse:0.15624 validation_1-rmse:0.14525 +[39] validation_0-rmse:0.15518 validation_1-rmse:0.14395 +[40] validation_0-rmse:0.15451 validation_1-rmse:0.14267 +[41] validation_0-rmse:0.15396 validation_1-rmse:0.14127 +[42] validation_0-rmse:0.15309 validation_1-rmse:0.14006 +[43] validation_0-rmse:0.15219 validation_1-rmse:0.13890 +[44] validation_0-rmse:0.15156 validation_1-rmse:0.13749 +[45] validation_0-rmse:0.15061 validation_1-rmse:0.13637 +[46] validation_0-rmse:0.14982 validation_1-rmse:0.13528 +[47] validation_0-rmse:0.14918 validation_1-rmse:0.13414 +[48] validation_0-rmse:0.14840 validation_1-rmse:0.13312 +[49] validation_0-rmse:0.14802 validation_1-rmse:0.13212 +[50] validation_0-rmse:0.14738 validation_1-rmse:0.13089 +[51] validation_0-rmse:0.14671 validation_1-rmse:0.12994 +[52] validation_0-rmse:0.14604 validation_1-rmse:0.12894 +[53] validation_0-rmse:0.14534 validation_1-rmse:0.12802 +[54] validation_0-rmse:0.14464 validation_1-rmse:0.12718 +[55] validation_0-rmse:0.14423 validation_1-rmse:0.12625 +[56] validation_0-rmse:0.14371 validation_1-rmse:0.12531 +[57] validation_0-rmse:0.14321 validation_1-rmse:0.12446 +[58] validation_0-rmse:0.14279 validation_1-rmse:0.12346 +[59] validation_0-rmse:0.14234 validation_1-rmse:0.12257 +[60] validation_0-rmse:0.14194 validation_1-rmse:0.12181 +[61] validation_0-rmse:0.14176 validation_1-rmse:0.12077 +[62] validation_0-rmse:0.14120 validation_1-rmse:0.12003 +[63] validation_0-rmse:0.14073 validation_1-rmse:0.11932 +[64] validation_0-rmse:0.14023 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.14001 validation_1-rmse:0.11791 +[66] validation_0-rmse:0.13966 validation_1-rmse:0.11720 +[67] validation_0-rmse:0.13920 validation_1-rmse:0.11644 +[68] validation_0-rmse:0.13872 validation_1-rmse:0.11560 +[69] validation_0-rmse:0.13831 validation_1-rmse:0.11494 +[70] validation_0-rmse:0.13808 validation_1-rmse:0.11425 +[71] validation_0-rmse:0.13762 validation_1-rmse:0.11348 +[72] validation_0-rmse:0.13725 validation_1-rmse:0.11284 +[73] validation_0-rmse:0.13681 validation_1-rmse:0.11225 +[74] validation_0-rmse:0.13629 validation_1-rmse:0.11165 +[75] validation_0-rmse:0.13595 validation_1-rmse:0.11109 +[76] validation_0-rmse:0.13585 validation_1-rmse:0.11023 +[77] validation_0-rmse:0.13541 validation_1-rmse:0.10972 +[78] validation_0-rmse:0.13505 validation_1-rmse:0.10920 +[79] validation_0-rmse:0.13465 validation_1-rmse:0.10861 +[80] validation_0-rmse:0.13433 validation_1-rmse:0.10810 +[81] validation_0-rmse:0.13409 validation_1-rmse:0.10744 +[82] validation_0-rmse:0.13377 validation_1-rmse:0.10695 +[83] validation_0-rmse:0.13353 validation_1-rmse:0.10641 +[84] validation_0-rmse:0.13337 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.13329 validation_1-rmse:0.10533 +[86] validation_0-rmse:0.13296 validation_1-rmse:0.10488 +[87] validation_0-rmse:0.13264 validation_1-rmse:0.10442 +[88] validation_0-rmse:0.13247 validation_1-rmse:0.10394 +[89] validation_0-rmse:0.13216 validation_1-rmse:0.10351 +[90] validation_0-rmse:0.13188 validation_1-rmse:0.10297 +[91] validation_0-rmse:0.13145 validation_1-rmse:0.10203 +[92] validation_0-rmse:0.13122 validation_1-rmse:0.10157 +[93] validation_0-rmse:0.13102 validation_1-rmse:0.10118 +[94] validation_0-rmse:0.13060 validation_1-rmse:0.10033 +[95] validation_0-rmse:0.13033 validation_1-rmse:0.09981 +[96] validation_0-rmse:0.13016 validation_1-rmse:0.09933 +[97] validation_0-rmse:0.12995 validation_1-rmse:0.09894 +[98] validation_0-rmse:0.12972 validation_1-rmse:0.09860 +[99] validation_0-rmse:0.12954 validation_1-rmse:0.09825 +2025-04-29 01:57:23,725 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:23,726 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:24,305 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:57:24,305 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather +2025-04-29 01:57:24,318 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,325 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:24,327 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:24,337 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,345 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:57:24,346 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.729824 +1 0.003044 49.729824 +2 0.000465 49.729824 +3 -0.000380 49.729824 +4 0.002829 49.729824 +2025-04-29 01:57:24,352 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 +2025-04-29 01:57:24,353 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:24,376 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:24,376 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:57:29,392 - datasieve.pipeline - INFO - DI tossed 1948 predictions for being too far from training data. +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30616 validation_1-rmse:0.27906 +[1] validation_0-rmse:0.30021 validation_1-rmse:0.27322 +[2] validation_0-rmse:0.29443 validation_1-rmse:0.26757 +[3] validation_0-rmse:0.28911 validation_1-rmse:0.26205 +[4] validation_0-rmse:0.28365 validation_1-rmse:0.25699 +[5] validation_0-rmse:0.27823 validation_1-rmse:0.25219 +[6] validation_0-rmse:0.27295 validation_1-rmse:0.24748 +[7] validation_0-rmse:0.26797 validation_1-rmse:0.24295 +[8] validation_0-rmse:0.26320 validation_1-rmse:0.23854 +[9] validation_0-rmse:0.25898 validation_1-rmse:0.23437 +[10] validation_0-rmse:0.25517 validation_1-rmse:0.23021 +[11] validation_0-rmse:0.25113 validation_1-rmse:0.22639 +[12] validation_0-rmse:0.24762 validation_1-rmse:0.22270 +[13] validation_0-rmse:0.24393 validation_1-rmse:0.21915 +[14] validation_0-rmse:0.24169 validation_1-rmse:0.21579 +[15] validation_0-rmse:0.23898 validation_1-rmse:0.21236 +[16] validation_0-rmse:0.23539 validation_1-rmse:0.20924 +[17] validation_0-rmse:0.23364 validation_1-rmse:0.20621 +[18] validation_0-rmse:0.23062 validation_1-rmse:0.20322 +[19] validation_0-rmse:0.22764 validation_1-rmse:0.20024 +[20] validation_0-rmse:0.22488 validation_1-rmse:0.19731 +[21] validation_0-rmse:0.22211 validation_1-rmse:0.19445 +[22] validation_0-rmse:0.21952 validation_1-rmse:0.19188 +[23] validation_0-rmse:0.21699 validation_1-rmse:0.18935 +[24] validation_0-rmse:0.21549 validation_1-rmse:0.18686 +[25] validation_0-rmse:0.21310 validation_1-rmse:0.18454 +[26] validation_0-rmse:0.21118 validation_1-rmse:0.18198 +[27] validation_0-rmse:0.20904 validation_1-rmse:0.17979 +[28] validation_0-rmse:0.20726 validation_1-rmse:0.17755 +[29] validation_0-rmse:0.20511 validation_1-rmse:0.17547 +[30] validation_0-rmse:0.20336 validation_1-rmse:0.17335 +[31] validation_0-rmse:0.20172 validation_1-rmse:0.17144 +[32] validation_0-rmse:0.19983 validation_1-rmse:0.16961 +[33] validation_0-rmse:0.19794 validation_1-rmse:0.16759 +[34] validation_0-rmse:0.19658 validation_1-rmse:0.16581 +[35] validation_0-rmse:0.19492 validation_1-rmse:0.16409 +[36] validation_0-rmse:0.19347 validation_1-rmse:0.16229 +[37] validation_0-rmse:0.19225 validation_1-rmse:0.16064 +[38] validation_0-rmse:0.19083 validation_1-rmse:0.15877 +[39] validation_0-rmse:0.18921 validation_1-rmse:0.15720 +[40] validation_0-rmse:0.18766 validation_1-rmse:0.15572 +[41] validation_0-rmse:0.18652 validation_1-rmse:0.15414 +[42] validation_0-rmse:0.18519 validation_1-rmse:0.15277 +[43] validation_0-rmse:0.18396 validation_1-rmse:0.15125 +[44] validation_0-rmse:0.18264 validation_1-rmse:0.14968 +[45] validation_0-rmse:0.18134 validation_1-rmse:0.14841 +[46] validation_0-rmse:0.18026 validation_1-rmse:0.14717 +[47] validation_0-rmse:0.17900 validation_1-rmse:0.14594 +[48] validation_0-rmse:0.17815 validation_1-rmse:0.14460 +[49] validation_0-rmse:0.17713 validation_1-rmse:0.14344 +[50] validation_0-rmse:0.17609 validation_1-rmse:0.14232 +[51] validation_0-rmse:0.17502 validation_1-rmse:0.14112 +[52] validation_0-rmse:0.17414 validation_1-rmse:0.13991 +[53] validation_0-rmse:0.17317 validation_1-rmse:0.13889 +[54] validation_0-rmse:0.17267 validation_1-rmse:0.13770 +[55] validation_0-rmse:0.17175 validation_1-rmse:0.13665 +[56] validation_0-rmse:0.17087 validation_1-rmse:0.13573 +[57] validation_0-rmse:0.17001 validation_1-rmse:0.13483 +[58] validation_0-rmse:0.16920 validation_1-rmse:0.13384 +[59] validation_0-rmse:0.16869 validation_1-rmse:0.13280 +[60] validation_0-rmse:0.16790 validation_1-rmse:0.13189 +[61] validation_0-rmse:0.16689 validation_1-rmse:0.13093 +[62] validation_0-rmse:0.16600 validation_1-rmse:0.13007 +[63] validation_0-rmse:0.16548 validation_1-rmse:0.12921 +[64] validation_0-rmse:0.16482 validation_1-rmse:0.12837 +[65] validation_0-rmse:0.16397 validation_1-rmse:0.12747 +[66] validation_0-rmse:0.16316 validation_1-rmse:0.12669 +[67] validation_0-rmse:0.16267 validation_1-rmse:0.12587 +[68] validation_0-rmse:0.16204 validation_1-rmse:0.12501 +[69] validation_0-rmse:0.16159 validation_1-rmse:0.12422 +[70] validation_0-rmse:0.16090 validation_1-rmse:0.12354 +[71] validation_0-rmse:0.16026 validation_1-rmse:0.12282 +[72] validation_0-rmse:0.15986 validation_1-rmse:0.12206 +[73] validation_0-rmse:0.15919 validation_1-rmse:0.12129 +[74] validation_0-rmse:0.15875 validation_1-rmse:0.12061 +[75] validation_0-rmse:0.15829 validation_1-rmse:0.11966 +[76] validation_0-rmse:0.15790 validation_1-rmse:0.11864 +[77] validation_0-rmse:0.15732 validation_1-rmse:0.11802 +[78] validation_0-rmse:0.15696 validation_1-rmse:0.11739 +[79] validation_0-rmse:0.15615 validation_1-rmse:0.11660 +[80] validation_0-rmse:0.15556 validation_1-rmse:0.11593 +[81] validation_0-rmse:0.15516 validation_1-rmse:0.11531 +[82] validation_0-rmse:0.15466 validation_1-rmse:0.11437 +[83] validation_0-rmse:0.15422 validation_1-rmse:0.11383 +[84] validation_0-rmse:0.15382 validation_1-rmse:0.11332 +[85] validation_0-rmse:0.15350 validation_1-rmse:0.11244 +[86] validation_0-rmse:0.15310 validation_1-rmse:0.11180 +[87] validation_0-rmse:0.15277 validation_1-rmse:0.11119 +[88] validation_0-rmse:0.15228 validation_1-rmse:0.11060 +[89] validation_0-rmse:0.15192 validation_1-rmse:0.11011 +[90] validation_0-rmse:0.15144 validation_1-rmse:0.10956 +[91] validation_0-rmse:0.15092 validation_1-rmse:0.10913 +[92] validation_0-rmse:0.15058 validation_1-rmse:0.10847 +[93] validation_0-rmse:0.15017 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.14984 validation_1-rmse:0.10702 +[95] validation_0-rmse:0.14967 validation_1-rmse:0.10629 +[96] validation_0-rmse:0.14914 validation_1-rmse:0.10587 +[97] validation_0-rmse:0.14882 validation_1-rmse:0.10545 +[98] validation_0-rmse:0.14853 validation_1-rmse:0.10454 +[99] validation_0-rmse:0.14837 validation_1-rmse:0.10398 +2025-04-29 01:57:30,474 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.12 secs) -------------------- +2025-04-29 01:57:30,475 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:31,077 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:57:31,096 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +0 33825 +1 32946 +2025-04-29 01:57:31,097 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 36730 +1.0 30041 +2025-04-29 01:57:31,105 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-01-01 00:00:00 up to 2025-04-20 00:00:00 (109 days). +2025-04-29 01:57:31,109 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,111 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:31,193 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,195 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:33,776 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_01-57-33.meta.json" +Result for strategy FreqaiExampleStrategy + BACKTESTING REPORT  +┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃  Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃ +┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ BTC/USDT │ 38 │ -0.39 │ -22.029 │ -2.2 │ 22:13:00 │ 5 32 1 13.2 │ +│ SOL/USDT │ 44 │ -1.94 │ -128.236 │ -12.82 │ 16:35:00 │ 12 26 6 27.3 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + LEFT OPEN TRADES REPORT  +┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃  Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃ +┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │ +└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + ENTER TAG STATS  +┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃ +┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ long │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + EXIT REASON STATS  +┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃  Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃  Avg Duration ┃  Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ roi │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ trailing_stop_loss │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + MIXED TAG STATS  +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃  Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃  Avg Duration ┃  Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ('long', 'roi') │ │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ ('long', 'trailing_stop_loss') │ │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + SUMMARY METRICS  +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Metric  ┃ Value  ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ Backtesting from │ 2025-01-01 00:00:00 │ +│ Backtesting to │ 2025-04-20 00:00:00 │ +│ Trading Mode │ Spot │ +│ Max open trades │ 2 │ +│ │ │ +│ Total/Daily Avg Trades │ 82 / 0.75 │ +│ Starting balance │ 1000 USDT │ +│ Final balance │ 849.735 USDT │ +│ Absolute profit │ -150.265 USDT │ +│ Total profit % │ -15.03% │ +│ CAGR % │ -42.03% │ +│ Sortino │ -252.56 │ +│ Sharpe │ -4.15 │ +│ Calmar │ -17.48 │ +│ SQN │ -2.60 │ +│ Profit factor │ 0.05 │ +│ Expectancy (Ratio) │ -1.83 (-0.79) │ +│ Avg. daily profit % │ -0.14% │ +│ Avg. stake amount │ 150 USDT │ +│ Total trade volume │ 24523.15 USDT │ +│ │ │ +│ Best Pair │ BTC/USDT -2.20% │ +│ Worst Pair │ SOL/USDT -12.82% │ +│ Best trade │ SOL/USDT 0.90% │ +│ Worst trade │ SOL/USDT -15.19% │ +│ Best day │ 1.76 USDT │ +│ Worst day │ -22.827 USDT │ +│ Days win/draw/lose │ 14 / 80 / 7 │ +│ Avg. Duration Winners │ 0:55:00 │ +│ Avg. Duration Loser │ 2 days, 18:13:00 │ +│ Max Consecutive Wins / Loss │ 2 / 16 │ +│ Rejected Entry signals │ 0 │ +│ Entry/Exit Timeouts │ 0 / 0 │ +│ │ │ +│ Min balance │ 849.735 USDT │ +│ Max balance │ 1000.508 USDT │ +│ Max % of account underwater │ 15.07% │ +│ Absolute Drawdown (Account) │ 15.07% │ +│ Absolute Drawdown │ 150.773 USDT │ +│ Drawdown high │ 0.508 USDT │ +│ Drawdown low │ -150.265 USDT │ +│ Drawdown Start │ 2025-01-06 19:48:00 │ +│ Drawdown End │ 2025-04-06 23:15:00 │ +│ Market change │ -26.79% │ +└─────────────────────────────┴─────────────────────┘ + +Backtested 2025-01-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2 + STRATEGY SUMMARY  +┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ +┃  Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃  Drawdown ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ +│ FreqaiExampleStrategy │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ 150.773 USDT 15.07% │ +└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴──────────────────────┘ diff --git a/output.log b/output.log new file mode 100644 index 00000000..e0d47248 --- /dev/null +++ b/output.log @@ -0,0 +1,1232 @@ +Creating freqtrade_freqtrade_run ... +Creating freqtrade_freqtrade_run ... done +2025-05-01 04:27:17,240 - freqtrade - INFO - freqtrade 2025.3 +2025-05-01 04:27:17,468 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-05-01 04:27:18,889 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-05-01 04:27:18,891 - freqtrade.loggers - INFO - Enabling colorized output. +2025-05-01 04:27:18,891 - root - INFO - Logfile configured +2025-05-01 04:27:18,892 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-05-01 04:27:18,892 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-05-01 04:27:18,892 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-05-01 04:27:18,893 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250401-20250420 ... +2025-05-01 04:27:18,909 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-05-01 04:27:18,910 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-05-01 04:27:18,910 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-05-01 04:27:18,911 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250401-20250420 +2025-05-01 04:27:18,911 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-05-01 04:27:18,912 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-05-01 04:27:18,918 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-05-01 04:27:18,918 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-05-01 04:27:18,918 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-05-01 04:27:18,920 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-05-01 04:27:18,921 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-05-01 04:27:18,921 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-05-01 04:27:18,922 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-05-01 04:27:18,927 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-05-01 04:27:18,933 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-05-01 04:27:21,480 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-05-01 04:27:21,502 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-05-01 04:27:21,503 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-05-01 04:27:21,503 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-05-01 04:27:21,504 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-05-01 04:27:21,504 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-05-01 04:27:21,504 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-05-01 04:27:21,505 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-05-01 04:27:21,505 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +'minutes'}. +2025-05-01 04:27:21,505 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-05-01 04:27:21,507 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-05-01 04:27:21,507 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-05-01 04:27:21,507 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +'stoploss_on_exchange_interval': 60} +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-05-01 04:27:21,509 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-05-01 04:27:21,509 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-05-01 04:27:21,509 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-05-01 04:27:21,512 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-05-01 04:27:21,512 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-05-01 04:27:21,512 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-05-01 04:27:21,516 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-05-01 04:27:21,522 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-05-01 04:27:21,523 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-05-01 04:27:21,524 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-05-01 04:27:21,658 - freqtrade.optimize.backtesting - INFO - Loading data from 2025-03-01 21:30:00 up to 2025-04-20 00:00:00 (49 days). +2025-05-01 04:27:21,658 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-05-01 04:27:21,659 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-05-01 04:27:23,258 - matplotlib.font_manager - INFO - generated new fontManager +2025-05-01 04:27:23,463 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-05-01 04:27:23,463 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-05-01 04:27:23,464 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-05-01 04:27:23,464 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-05-01 04:27:23,465 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-05-01 04:27:23,465 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-05-01 04:27:23,466 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-05-01 04:27:23,468 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-05-01 04:27:23,470 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges +2025-05-01 04:27:23,471 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains +2025-05-01 04:27:23,471 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather +2025-05-01 04:27:23,486 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,502 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,519 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,570 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-05-01 04:27:23,571 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:23,623 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,635 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,647 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,688 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-05-01 04:27:23,689 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:23,711 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,721 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,729 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,803 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-05-01 04:27:23,804 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:23,869 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,884 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,901 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,965 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-05-01 04:27:23,965 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:24,005 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,016 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,027 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,081 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-05-01 04:27:24,082 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:24,104 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,113 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,122 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,176 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:24,182 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-05-01 04:27:24,185 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.007116 50.010488 +1 0.005291 50.010488 +2 0.004416 50.010488 +3 0.002082 50.010488 +4 0.001904 50.010488 +2025-05-01 04:27:24,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:24,198 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:24,200 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.007116 49.846666 +1 0.005291 49.846666 +2 0.004416 49.846666 +3 0.002082 49.846666 +4 0.001904 49.846666 +2025-05-01 04:27:24,211 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-05-01 04:27:24,212 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-05-01 04:27:24,234 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:24,235 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-05-01 04:27:24,253 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-05-01 04:27:24,254 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: [04:27:24] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + warnings.warn(smsg, UserWarning) +[0] validation_0-rmse:0.29022 validation_1-rmse:0.28556 +[1] validation_0-rmse:0.28471 validation_1-rmse:0.27986 +[2] validation_0-rmse:0.27927 validation_1-rmse:0.27432 +[3] validation_0-rmse:0.27481 validation_1-rmse:0.26957 +[4] validation_0-rmse:0.27004 validation_1-rmse:0.26485 +[5] validation_0-rmse:0.26549 validation_1-rmse:0.26011 +[6] validation_0-rmse:0.26110 validation_1-rmse:0.25557 +[7] validation_0-rmse:0.25760 validation_1-rmse:0.25177 +[8] validation_0-rmse:0.25374 validation_1-rmse:0.24771 +[9] validation_0-rmse:0.24993 validation_1-rmse:0.24381 +[10] validation_0-rmse:0.24611 validation_1-rmse:0.23989 +[11] 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validation_1-rmse:0.08779 +[197] validation_0-rmse:0.10906 validation_1-rmse:0.08752 +[198] validation_0-rmse:0.10865 validation_1-rmse:0.08710 +[199] validation_0-rmse:0.10846 validation_1-rmse:0.08691 +2025-05-01 04:27:26,288 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (2.08 secs) -------------------- +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:26] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +2025-05-01 04:27:26,516 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600--buy_rsi.html +2025-05-01 04:27:26,517 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:26,539 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:26,546 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains +2025-05-01 04:27:26,547 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather +2025-05-01 04:27:26,553 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:26,559 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:26,560 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.007116 49.846666 +1 0.005291 49.846666 +2 0.004416 49.846666 +3 0.002082 49.846666 +4 0.001904 49.846666 +2025-05-01 04:27:26,570 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:26,576 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,) +2025-05-01 04:27:26,577 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.007116 50.074781 +1 0.005291 50.074781 +2 0.004416 50.074781 +3 0.002082 50.074781 +4 0.001904 50.074781 +2025-05-01 04:27:26,585 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-05-01 04:27:26,585 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-05-01 04:27:26,603 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:26,603 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-05-01 04:27:26,621 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-05-01 04:27:26,621 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:26] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +[0] validation_0-rmse:0.29190 validation_1-rmse:0.28988 +[1] validation_0-rmse:0.28631 validation_1-rmse:0.28388 +[2] validation_0-rmse:0.28059 validation_1-rmse:0.27791 +[3] validation_0-rmse:0.27597 validation_1-rmse:0.27282 +[4] validation_0-rmse:0.27132 validation_1-rmse:0.26791 +[5] validation_0-rmse:0.26666 validation_1-rmse:0.26287 +[6] validation_0-rmse:0.26193 validation_1-rmse:0.25802 +[7] validation_0-rmse:0.25814 validation_1-rmse:0.25375 +[8] validation_0-rmse:0.25389 validation_1-rmse:0.24927 +[9] validation_0-rmse:0.24990 validation_1-rmse:0.24489 +[10] validation_0-rmse:0.24602 validation_1-rmse:0.24078 +[11] validation_0-rmse:0.24233 validation_1-rmse:0.23687 +[12] validation_0-rmse:0.23880 validation_1-rmse:0.23304 +[13] validation_0-rmse:0.23523 validation_1-rmse:0.22935 +[14] validation_0-rmse:0.23205 validation_1-rmse:0.22588 +[15] validation_0-rmse:0.22868 validation_1-rmse:0.22242 +[16] 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validation_1-rmse:0.08860 +[185] validation_0-rmse:0.10794 validation_1-rmse:0.08844 +[186] validation_0-rmse:0.10770 validation_1-rmse:0.08818 +[187] validation_0-rmse:0.10760 validation_1-rmse:0.08806 +[188] validation_0-rmse:0.10736 validation_1-rmse:0.08781 +[189] validation_0-rmse:0.10723 validation_1-rmse:0.08766 +[190] validation_0-rmse:0.10709 validation_1-rmse:0.08750 +[191] validation_0-rmse:0.10701 validation_1-rmse:0.08739 +[192] validation_0-rmse:0.10682 validation_1-rmse:0.08719 +[193] validation_0-rmse:0.10677 validation_1-rmse:0.08707 +[194] validation_0-rmse:0.10659 validation_1-rmse:0.08687 +[195] validation_0-rmse:0.10626 validation_1-rmse:0.08655 +[196] validation_0-rmse:0.10605 validation_1-rmse:0.08635 +[197] validation_0-rmse:0.10592 validation_1-rmse:0.08620 +[198] validation_0-rmse:0.10571 validation_1-rmse:0.08597 +[199] validation_0-rmse:0.10560 validation_1-rmse:0.08582 +2025-05-01 04:27:28,783 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (2.20 secs) -------------------- +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:28] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +2025-05-01 04:27:28,818 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600--buy_rsi.html +2025-05-01 04:27:28,818 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:28,840 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:28,899 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-05-01 04:27:28,943 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +1 11845 +0 11726 +2025-05-01 04:27:28,944 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 14451 +1.0 9120 +2025-05-01 04:27:28,947 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-05-01 04:27:28,949 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges +2025-05-01 04:27:28,950 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains +2025-05-01 04:27:28,951 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather +2025-05-01 04:27:28,962 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:28,974 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:28,988 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,022 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-05-01 04:27:29,022 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:29,075 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,086 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,097 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,128 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-05-01 04:27:29,129 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:29,151 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,159 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,168 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,228 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-05-01 04:27:29,229 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:29,283 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,296 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,310 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,367 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,378 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,388 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,442 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,451 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,459 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,535 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,548 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,561 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,630 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,640 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,651 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,735 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,743 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,751 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,827 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:29,832 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-05-01 04:27:29,834 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.016595 49.72136 +1 0.012811 49.72136 +2 0.010135 49.72136 +3 0.008514 49.72136 +4 0.006242 49.72136 +2025-05-01 04:27:29,844 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:29,850 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:29,851 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.016595 49.562407 +1 0.012811 49.562407 +2 0.010135 49.562407 +3 0.008514 49.562407 +4 0.006242 49.562407 +2025-05-01 04:27:29,863 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 +2025-05-01 04:27:29,864 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-05-01 04:27:29,893 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:29,894 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-05-01 04:27:29,919 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-05-01 04:27:29,919 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:30] 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freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (3.31 secs) -------------------- +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:33] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +2025-05-01 04:27:33,208 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600--buy_rsi.html +2025-05-01 04:27:33,209 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:33,246 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:33,254 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains +2025-05-01 04:27:33,255 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather +2025-05-01 04:27:33,265 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:33,270 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:33,272 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.016595 49.562407 +1 0.012811 49.562407 +2 0.010135 49.562407 +3 0.008514 49.562407 +4 0.006242 49.562407 +2025-05-01 04:27:33,283 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:33,289 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,) +2025-05-01 04:27:33,290 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.016595 49.934347 +1 0.012811 49.934347 +2 0.010135 49.934347 +3 0.008514 49.934347 +4 0.006242 49.934347 +2025-05-01 04:27:33,300 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 +2025-05-01 04:27:33,300 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-05-01 04:27:33,322 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:33,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-05-01 04:27:33,349 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-05-01 04:27:33,349 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:33] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +[0] validation_0-rmse:0.28344 validation_1-rmse:0.28168 +[1] validation_0-rmse:0.27833 validation_1-rmse:0.27634 +[2] 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validation_1-rmse:0.08423 +[188] validation_0-rmse:0.10357 validation_1-rmse:0.08406 +[189] validation_0-rmse:0.10351 validation_1-rmse:0.08396 +[190] validation_0-rmse:0.10324 validation_1-rmse:0.08368 +[191] validation_0-rmse:0.10302 validation_1-rmse:0.08341 +[192] validation_0-rmse:0.10292 validation_1-rmse:0.08323 +[193] validation_0-rmse:0.10273 validation_1-rmse:0.08299 +[194] validation_0-rmse:0.10262 validation_1-rmse:0.08285 +[195] validation_0-rmse:0.10246 validation_1-rmse:0.08265 +[196] validation_0-rmse:0.10218 validation_1-rmse:0.08237 +[197] validation_0-rmse:0.10207 validation_1-rmse:0.08220 +[198] validation_0-rmse:0.10188 validation_1-rmse:0.08196 +[199] validation_0-rmse:0.10169 validation_1-rmse:0.08171 +2025-05-01 04:27:36,303 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (3.00 secs) -------------------- +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:36] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +2025-05-01 04:27:36,337 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600--buy_rsi.html +2025-05-01 04:27:36,338 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:36,372 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:36,441 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 +2025-05-01 04:27:36,503 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +0 11865 +1 11706 +2025-05-01 04:27:36,504 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 14451 +1.0 9120 +2025-05-01 04:27:36,512 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-04-01 00:00:00 up to 2025-04-20 00:00:00 (19 days). +2025-05-01 04:27:36,517 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-05-01 04:27:36,519 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-05-01 04:27:36,549 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-05-01 04:27:36,551 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-05-01 04:27:37,138 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-05-01_04-27-37.meta.json" +Result for strategy FreqaiExampleStrategy + BACKTESTING REPORT +┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ BTC/USDT │ 17 │ 0.03 │ 0.801 │ 0.08 │ 11:49:00 │ 2 15 0 100 │ +│ SOL/USDT │ 13 │ -0.93 │ -18.095 │ -1.81 │ 8:54:00 │ 5 7 1 38.5 │ +│ TOTAL │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ +└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + LEFT OPEN TRADES REPORT +┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │ +└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + ENTER TAG STATS +┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ long │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ +│ TOTAL │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ +└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + EXIT REASON STATS +┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ roi │ 29 │ 0.12 │ 5.235 │ 0.52 │ 9:21:00 │ 7 22 0 100 │ +│ trailing_stop_loss │ 1 │ -15.0 │ -22.529 │ -2.25 │ 1 day, 21:45:00 │ 0 0 1 0 │ +│ TOTAL │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ +└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘ + MIXED TAG STATS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ('long', 'roi') │ │ 29 │ 0.12 │ 5.235 │ 0.52 │ 9:21:00 │ 7 22 0 100 │ +│ ('long', 'trailing_stop_loss') │ │ 1 │ -15.0 │ -22.529 │ -2.25 │ 1 day, 21:45:00 │ 0 0 1 0 │ +│ TOTAL │ │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ +└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘ + SUMMARY METRICS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Metric ┃ Value ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ Backtesting from │ 2025-04-01 00:00:00 │ +│ Backtesting to │ 2025-04-20 00:00:00 │ +│ Trading Mode │ Spot │ +│ Max open trades │ 2 │ +│ │ │ +│ Total/Daily Avg Trades │ 30 / 1.58 │ +│ Starting balance │ 1000 USDT │ +│ Final balance │ 982.706 USDT │ +│ Absolute profit │ -17.294 USDT │ +│ Total profit % │ -1.73% │ +│ CAGR % │ -28.48% │ +│ Sortino │ -100.00 │ +│ Sharpe │ -4.24 │ +│ Calmar │ -77.19 │ +│ SQN │ -0.76 │ +│ Profit factor │ 0.23 │ +│ Expectancy (Ratio) │ -0.58 (-0.76) │ +│ Avg. daily profit % │ -0.09% │ +│ Avg. stake amount │ 150 USDT │ +│ Total trade volume │ 9009.677 USDT │ +│ │ │ +│ Best Pair │ BTC/USDT 0.08% │ +│ Worst Pair │ SOL/USDT -1.81% │ +│ Best trade │ SOL/USDT 1.41% │ +│ Worst trade │ SOL/USDT -15.00% │ +│ Best day │ 2.151 USDT │ +│ Worst day │ -22.529 USDT │ +│ Days win/draw/lose │ 4 / 14 / 1 │ +│ Avg. Duration Winners │ 0:51:00 │ +│ Avg. Duration Loser │ 1 day, 21:45:00 │ +│ Max Consecutive Wins / Loss │ 3 / 11 │ +│ Rejected Entry signals │ 0 │ +│ Entry/Exit Timeouts │ 0 / 0 │ +│ │ │ +│ Min balance │ 977.471 USDT │ +│ Max balance │ 1000 USDT │ +│ Max % of account underwater │ 2.25% │ +│ Absolute Drawdown (Account) │ 2.25% │ +│ Absolute Drawdown │ 22.529 USDT │ +│ Drawdown high │ 0 USDT │ +│ Drawdown low │ -22.529 USDT │ +│ Drawdown Start │ 2025-04-01 14:54:00 │ +│ Drawdown End │ 2025-04-06 23:15:00 │ +│ Market change │ -0.80% │ +└─────────────────────────────┴─────────────────────┘ + +Backtested 2025-04-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2 + STRATEGY SUMMARY +┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓ +┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩ +│ FreqaiExampleStrategy │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ 22.529 USDT 2.25% │ +└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴────────────────────┘ diff --git a/output_filted.log b/output_filted.log index 3967baef..324a0376 100644 --- a/output_filted.log +++ b/output_filted.log @@ -1,335 +1,385 @@ Creating freqtrade_freqtrade_run ... Creating freqtrade_freqtrade_run ... done -2025-04-29 07:44:18,057 - freqtrade - INFO - freqtrade 2025.3 -2025-04-29 07:44:18,268 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. -2025-04-29 07:44:19,654 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... -2025-04-29 07:44:19,654 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ... -2025-04-29 07:44:19,656 - freqtrade.loggers - INFO - Enabling colorized output. -2025-04-29 07:44:19,657 - root - INFO - Logfile configured -2025-04-29 07:44:19,657 - freqtrade.loggers - INFO - Verbosity set to 0 -2025-04-29 07:44:19,657 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates -2025-04-29 07:44:19,658 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... -2025-04-29 07:44:19,658 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250401-20250420 ... -2025-04-29 07:44:19,698 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... -2025-04-29 07:44:19,699 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... -2025-04-29 07:44:19,699 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... -2025-04-29 07:44:19,700 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250401-20250420 -2025-04-29 07:44:19,700 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor -2025-04-29 07:44:19,701 - freqtrade.exchange.check_exchange - INFO - Checking exchange... -2025-04-29 07:44:19,708 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. -2025-04-29 07:44:19,708 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. -2025-04-29 07:44:19,708 - freqtrade.configuration.config_validation - INFO - Validating configuration ... -2025-04-29 07:44:19,710 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode -2025-04-29 07:44:19,711 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled -2025-04-29 07:44:19,711 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 -2025-04-29 07:44:19,712 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} -2025-04-29 07:44:19,717 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} -2025-04-29 07:44:19,722 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" -2025-04-29 07:44:22,575 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... -2025-04-29 07:44:22,595 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... -2025-04-29 07:44:22,596 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json -2025-04-29 07:44:22,597 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. -2025-04-29 07:44:22,597 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. -2025-04-29 07:44:22,598 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. -2025-04-29 07:44:22,598 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. -2025-04-29 07:44:22,598 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. -2025-04-29 07:44:22,599 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +2025-05-01 04:27:17,240 - freqtrade - INFO - freqtrade 2025.3 +2025-05-01 04:27:17,468 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-05-01 04:27:18,889 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-05-01 04:27:18,891 - freqtrade.loggers - INFO - Enabling colorized output. +2025-05-01 04:27:18,891 - root - INFO - Logfile configured +2025-05-01 04:27:18,892 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-05-01 04:27:18,892 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-05-01 04:27:18,892 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-05-01 04:27:18,893 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250401-20250420 ... +2025-05-01 04:27:18,909 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-05-01 04:27:18,910 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-05-01 04:27:18,910 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-05-01 04:27:18,911 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250401-20250420 +2025-05-01 04:27:18,911 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-05-01 04:27:18,912 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-05-01 04:27:18,918 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-05-01 04:27:18,918 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-05-01 04:27:18,918 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-05-01 04:27:18,920 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-05-01 04:27:18,921 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-05-01 04:27:18,921 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-05-01 04:27:18,922 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-05-01 04:27:18,927 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-05-01 04:27:18,933 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-05-01 04:27:21,480 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-05-01 04:27:21,502 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-05-01 04:27:21,503 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-05-01 04:27:21,503 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-05-01 04:27:21,504 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-05-01 04:27:21,504 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-05-01 04:27:21,504 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-05-01 04:27:21,505 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-05-01 04:27:21,505 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'}. -2025-04-29 07:44:22,599 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. -2025-04-29 07:44:22,599 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} -2025-04-29 07:44:22,600 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m -2025-04-29 07:44:22,600 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 -2025-04-29 07:44:22,600 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True -2025-04-29 07:44:22,601 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 -2025-04-29 07:44:22,601 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 -2025-04-29 07:44:22,602 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False -2025-04-29 07:44:22,602 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False -2025-04-29 07:44:22,602 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True -2025-04-29 07:44:22,603 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +2025-05-01 04:27:21,505 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-05-01 04:27:21,506 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-05-01 04:27:21,507 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-05-01 04:27:21,507 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-05-01 04:27:21,507 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60} -2025-04-29 07:44:22,603 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} -2025-04-29 07:44:22,603 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT -2025-04-29 07:44:22,604 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 -2025-04-29 07:44:22,604 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 -2025-04-29 07:44:22,604 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} -2025-04-29 07:44:22,605 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True -2025-04-29 07:44:22,605 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False -2025-04-29 07:44:22,605 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False -2025-04-29 07:44:22,606 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 -2025-04-29 07:44:22,606 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False -2025-04-29 07:44:22,606 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 -2025-04-29 07:44:22,607 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False -2025-04-29 07:44:22,607 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 -2025-04-29 07:44:22,607 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 -2025-04-29 07:44:22,608 - freqtrade.configuration.config_validation - INFO - Validating configuration ... -2025-04-29 07:44:22,611 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... -2025-04-29 07:44:22,618 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). -2025-04-29 07:44:22,619 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 -2025-04-29 07:44:22,619 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... -2025-04-29 07:44:22,751 - freqtrade.optimize.backtesting - INFO - Loading data from 2025-03-01 21:30:00 up to 2025-04-20 00:00:00 (49 days). -2025-04-29 07:44:22,751 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators -2025-04-29 07:44:22,752 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy -2025-04-29 07:44:24,335 - matplotlib.font_manager - INFO - generated new fontManager -2025-04-29 07:44:24,532 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... -2025-04-29 07:44:24,532 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch -2025-04-29 07:44:24,533 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch -2025-04-29 07:44:24,533 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] -2025-04-29 07:44:24,534 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 -2025-04-29 07:44:24,535 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 -2025-04-29 07:44:24,535 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. -2025-04-29 07:44:24,537 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT -2025-04-29 07:44:24,539 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges -2025-04-29 07:44:24,540 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains -2025-04-29 07:44:24,540 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +2025-05-01 04:27:21,508 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-05-01 04:27:21,509 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-05-01 04:27:21,509 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-05-01 04:27:21,509 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-05-01 04:27:21,510 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-05-01 04:27:21,511 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-05-01 04:27:21,512 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-05-01 04:27:21,512 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-05-01 04:27:21,512 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-05-01 04:27:21,516 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-05-01 04:27:21,522 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-05-01 04:27:21,523 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-05-01 04:27:21,524 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-05-01 04:27:21,658 - freqtrade.optimize.backtesting - INFO - Loading data from 2025-03-01 21:30:00 up to 2025-04-20 00:00:00 (49 days). +2025-05-01 04:27:21,658 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-05-01 04:27:21,659 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-05-01 04:27:23,258 - matplotlib.font_manager - INFO - generated new fontManager +2025-05-01 04:27:23,463 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-05-01 04:27:23,463 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-05-01 04:27:23,464 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-05-01 04:27:23,464 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-05-01 04:27:23,465 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-05-01 04:27:23,465 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-05-01 04:27:23,466 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-05-01 04:27:23,468 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-05-01 04:27:23,470 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges +2025-05-01 04:27:23,471 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains +2025-05-01 04:27:23,471 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at /freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather -2025-04-29 07:44:24,576 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 -2025-04-29 07:44:24,577 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:24,646 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 -2025-04-29 07:44:24,647 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:24,698 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 -2025-04-29 07:44:24,698 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:24,786 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 -2025-04-29 07:44:24,787 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:24,858 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 -2025-04-29 07:44:24,859 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:24,907 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT -2025-04-29 07:44:24,912 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) -2025-04-29 07:44:24,914 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:23,486 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,502 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,519 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,570 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-05-01 04:27:23,571 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:23,623 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,635 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,647 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,688 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-05-01 04:27:23,689 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:23,711 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,721 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,729 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,803 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-05-01 04:27:23,804 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:23,869 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,884 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,901 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:23,965 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-05-01 04:27:23,965 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:24,005 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,016 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,027 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,081 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-05-01 04:27:24,082 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:24,104 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,113 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,122 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:24,176 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:24,182 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-05-01 04:27:24,185 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.007116 50.010488 1 0.005291 50.010488 2 0.004416 50.010488 3 0.002082 50.010488 4 0.001904 50.010488 -2025-04-29 07:44:24,917 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT -2025-04-29 07:44:24,922 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) -2025-04-29 07:44:24,924 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:24,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:24,198 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:24,200 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.007116 49.846666 1 0.005291 49.846666 2 0.004416 49.846666 3 0.002082 49.846666 4 0.001904 49.846666 -2025-04-29 07:44:24,928 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 -2025-04-29 07:44:24,929 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- -2025-04-29 07:44:24,947 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. -2025-04-29 07:44:24,948 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- -2025-04-29 07:44:24,963 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features -2025-04-29 07:44:24,963 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points -/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: [07:44:25] WARNING: /workspace/src/learner.cc:740: +2025-05-01 04:27:24,211 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-05-01 04:27:24,212 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-05-01 04:27:24,234 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:24,235 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-05-01 04:27:24,253 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-05-01 04:27:24,254 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: [04:27:24] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. warnings.warn(smsg, UserWarning) [99] validation_0-rmse:0.13679 validation_1-rmse:0.11901 -2025-04-29 07:44:25,735 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (0.81 secs) -------------------- +2025-05-01 04:27:26,288 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (2.08 secs) -------------------- /home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: -[07:44:25] WARNING: /workspace/src/learner.cc:740: +[04:27:26] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. -2025-04-29 07:44:25,961 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600--buy_rsi.html -2025-04-29 07:44:25,962 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. -2025-04-29 07:44:25,985 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None -2025-04-29 07:44:25,993 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains -2025-04-29 07:44:25,993 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +2025-05-01 04:27:26,516 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600--buy_rsi.html +2025-05-01 04:27:26,517 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:26,539 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:26,546 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains +2025-05-01 04:27:26,547 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at /freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather -2025-04-29 07:44:25,996 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT -2025-04-29 07:44:26,001 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) -2025-04-29 07:44:26,003 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:26,553 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:26,559 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:26,560 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.007116 49.846666 1 0.005291 49.846666 2 0.004416 49.846666 3 0.002082 49.846666 4 0.001904 49.846666 -2025-04-29 07:44:26,007 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT -2025-04-29 07:44:26,013 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,) -2025-04-29 07:44:26,015 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:26,570 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-05-01 04:27:26,576 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,) +2025-05-01 04:27:26,577 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.007116 50.074781 1 0.005291 50.074781 2 0.004416 50.074781 3 0.002082 50.074781 4 0.001904 50.074781 -2025-04-29 07:44:26,019 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 -2025-04-29 07:44:26,020 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- -2025-04-29 07:44:26,040 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. -2025-04-29 07:44:26,040 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- -2025-04-29 07:44:26,055 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features -2025-04-29 07:44:26,056 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-05-01 04:27:26,585 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-05-01 04:27:26,585 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-05-01 04:27:26,603 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:26,603 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-05-01 04:27:26,621 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-05-01 04:27:26,621 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points /home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: -[07:44:26] WARNING: /workspace/src/learner.cc:740: +[04:27:26] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. [99] validation_0-rmse:0.13376 validation_1-rmse:0.11638 -2025-04-29 07:44:26,827 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (0.81 secs) -------------------- -2025-04-29 07:44:26,867 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600--buy_rsi.html -2025-04-29 07:44:26,868 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. -2025-04-29 07:44:26,886 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None -2025-04-29 07:44:26,923 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 -2025-04-29 07:44:26,930 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +2025-05-01 04:27:28,783 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (2.20 secs) -------------------- +/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: + +[04:27:28] WARNING: /workspace/src/learner.cc:740: +Parameters: { "verbose" } are not used. + + +2025-05-01 04:27:28,818 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600--buy_rsi.html +2025-05-01 04:27:28,818 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:28,840 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:28,899 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-05-01 04:27:28,943 - FreqaiExampleStrategy - INFO - up_or_down 值统计： up_or_down 1 11845 0 11726 -2025-04-29 07:44:26,931 - FreqaiExampleStrategy - INFO - do_predict 值统计： +2025-05-01 04:27:28,944 - FreqaiExampleStrategy - INFO - do_predict 值统计： do_predict 0.0 14451 1.0 9120 -2025-04-29 07:44:26,933 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT -2025-04-29 07:44:26,934 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges -2025-04-29 07:44:26,935 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains -2025-04-29 07:44:26,935 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +2025-05-01 04:27:28,947 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-05-01 04:27:28,949 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges +2025-05-01 04:27:28,950 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains +2025-05-01 04:27:28,951 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at /freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather -2025-04-29 07:44:26,959 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 -2025-04-29 07:44:26,959 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:27,020 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 -2025-04-29 07:44:27,020 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:27,068 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 -2025-04-29 07:44:27,068 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00 -2025-04-29 07:44:27,332 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT -2025-04-29 07:44:27,337 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) -2025-04-29 07:44:27,339 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:28,962 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:28,974 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:28,988 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,022 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-05-01 04:27:29,022 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:29,075 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,086 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,097 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,128 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-05-01 04:27:29,129 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:29,151 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,159 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,168 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,228 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-05-01 04:27:29,229 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00 +2025-05-01 04:27:29,283 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,296 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,310 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,367 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,378 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,388 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,442 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,451 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,459 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,535 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,548 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,561 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,630 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,640 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,651 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,735 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,743 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,751 - FreqaiExampleStrategy - INFO - 特征工程完成，特征数量：13 +2025-05-01 04:27:29,827 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:29,832 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-05-01 04:27:29,834 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.016595 49.72136 1 0.012811 49.72136 2 0.010135 49.72136 3 0.008514 49.72136 4 0.006242 49.72136 -2025-04-29 07:44:27,342 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT -2025-04-29 07:44:27,347 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) -2025-04-29 07:44:27,349 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:29,844 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:29,850 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:29,851 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.016595 49.562407 1 0.012811 49.562407 2 0.010135 49.562407 3 0.008514 49.562407 4 0.006242 49.562407 -2025-04-29 07:44:27,353 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 -2025-04-29 07:44:27,354 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- -2025-04-29 07:44:27,378 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. -2025-04-29 07:44:27,379 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- -2025-04-29 07:44:27,402 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features -2025-04-29 07:44:27,402 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-05-01 04:27:29,863 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 +2025-05-01 04:27:29,864 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-05-01 04:27:29,893 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:29,894 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-05-01 04:27:29,919 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-05-01 04:27:29,919 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points /home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: -[07:44:27] WARNING: /workspace/src/learner.cc:740: +[04:27:30] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. [99] validation_0-rmse:0.11848 validation_1-rmse:0.10165 -2025-04-29 07:44:28,610 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (1.26 secs) -------------------- +2025-05-01 04:27:33,175 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (3.31 secs) -------------------- /home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: -[07:44:28] WARNING: /workspace/src/learner.cc:740: +[04:27:33] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. -2025-04-29 07:44:28,642 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600--buy_rsi.html -2025-04-29 07:44:28,643 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. -2025-04-29 07:44:28,668 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None -2025-04-29 07:44:28,675 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains -2025-04-29 07:44:28,675 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +2025-05-01 04:27:33,208 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600--buy_rsi.html +2025-05-01 04:27:33,209 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:33,246 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:33,254 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains +2025-05-01 04:27:33,255 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at /freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather -2025-04-29 07:44:28,679 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT -2025-04-29 07:44:28,684 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) -2025-04-29 07:44:28,685 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:33,265 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:33,270 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-05-01 04:27:33,272 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.016595 49.562407 1 0.012811 49.562407 2 0.010135 49.562407 3 0.008514 49.562407 4 0.006242 49.562407 -2025-04-29 07:44:28,690 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT -2025-04-29 07:44:28,696 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,) -2025-04-29 07:44:28,697 - FreqaiExampleStrategy - INFO - 目标列预览： +2025-05-01 04:27:33,283 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-05-01 04:27:33,289 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,) +2025-05-01 04:27:33,290 - FreqaiExampleStrategy - INFO - 目标列预览： up_or_down &-buy_rsi 0 0.016595 49.934347 1 0.012811 49.934347 2 0.010135 49.934347 3 0.008514 49.934347 4 0.006242 49.934347 -2025-04-29 07:44:28,702 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 -2025-04-29 07:44:28,703 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- -2025-04-29 07:44:28,727 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. -2025-04-29 07:44:28,728 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- -2025-04-29 07:44:28,750 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features -2025-04-29 07:44:28,751 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +2025-05-01 04:27:33,300 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 +2025-05-01 04:27:33,300 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-05-01 04:27:33,322 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-05-01 04:27:33,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-05-01 04:27:33,349 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-05-01 04:27:33,349 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points /home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: -[07:44:28] WARNING: /workspace/src/learner.cc:740: +[04:27:33] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. [99] validation_0-rmse:0.12679 validation_1-rmse:0.11178 -2025-04-29 07:44:29,926 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (1.22 secs) -------------------- +2025-05-01 04:27:36,303 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (3.00 secs) -------------------- /home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: -[07:44:29] WARNING: /workspace/src/learner.cc:740: +[04:27:36] WARNING: /workspace/src/learner.cc:740: Parameters: { "verbose" } are not used. -2025-04-29 07:44:29,964 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600--buy_rsi.html -2025-04-29 07:44:29,964 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. -2025-04-29 07:44:29,989 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None -2025-04-29 07:44:30,034 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 -2025-04-29 07:44:30,041 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +2025-05-01 04:27:36,337 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600--buy_rsi.html +2025-05-01 04:27:36,338 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-05-01 04:27:36,372 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None +2025-05-01 04:27:36,441 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 +2025-05-01 04:27:36,503 - FreqaiExampleStrategy - INFO - up_or_down 值统计： up_or_down 0 11865 1 11706 -2025-04-29 07:44:30,043 - FreqaiExampleStrategy - INFO - do_predict 值统计： +2025-05-01 04:27:36,504 - FreqaiExampleStrategy - INFO - do_predict 值统计： do_predict 0.0 14451 1.0 9120 -2025-04-29 07:44:30,046 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-04-01 00:00:00 up to 2025-04-20 00:00:00 (19 days). -2025-04-29 07:44:30,047 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 -2025-04-29 07:44:30,049 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 -2025-04-29 07:44:30,066 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 -2025-04-29 07:44:30,067 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 -2025-04-29 07:44:30,639 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_07-44-30.meta.json" +2025-05-01 04:27:36,512 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-04-01 00:00:00 up to 2025-04-20 00:00:00 (19 days). +2025-05-01 04:27:36,517 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-05-01 04:27:36,519 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-05-01 04:27:36,549 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-05-01 04:27:36,551 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-05-01 04:27:37,138 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-05-01_04-27-37.meta.json" Result for strategy FreqaiExampleStrategy - BACKTESTING REPORT  + BACKTESTING REPORT ┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ -┃  Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ ┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ BTC/USDT │ 17 │ 0.03 │ 0.801 │ 0.08 │ 11:49:00 │ 2 15 0 100 │ │ SOL/USDT │ 13 │ -0.93 │ -18.095 │ -1.81 │ 8:54:00 │ 5 7 1 38.5 │ │ TOTAL │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ └──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ - LEFT OPEN TRADES REPORT  + LEFT OPEN TRADES REPORT ┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ -┃  Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ ┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │ └───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ - ENTER TAG STATS  + ENTER TAG STATS ┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ -┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃ +┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ ┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ long │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ │ TOTAL │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ └───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ - EXIT REASON STATS  + EXIT REASON STATS ┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ -┃  Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃  Avg Duration ┃  Win Draw Loss Win% ┃ +┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ ┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ roi │ 29 │ 0.12 │ 5.235 │ 0.52 │ 9:21:00 │ 7 22 0 100 │ │ trailing_stop_loss │ 1 │ -15.0 │ -22.529 │ -2.25 │ 1 day, 21:45:00 │ 0 0 1 0 │ │ TOTAL │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ └────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘ - MIXED TAG STATS  + MIXED TAG STATS ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ -┃  Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃  Avg Duration ┃  Win Draw Loss Win% ┃ +┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ ('long', 'roi') │ │ 29 │ 0.12 │ 5.235 │ 0.52 │ 9:21:00 │ 7 22 0 100 │ │ ('long', 'trailing_stop_loss') │ │ 1 │ -15.0 │ -22.529 │ -2.25 │ 1 day, 21:45:00 │ 0 0 1 0 │ │ TOTAL │ │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ └────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘ - SUMMARY METRICS  + SUMMARY METRICS ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ -┃ Metric  ┃ Value  ┃ +┃ Metric ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ Backtesting from │ 2025-04-01 00:00:00 │ │ Backtesting to │ 2025-04-20 00:00:00 │ @@ -378,9 +428,9 @@ Result for strategy FreqaiExampleStrategy └─────────────────────────────┴─────────────────────┘ Backtested 2025-04-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2 - STRATEGY SUMMARY  + STRATEGY SUMMARY ┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓ -┃  Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win Draw Loss Win% ┃  Drawdown ┃ +┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩ │ FreqaiExampleStrategy │ 30 │ -0.38 │ -17.294 │ -1.73 │ 10:34:00 │ 7 22 1 23.3 │ 22.529 USDT 2.25% │ └───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴────────────────────┘ diff --git a/result/backtest-result-2025-05-01_04-27-37.json b/result/backtest-result-2025-05-01_04-27-37.json new file mode 100644 index 00000000..937f21df --- /dev/null +++ b/result/backtest-result-2025-05-01_04-27-37.json @@ -0,0 +1 @@ +{"strategy":{"FreqaiExampleStrategy":{"trades":[{"pair":"BTC/USDT","stake_amount":149.99942125,"max_stake_amount":149.99942125,"amount":0.00178565,"open_date":"2025-04-01 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z_-)?`P$cnIy=x&EL8jzg%$@+sJCZs^aGki`G+NsBlzq2yE+eYdmHBHmWiVC%@6#62yZ=D~pJJn2gZc+}zyw8wMa2Y# z@0W)vUjK{wEe1l;gbM&z5&;09|3KdFkywlOT>k*o*1_A$$<_J) zg7xT+u2*WOhn$llY(18NBKvxh;QgN1y8|5t_^sKA%& zJ`fq$0088_n9}c6{Sz7gpXvXF?4=^Kq)!qX;MRu?ko+$$N|ArY DataFrame: + # 保留关键的技术指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 确保 MACD 列被正确计算并保留 + try: + macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe["macd"] = macd["macd"] + dataframe["macdsignal"] = macd["macdsignal"] + except Exception as e: + logger.error(f"计算 MACD 列时出错：{str(e)}") + dataframe["macd"] = np.nan + dataframe["macdsignal"] = np.nan + + # 检查 MACD 列是否存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 确保 MACD 列存在 + if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号") + raise ValueError("DataFrame 缺少必要的 MACD 列") + + # 保留布林带相关特征 + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + + # 保留成交量相关特征 + dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() + + # 数据清理 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill().fillna(0) + + logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}") + return dataframe + + def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-pct-change"] = dataframe["close"].pct_change() + dataframe["%-raw_volume"] = dataframe["volume"] + dataframe["%-raw_price"] = dataframe["close"] +# 数据清理逻辑 + for col in dataframe.columns: + if dataframe[col].dtype in ["float64", "int64"]: + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0) + dataframe[col] = dataframe[col].ffill() + dataframe[col] = dataframe[col].fillna(0) + + # 检查是否仍有无效值 + if dataframe[col].isna().any() or np.isinf(dataframe[col]).any(): + logger.warning(f"列 {col} 仍包含无效值，已填充为默认值") + dataframe[col] = dataframe[col].fillna(0) + return dataframe + + def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek + dataframe["%-hour_of_day"] = dataframe["date"].dt.hour + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + return dataframe + + def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}") + if "close" not in dataframe.columns: + logger.error("数据框缺少必要的 'close' 列") + raise ValueError("数据框缺少必要的 'close' 列") + + try: + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + + # 定义目标变量为未来价格变化百分比（连续值） + dataframe["up_or_down"] = ( + dataframe["close"].shift(-label_period) - dataframe["close"] + ) / dataframe["close"] + + # 数据清理：处理 NaN 和 Inf 值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 确保目标变量是二维数组 + if dataframe["up_or_down"].ndim == 1: + dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1) + + # 检查并处理 NaN 或无限值 + dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan) + dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0) + + # 生成 %-volatility 特征 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + + # 确保 &-buy_rsi 列的值计算正确 + dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14) + + # 数据清理 + for col in ["&-buy_rsi", "up_or_down", "%-volatility"]: + # 使用直接操作避免链式赋值 + dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan) + dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill') + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值 + if dataframe[col].isna().any(): + logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值") + + except Exception as e: + logger.error(f"创建 FreqAI 目标失败：{str(e)}") + raise + + # Log the shape of the target variable for debugging + logger.info(f"目标列形状：{dataframe['up_or_down'].shape}") + logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}") + return dataframe + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + logger.info(f"处理交易对：{metadata['pair']}") + dataframe = self.freqai.start(dataframe, metadata, self) + + # 计算传统指标 + dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe["bb_lowerband"] = bollinger["lower"] + dataframe["bb_middleband"] = bollinger["mid"] + dataframe["bb_upperband"] = bollinger["upper"] + dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) + + # 生成 up_or_down 信号（非 FreqAI 目标） + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + # 使用未来价格变化方向生成 up_or_down 信号 + label_period = self.freqai_info["feature_parameters"]["label_period_candles"] + dataframe["up_or_down"] = np.where( + dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0 + ) + + # 动态设置参数 + if "&-buy_rsi" in dataframe.columns: + # 派生其他目标 + dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30 + dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std() + # Ensure proper calculation and handle potential NaN values + dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1) + dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0) + + # Additional check to ensure no NaN values remain + for col in ["&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 仍包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0) + + # 简化动态参数生成逻辑 + # 放松 buy_rsi 和 sell_rsi 的生成逻辑 + # 计算 buy_rsi_pred 并清理 NaN 值 + dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50) + dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median()) + + # 计算 sell_rsi_pred 并清理 NaN 值 + dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20 + dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median()) + + # 计算 stoploss_pred 并清理 NaN 值 + dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25) + dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean()) + + # 计算 roi_0_pred 并清理 NaN 值 + dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2) + dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean()) + + # 检查预测值 + for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]: + if dataframe[col].isna().any(): + logger.warning(f"列 {col} 包含 NaN，填充为默认值") + dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) + + # 更保守的止损和止盈设置 + dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2) + dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3) + + # 设置策略级参数 + self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1]) + self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1]) +# 更保守的止损设置 + self.stoploss = -0.15 # 固定止损 15% + self.minimal_roi = { + 0: float(self.roi_0.value), + 15: float(self.roi_15.value), + 30: float(self.roi_30.value), + 60: 0 + } +# 更保守的追踪止损设置 + self.trailing_stop_positive = 0.05 # 追踪止损触发点 + self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量 + + logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, " + f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}") + + dataframe.replace([np.inf, -np.inf], 0, inplace=True) + dataframe.ffill(inplace=True) + dataframe.fillna(0, inplace=True) + + logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}") + logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}") + + return dataframe + + def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: +# 改进卖出信号条件 + exit_long_conditions = [ + (df["rsi"] > df["sell_rsi_pred"]), # RSI 高于卖出阈值 + (df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值 + (df["close"] < df["bb_middleband"]) # 价格低于布林带中轨 + ] + if exit_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, exit_long_conditions), + "exit_long" + ] = 1 + return df + def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + # 改进买入信号条件 + # 检查 MACD 列是否存在 + if "macd" not in df.columns or "macdsignal" not in df.columns: + logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。") + + try: + macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9) + df["macd"] = macd["macd"] + df["macdsignal"] = macd["macdsignal"] + logger.info("MACD 列已成功重新计算。") + except Exception as e: + logger.error(f"重新计算 MACD 列时出错：{str(e)}") + raise ValueError("DataFrame 缺少必要的 MACD 列且无法重新计算。") + + enter_long_conditions = [ + (df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值 + (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20% + (df["close"] > df["bb_middleband"]) # 价格高于布林带中轨 + ] + + # 如果 MACD 列存在，则添加 MACD 金叉条件 + if "macd" in df.columns and "macdsignal" in df.columns: + enter_long_conditions.append((df["macd"] > df["macdsignal"])) + + # 确保模型预测为买入 + enter_long_conditions.append((df["do_predict"] == 1)) + if enter_long_conditions: + df.loc[ + reduce(lambda x, y: x & y, enter_long_conditions), + ["enter_long", "enter_tag"] + ] = (1, "long") + return df + def confirm_trade_entry( + self, pair: str, order_type: str, amount: float, rate: float, + time_in_force: str, current_time, entry_tag, side: str, **kwargs + ) -> bool: + df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + last_candle = df.iloc[-1].squeeze() + if side == "long": + if rate > (last_candle["close"] * (1 + 0.0025)): + return False + return True diff --git a/result/backtest-result-2025-05-01_04-27-37_config.json b/result/backtest-result-2025-05-01_04-27-37_config.json new file mode 100644 index 00000000..122af1de --- /dev/null +++ b/result/backtest-result-2025-05-01_04-27-37_config.json @@ -0,0 +1 @@ +{"$schema":"https://schema.freqtrade.io/schema.json","trading_mode":"spot","margin_mode":"isolated","max_open_trades":4,"stake_currency":"USDT","stake_amount":150,"startup_candle_count":30,"tradable_balance_ratio":1,"fiat_display_currency":"USD","dry_run":true,"timeframe":"3m","dry_run_wallet":1000,"cancel_open_orders_on_exit":true,"stoploss":-0.05,"unfilledtimeout":{"entry":5,"exit":15},"exchange":{"name":"okx","key":"REDACTED","secret":"REDACTED","enable_ws":false,"ccxt_config":{"enableRateLimit":true,"rateLimit":500,"options":{"defaultType":"spot"}},"ccxt_async_config":{"enableRateLimit":true,"rateLimit":500,"timeout":20000},"pair_whitelist":["BTC/USDT","SOL/USDT"],"pair_blacklist":[]},"entry_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1,"price_last_balance":0.0,"check_depth_of_market":{"enabled":false,"bids_to_ask_delta":1}},"exit_pricing":{"price_side":"other","use_order_book":true,"order_book_top":1},"pairlists":[{"method":"StaticPairList"}],"freqai":{"enabled":true,"data_kitchen":{"fillna":"ffill","feature_parameters":{"DI_threshold":0.9,"weight_factor":0.9}},"freqaimodel":"XGBoostRegressor","purge_old_models":2,"identifier":"test175","train_period_days":30,"backtest_period_days":10,"live_retrain_hours":0,"feature_selection":{"method":"recursive_elimination","threshold":0.01},"feature_parameters":{"include_timeframes":["3m","5m","1h"],"include_corr_pairlist":["BTC/USDT","ETH/USDT"],"label_period_candles":12,"include_shifted_candles":3,"indicator_periods_candles":[10,20,50],"plot_feature_importances":1},"data_split_parameters":{"test_size":0.2,"shuffle":true,"random_state":42},"model_training_parameters":{"n_estimators":200,"learning_rate":0.05,"max_depth":5,"subsample":0.8,"colsample_bytree":0.8,"objective":"reg:squarederror","eval_metric":"rmse","early_stopping_rounds":50,"verbose":0}},"api_server":{"enabled":true,"listen_ip_address":"0.0.0.0","listen_port":8080,"verbosity":"error","enable_openapi":false,"jwt_secret_key":"6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca","ws_token":"6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ","CORS_origins":[],"username":"freqAdmin","password":"REDACTED"},"bot_name":"freqtrade","initial_state":"running","force_entry_enable":false,"internals":{"process_throttle_secs":5,"heartbeat_interval":20,"loglevel":"DEBUG"},"config_files":["/freqtrade/config_examples/config_freqai.okx.json"]} 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ze4SR|d9?7vQl0Sch>8DF6XisKLkeM!~kE@{R8AWBdyBR+1TLCbs`K#~zi{j1%eKR8ZMJ>ey9KLOLYh*<3q(q(6=Hd`md0%p#N(A6$d~P z!)KyxfP#J}Ns^f8cL)km@sItxoQR+t&WlXo0+`72A1MgJPbTsSlX(A!`~n&TvJ_A% ziI0F@5=j^qWXeQXV!|SjC+rKVmijlHg7N~*6_gUtDZnDI4output.log 2>&1 +sed -i 's/\x1B\[[0-9;]*m//g' output.log +python3 filter.py + +rm ./result/* -fr +mv ./user_data/backtest_results/* ./result/ + +cd ./result +# 查找当前目录下的所有 zip 文件 +zip_files=(*.zip) + +# 检查是否只有一个 zip 文件 +if [ ${#zip_files[@]} -eq 1 ]; then + # 解压缩该 zip 文件到当前目录 + unzip "${zip_files[0]}" +else + echo "当前目录下没有 zip 文件或者有多个 zip 文件，无法操作。" +fi diff --git a/temp_validation.log b/temp_validation.log new file mode 100644 index 00000000..3d10ae37 --- /dev/null +++ b/temp_validation.log @@ -0,0 +1,2202 @@ +2025-04-29 01:54:56,917 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:59,502 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +[0] validation_0-rmse:0.24624 validation_1-rmse:0.26036 +[1] validation_0-rmse:0.24176 validation_1-rmse:0.25460 +[2] validation_0-rmse:0.23782 validation_1-rmse:0.24904 +[3] validation_0-rmse:0.23408 validation_1-rmse:0.24381 +[4] validation_0-rmse:0.23057 validation_1-rmse:0.23882 +[5] validation_0-rmse:0.22701 validation_1-rmse:0.23409 +[6] validation_0-rmse:0.22400 validation_1-rmse:0.22962 +[7] validation_0-rmse:0.22088 validation_1-rmse:0.22533 +[8] validation_0-rmse:0.21817 validation_1-rmse:0.22130 +[9] validation_0-rmse:0.21491 validation_1-rmse:0.21740 +[10] validation_0-rmse:0.21265 validation_1-rmse:0.21347 +[11] validation_0-rmse:0.20982 validation_1-rmse:0.20978 +[12] validation_0-rmse:0.20747 validation_1-rmse:0.20640 +[13] validation_0-rmse:0.20512 validation_1-rmse:0.20299 +[14] validation_0-rmse:0.20280 validation_1-rmse:0.19966 +[15] validation_0-rmse:0.20012 validation_1-rmse:0.19656 +[16] validation_0-rmse:0.19785 validation_1-rmse:0.19346 +[17] validation_0-rmse:0.19572 validation_1-rmse:0.19054 +[18] validation_0-rmse:0.19400 validation_1-rmse:0.18759 +[19] validation_0-rmse:0.19164 validation_1-rmse:0.18488 +[20] validation_0-rmse:0.18956 validation_1-rmse:0.18205 +[21] validation_0-rmse:0.18746 validation_1-rmse:0.17951 +[22] validation_0-rmse:0.18593 validation_1-rmse:0.17696 +[23] validation_0-rmse:0.18395 validation_1-rmse:0.17465 +[24] validation_0-rmse:0.18249 validation_1-rmse:0.17217 +[25] validation_0-rmse:0.18084 validation_1-rmse:0.16993 +[26] validation_0-rmse:0.17928 validation_1-rmse:0.16771 +[27] validation_0-rmse:0.17776 validation_1-rmse:0.16571 +[28] validation_0-rmse:0.17652 validation_1-rmse:0.16356 +[29] validation_0-rmse:0.17499 validation_1-rmse:0.16166 +[30] validation_0-rmse:0.17371 validation_1-rmse:0.15983 +[31] validation_0-rmse:0.17243 validation_1-rmse:0.15792 +[32] validation_0-rmse:0.17110 validation_1-rmse:0.15628 +[33] validation_0-rmse:0.16996 validation_1-rmse:0.15433 +[34] validation_0-rmse:0.16884 validation_1-rmse:0.15277 +[35] validation_0-rmse:0.16785 validation_1-rmse:0.15090 +[36] validation_0-rmse:0.16682 validation_1-rmse:0.14942 +[37] validation_0-rmse:0.16559 validation_1-rmse:0.14774 +[38] validation_0-rmse:0.16459 validation_1-rmse:0.14628 +[39] validation_0-rmse:0.16356 validation_1-rmse:0.14466 +[40] validation_0-rmse:0.16250 validation_1-rmse:0.14330 +[41] validation_0-rmse:0.16153 validation_1-rmse:0.14201 +[42] validation_0-rmse:0.16059 validation_1-rmse:0.14075 +[43] validation_0-rmse:0.15986 validation_1-rmse:0.13938 +[44] validation_0-rmse:0.15908 validation_1-rmse:0.13822 +[45] validation_0-rmse:0.15810 validation_1-rmse:0.13687 +[46] validation_0-rmse:0.15733 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.15655 validation_1-rmse:0.13458 +[48] validation_0-rmse:0.15580 validation_1-rmse:0.13355 +[49] validation_0-rmse:0.15512 validation_1-rmse:0.13228 +[50] validation_0-rmse:0.15434 validation_1-rmse:0.13121 +[51] validation_0-rmse:0.15363 validation_1-rmse:0.13030 +[52] validation_0-rmse:0.15294 validation_1-rmse:0.12937 +[53] validation_0-rmse:0.15243 validation_1-rmse:0.12818 +[54] validation_0-rmse:0.15170 validation_1-rmse:0.12720 +[55] validation_0-rmse:0.15096 validation_1-rmse:0.12632 +[56] validation_0-rmse:0.15035 validation_1-rmse:0.12538 +[57] validation_0-rmse:0.14977 validation_1-rmse:0.12453 +[58] validation_0-rmse:0.14914 validation_1-rmse:0.12363 +[59] validation_0-rmse:0.14867 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.14819 validation_1-rmse:0.12183 +[61] validation_0-rmse:0.14763 validation_1-rmse:0.12108 +[62] validation_0-rmse:0.14706 validation_1-rmse:0.12035 +[63] validation_0-rmse:0.14648 validation_1-rmse:0.11946 +[64] validation_0-rmse:0.14601 validation_1-rmse:0.11876 +[65] validation_0-rmse:0.14553 validation_1-rmse:0.11808 +[66] validation_0-rmse:0.14506 validation_1-rmse:0.11742 +[67] validation_0-rmse:0.14469 validation_1-rmse:0.11671 +[68] validation_0-rmse:0.14422 validation_1-rmse:0.11604 +[69] validation_0-rmse:0.14381 validation_1-rmse:0.11543 +[70] validation_0-rmse:0.14337 validation_1-rmse:0.11485 +[71] validation_0-rmse:0.14294 validation_1-rmse:0.11398 +[72] validation_0-rmse:0.14260 validation_1-rmse:0.11335 +[73] validation_0-rmse:0.14223 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14190 validation_1-rmse:0.11225 +[75] validation_0-rmse:0.14144 validation_1-rmse:0.11143 +[76] validation_0-rmse:0.14098 validation_1-rmse:0.11052 +[77] validation_0-rmse:0.14062 validation_1-rmse:0.10998 +[78] validation_0-rmse:0.14029 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.13993 validation_1-rmse:0.10888 +[80] validation_0-rmse:0.13958 validation_1-rmse:0.10839 +[81] validation_0-rmse:0.13918 validation_1-rmse:0.10767 +[82] validation_0-rmse:0.13897 validation_1-rmse:0.10720 +[83] validation_0-rmse:0.13864 validation_1-rmse:0.10669 +[84] validation_0-rmse:0.13836 validation_1-rmse:0.10620 +[85] validation_0-rmse:0.13810 validation_1-rmse:0.10573 +[86] validation_0-rmse:0.13782 validation_1-rmse:0.10526 +[87] validation_0-rmse:0.13756 validation_1-rmse:0.10458 +[88] validation_0-rmse:0.13736 validation_1-rmse:0.10420 +[89] validation_0-rmse:0.13708 validation_1-rmse:0.10383 +[90] validation_0-rmse:0.13685 validation_1-rmse:0.10343 +[91] validation_0-rmse:0.13658 validation_1-rmse:0.10298 +[92] validation_0-rmse:0.13646 validation_1-rmse:0.10231 +[93] validation_0-rmse:0.13615 validation_1-rmse:0.10190 +[94] validation_0-rmse:0.13589 validation_1-rmse:0.10154 +[95] validation_0-rmse:0.13572 validation_1-rmse:0.10095 +[96] validation_0-rmse:0.13550 validation_1-rmse:0.10058 +[97] validation_0-rmse:0.13530 validation_1-rmse:0.10026 +[98] validation_0-rmse:0.13513 validation_1-rmse:0.09995 +[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950 +[0] validation_0-rmse:0.26037 validation_1-rmse:0.25324 +[1] validation_0-rmse:0.25572 validation_1-rmse:0.24787 +[2] validation_0-rmse:0.25117 validation_1-rmse:0.24281 +[3] validation_0-rmse:0.24697 validation_1-rmse:0.23802 +[4] validation_0-rmse:0.24328 validation_1-rmse:0.23332 +[5] validation_0-rmse:0.23939 validation_1-rmse:0.22905 +[6] validation_0-rmse:0.23522 validation_1-rmse:0.22484 +[7] validation_0-rmse:0.23148 validation_1-rmse:0.22085 +[8] validation_0-rmse:0.22873 validation_1-rmse:0.21697 +[9] validation_0-rmse:0.22519 validation_1-rmse:0.21317 +[10] validation_0-rmse:0.22206 validation_1-rmse:0.20963 +[11] validation_0-rmse:0.21866 validation_1-rmse:0.20626 +[12] validation_0-rmse:0.21563 validation_1-rmse:0.20296 +[13] validation_0-rmse:0.21313 validation_1-rmse:0.19956 +[14] validation_0-rmse:0.21062 validation_1-rmse:0.19636 +[15] validation_0-rmse:0.20808 validation_1-rmse:0.19339 +[16] validation_0-rmse:0.20570 validation_1-rmse:0.19058 +[17] validation_0-rmse:0.20318 validation_1-rmse:0.18781 +[18] validation_0-rmse:0.20113 validation_1-rmse:0.18518 +[19] validation_0-rmse:0.19934 validation_1-rmse:0.18248 +[20] validation_0-rmse:0.19735 validation_1-rmse:0.18006 +[21] validation_0-rmse:0.19541 validation_1-rmse:0.17744 +[22] validation_0-rmse:0.19336 validation_1-rmse:0.17517 +[23] validation_0-rmse:0.19145 validation_1-rmse:0.17301 +[24] validation_0-rmse:0.18989 validation_1-rmse:0.17058 +[25] validation_0-rmse:0.18782 validation_1-rmse:0.16854 +[26] validation_0-rmse:0.18634 validation_1-rmse:0.16625 +[27] validation_0-rmse:0.18471 validation_1-rmse:0.16430 +[28] validation_0-rmse:0.18312 validation_1-rmse:0.16236 +[29] validation_0-rmse:0.18157 validation_1-rmse:0.16053 +[30] validation_0-rmse:0.17991 validation_1-rmse:0.15849 +[31] validation_0-rmse:0.17839 validation_1-rmse:0.15677 +[32] validation_0-rmse:0.17693 validation_1-rmse:0.15498 +[33] validation_0-rmse:0.17574 validation_1-rmse:0.15336 +[34] validation_0-rmse:0.17469 validation_1-rmse:0.15168 +[35] validation_0-rmse:0.17352 validation_1-rmse:0.15015 +[36] validation_0-rmse:0.17228 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validation_1-rmse:0.11165 +[75] validation_0-rmse:0.13595 validation_1-rmse:0.11109 +[76] validation_0-rmse:0.13585 validation_1-rmse:0.11023 +[77] validation_0-rmse:0.13541 validation_1-rmse:0.10972 +[78] validation_0-rmse:0.13505 validation_1-rmse:0.10920 +[79] validation_0-rmse:0.13465 validation_1-rmse:0.10861 +[80] validation_0-rmse:0.13433 validation_1-rmse:0.10810 +[81] validation_0-rmse:0.13409 validation_1-rmse:0.10744 +[82] validation_0-rmse:0.13377 validation_1-rmse:0.10695 +[83] validation_0-rmse:0.13353 validation_1-rmse:0.10641 +[84] validation_0-rmse:0.13337 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.13329 validation_1-rmse:0.10533 +[86] validation_0-rmse:0.13296 validation_1-rmse:0.10488 +[87] validation_0-rmse:0.13264 validation_1-rmse:0.10442 +[88] validation_0-rmse:0.13247 validation_1-rmse:0.10394 +[89] validation_0-rmse:0.13216 validation_1-rmse:0.10351 +[90] validation_0-rmse:0.13188 validation_1-rmse:0.10297 +[91] validation_0-rmse:0.13145 validation_1-rmse:0.10203 +[92] validation_0-rmse:0.13122 validation_1-rmse:0.10157 +[93] validation_0-rmse:0.13102 validation_1-rmse:0.10118 +[94] validation_0-rmse:0.13060 validation_1-rmse:0.10033 +[95] validation_0-rmse:0.13033 validation_1-rmse:0.09981 +[96] validation_0-rmse:0.13016 validation_1-rmse:0.09933 +[97] validation_0-rmse:0.12995 validation_1-rmse:0.09894 +[98] validation_0-rmse:0.12972 validation_1-rmse:0.09860 +[99] validation_0-rmse:0.12954 validation_1-rmse:0.09825 +[0] validation_0-rmse:0.30616 validation_1-rmse:0.27906 +[1] validation_0-rmse:0.30021 validation_1-rmse:0.27322 +[2] validation_0-rmse:0.29443 validation_1-rmse:0.26757 +[3] validation_0-rmse:0.28911 validation_1-rmse:0.26205 +[4] validation_0-rmse:0.28365 validation_1-rmse:0.25699 +[5] validation_0-rmse:0.27823 validation_1-rmse:0.25219 +[6] validation_0-rmse:0.27295 validation_1-rmse:0.24748 +[7] validation_0-rmse:0.26797 validation_1-rmse:0.24295 +[8] validation_0-rmse:0.26320 validation_1-rmse:0.23854 +[9] validation_0-rmse:0.25898 validation_1-rmse:0.23437 +[10] validation_0-rmse:0.25517 validation_1-rmse:0.23021 +[11] validation_0-rmse:0.25113 validation_1-rmse:0.22639 +[12] validation_0-rmse:0.24762 validation_1-rmse:0.22270 +[13] validation_0-rmse:0.24393 validation_1-rmse:0.21915 +[14] validation_0-rmse:0.24169 validation_1-rmse:0.21579 +[15] validation_0-rmse:0.23898 validation_1-rmse:0.21236 +[16] validation_0-rmse:0.23539 validation_1-rmse:0.20924 +[17] validation_0-rmse:0.23364 validation_1-rmse:0.20621 +[18] validation_0-rmse:0.23062 validation_1-rmse:0.20322 +[19] validation_0-rmse:0.22764 validation_1-rmse:0.20024 +[20] validation_0-rmse:0.22488 validation_1-rmse:0.19731 +[21] validation_0-rmse:0.22211 validation_1-rmse:0.19445 +[22] validation_0-rmse:0.21952 validation_1-rmse:0.19188 +[23] validation_0-rmse:0.21699 validation_1-rmse:0.18935 +[24] validation_0-rmse:0.21549 validation_1-rmse:0.18686 +[25] validation_0-rmse:0.21310 validation_1-rmse:0.18454 +[26] validation_0-rmse:0.21118 validation_1-rmse:0.18198 +[27] validation_0-rmse:0.20904 validation_1-rmse:0.17979 +[28] validation_0-rmse:0.20726 validation_1-rmse:0.17755 +[29] validation_0-rmse:0.20511 validation_1-rmse:0.17547 +[30] validation_0-rmse:0.20336 validation_1-rmse:0.17335 +[31] validation_0-rmse:0.20172 validation_1-rmse:0.17144 +[32] validation_0-rmse:0.19983 validation_1-rmse:0.16961 +[33] validation_0-rmse:0.19794 validation_1-rmse:0.16759 +[34] validation_0-rmse:0.19658 validation_1-rmse:0.16581 +[35] validation_0-rmse:0.19492 validation_1-rmse:0.16409 +[36] validation_0-rmse:0.19347 validation_1-rmse:0.16229 +[37] validation_0-rmse:0.19225 validation_1-rmse:0.16064 +[38] validation_0-rmse:0.19083 validation_1-rmse:0.15877 +[39] validation_0-rmse:0.18921 validation_1-rmse:0.15720 +[40] validation_0-rmse:0.18766 validation_1-rmse:0.15572 +[41] validation_0-rmse:0.18652 validation_1-rmse:0.15414 +[42] validation_0-rmse:0.18519 validation_1-rmse:0.15277 +[43] validation_0-rmse:0.18396 validation_1-rmse:0.15125 +[44] validation_0-rmse:0.18264 validation_1-rmse:0.14968 +[45] validation_0-rmse:0.18134 validation_1-rmse:0.14841 +[46] validation_0-rmse:0.18026 validation_1-rmse:0.14717 +[47] validation_0-rmse:0.17900 validation_1-rmse:0.14594 +[48] validation_0-rmse:0.17815 validation_1-rmse:0.14460 +[49] validation_0-rmse:0.17713 validation_1-rmse:0.14344 +[50] validation_0-rmse:0.17609 validation_1-rmse:0.14232 +[51] validation_0-rmse:0.17502 validation_1-rmse:0.14112 +[52] validation_0-rmse:0.17414 validation_1-rmse:0.13991 +[53] validation_0-rmse:0.17317 validation_1-rmse:0.13889 +[54] validation_0-rmse:0.17267 validation_1-rmse:0.13770 +[55] validation_0-rmse:0.17175 validation_1-rmse:0.13665 +[56] validation_0-rmse:0.17087 validation_1-rmse:0.13573 +[57] validation_0-rmse:0.17001 validation_1-rmse:0.13483 +[58] validation_0-rmse:0.16920 validation_1-rmse:0.13384 +[59] validation_0-rmse:0.16869 validation_1-rmse:0.13280 +[60] validation_0-rmse:0.16790 validation_1-rmse:0.13189 +[61] validation_0-rmse:0.16689 validation_1-rmse:0.13093 +[62] validation_0-rmse:0.16600 validation_1-rmse:0.13007 +[63] validation_0-rmse:0.16548 validation_1-rmse:0.12921 +[64] validation_0-rmse:0.16482 validation_1-rmse:0.12837 +[65] validation_0-rmse:0.16397 validation_1-rmse:0.12747 +[66] validation_0-rmse:0.16316 validation_1-rmse:0.12669 +[67] validation_0-rmse:0.16267 validation_1-rmse:0.12587 +[68] validation_0-rmse:0.16204 validation_1-rmse:0.12501 +[69] validation_0-rmse:0.16159 validation_1-rmse:0.12422 +[70] validation_0-rmse:0.16090 validation_1-rmse:0.12354 +[71] validation_0-rmse:0.16026 validation_1-rmse:0.12282 +[72] validation_0-rmse:0.15986 validation_1-rmse:0.12206 +[73] validation_0-rmse:0.15919 validation_1-rmse:0.12129 +[74] validation_0-rmse:0.15875 validation_1-rmse:0.12061 +[75] validation_0-rmse:0.15829 validation_1-rmse:0.11966 +[76] validation_0-rmse:0.15790 validation_1-rmse:0.11864 +[77] validation_0-rmse:0.15732 validation_1-rmse:0.11802 +[78] validation_0-rmse:0.15696 validation_1-rmse:0.11739 +[79] validation_0-rmse:0.15615 validation_1-rmse:0.11660 +[80] validation_0-rmse:0.15556 validation_1-rmse:0.11593 +[81] validation_0-rmse:0.15516 validation_1-rmse:0.11531 +[82] validation_0-rmse:0.15466 validation_1-rmse:0.11437 +[83] validation_0-rmse:0.15422 validation_1-rmse:0.11383 +[84] validation_0-rmse:0.15382 validation_1-rmse:0.11332 +[85] validation_0-rmse:0.15350 validation_1-rmse:0.11244 +[86] validation_0-rmse:0.15310 validation_1-rmse:0.11180 +[87] validation_0-rmse:0.15277 validation_1-rmse:0.11119 +[88] validation_0-rmse:0.15228 validation_1-rmse:0.11060 +[89] validation_0-rmse:0.15192 validation_1-rmse:0.11011 +[90] validation_0-rmse:0.15144 validation_1-rmse:0.10956 +[91] validation_0-rmse:0.15092 validation_1-rmse:0.10913 +[92] validation_0-rmse:0.15058 validation_1-rmse:0.10847 +[93] validation_0-rmse:0.15017 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.14984 validation_1-rmse:0.10702 +[95] validation_0-rmse:0.14967 validation_1-rmse:0.10629 +[96] validation_0-rmse:0.14914 validation_1-rmse:0.10587 +[97] validation_0-rmse:0.14882 validation_1-rmse:0.10545 +[98] validation_0-rmse:0.14853 validation_1-rmse:0.10454 +[99] validation_0-rmse:0.14837 validation_1-rmse:0.10398 diff --git a/your_output_file.log b/your_output_file.log new file mode 100644 index 00000000..13c51d31 --- /dev/null +++ b/your_output_file.log @@ -0,0 +1,3066 @@ +Creating freqtrade_freqtrade_run ... +Creating freqtrade_freqtrade_run ... done +2025-04-29 01:54:55,246 - freqtrade - INFO - freqtrade 2025.3 +2025-04-29 01:54:55,464 - numexpr.utils - INFO - NumExpr defaulting to 12 threads. +2025-04-29 01:54:56,878 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ... +2025-04-29 01:54:56,879 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ... +2025-04-29 01:54:56,881 - freqtrade.loggers - INFO - Enabling colorized output. +2025-04-29 01:54:56,881 - root - INFO - Logfile configured +2025-04-29 01:54:56,882 - freqtrade.loggers - INFO - Verbosity set to 0 +2025-04-29 01:54:56,882 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ... +2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250101-20250420 ... +2025-04-29 01:54:56,907 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ... +2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250101-20250420 +2025-04-29 01:54:56,909 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor +2025-04-29 01:54:56,910 - freqtrade.exchange.check_exchange - INFO - Checking exchange... +2025-04-29 01:54:56,916 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team. +2025-04-29 01:54:56,916 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration. +2025-04-29 01:54:56,917 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:56,919 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode +2025-04-29 01:54:56,919 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69 +2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}} +2025-04-29 01:54:56,925 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000} +2025-04-29 01:54:56,931 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX" +2025-04-29 01:54:59,471 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'... +2025-04-29 01:54:59,491 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'... +2025-04-29 01:54:59,491 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m. +2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150. +2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': +'minutes'}. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4. +2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0} +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05 +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True +2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02 +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False +2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, +'stoploss_on_exchange_interval': 60} +2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'} +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150 +2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30 +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'} +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True +2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0 +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False +2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1 +2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4 +2025-04-29 01:54:59,502 - freqtrade.configuration.config_validation - INFO - Validating configuration ... +2025-04-29 01:54:59,505 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'... +2025-04-29 01:54:59,512 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier). +2025-04-29 01:54:59,512 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:54:59,513 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ... +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Loading data from 2024-12-01 21:30:00 up to 2025-04-20 00:00:00 (139 days). +2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators +2025-04-29 01:54:59,673 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy +2025-04-29 01:55:01,274 - matplotlib.font_manager - INFO - generated new fontManager +2025-04-29 01:55:01,489 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'... +2025-04-29 01:55:01,490 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch +2025-04-29 01:55:01,491 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT'] +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069 +2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067 +2025-04-29 01:55:01,493 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values. +2025-04-29 01:55:01,498 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT +2025-04-29 01:55:01,500 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:55:01,501 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:55:01,502 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1735689600_prediction.feather +2025-04-29 01:55:01,602 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,603 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,705 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:01,706 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,814 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:55:01,815 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:01,942 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:55:01,943 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,037 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:55:02,038 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:55:02,113 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,118 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:55:02,121 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.152831 +1 0.003285 50.152831 +2 0.001898 50.152831 +3 0.000484 50.152831 +4 0.001688 50.152831 +2025-04-29 01:55:02,123 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:02,129 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:02,130 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:02,134 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1735689600/cb_btc_1735689600 +2025-04-29 01:55:02,135 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:02,151 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:02,152 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:55:07,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:07,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24624 validation_1-rmse:0.26036 +[1] validation_0-rmse:0.24176 validation_1-rmse:0.25460 +[2] validation_0-rmse:0.23782 validation_1-rmse:0.24904 +[3] validation_0-rmse:0.23408 validation_1-rmse:0.24381 +[4] validation_0-rmse:0.23057 validation_1-rmse:0.23882 +[5] validation_0-rmse:0.22701 validation_1-rmse:0.23409 +[6] validation_0-rmse:0.22400 validation_1-rmse:0.22962 +[7] validation_0-rmse:0.22088 validation_1-rmse:0.22533 +[8] validation_0-rmse:0.21817 validation_1-rmse:0.22130 +[9] validation_0-rmse:0.21491 validation_1-rmse:0.21740 +[10] validation_0-rmse:0.21265 validation_1-rmse:0.21347 +[11] validation_0-rmse:0.20982 validation_1-rmse:0.20978 +[12] validation_0-rmse:0.20747 validation_1-rmse:0.20640 +[13] validation_0-rmse:0.20512 validation_1-rmse:0.20299 +[14] validation_0-rmse:0.20280 validation_1-rmse:0.19966 +[15] validation_0-rmse:0.20012 validation_1-rmse:0.19656 +[16] validation_0-rmse:0.19785 validation_1-rmse:0.19346 +[17] validation_0-rmse:0.19572 validation_1-rmse:0.19054 +[18] validation_0-rmse:0.19400 validation_1-rmse:0.18759 +[19] validation_0-rmse:0.19164 validation_1-rmse:0.18488 +[20] validation_0-rmse:0.18956 validation_1-rmse:0.18205 +[21] validation_0-rmse:0.18746 validation_1-rmse:0.17951 +[22] validation_0-rmse:0.18593 validation_1-rmse:0.17696 +[23] validation_0-rmse:0.18395 validation_1-rmse:0.17465 +[24] validation_0-rmse:0.18249 validation_1-rmse:0.17217 +[25] validation_0-rmse:0.18084 validation_1-rmse:0.16993 +[26] validation_0-rmse:0.17928 validation_1-rmse:0.16771 +[27] validation_0-rmse:0.17776 validation_1-rmse:0.16571 +[28] validation_0-rmse:0.17652 validation_1-rmse:0.16356 +[29] validation_0-rmse:0.17499 validation_1-rmse:0.16166 +[30] validation_0-rmse:0.17371 validation_1-rmse:0.15983 +[31] validation_0-rmse:0.17243 validation_1-rmse:0.15792 +[32] validation_0-rmse:0.17110 validation_1-rmse:0.15628 +[33] validation_0-rmse:0.16996 validation_1-rmse:0.15433 +[34] validation_0-rmse:0.16884 validation_1-rmse:0.15277 +[35] validation_0-rmse:0.16785 validation_1-rmse:0.15090 +[36] validation_0-rmse:0.16682 validation_1-rmse:0.14942 +[37] validation_0-rmse:0.16559 validation_1-rmse:0.14774 +[38] validation_0-rmse:0.16459 validation_1-rmse:0.14628 +[39] validation_0-rmse:0.16356 validation_1-rmse:0.14466 +[40] validation_0-rmse:0.16250 validation_1-rmse:0.14330 +[41] validation_0-rmse:0.16153 validation_1-rmse:0.14201 +[42] validation_0-rmse:0.16059 validation_1-rmse:0.14075 +[43] validation_0-rmse:0.15986 validation_1-rmse:0.13938 +[44] validation_0-rmse:0.15908 validation_1-rmse:0.13822 +[45] validation_0-rmse:0.15810 validation_1-rmse:0.13687 +[46] validation_0-rmse:0.15733 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.15655 validation_1-rmse:0.13458 +[48] validation_0-rmse:0.15580 validation_1-rmse:0.13355 +[49] validation_0-rmse:0.15512 validation_1-rmse:0.13228 +[50] validation_0-rmse:0.15434 validation_1-rmse:0.13121 +[51] validation_0-rmse:0.15363 validation_1-rmse:0.13030 +[52] validation_0-rmse:0.15294 validation_1-rmse:0.12937 +[53] validation_0-rmse:0.15243 validation_1-rmse:0.12818 +[54] validation_0-rmse:0.15170 validation_1-rmse:0.12720 +[55] validation_0-rmse:0.15096 validation_1-rmse:0.12632 +[56] validation_0-rmse:0.15035 validation_1-rmse:0.12538 +[57] validation_0-rmse:0.14977 validation_1-rmse:0.12453 +[58] validation_0-rmse:0.14914 validation_1-rmse:0.12363 +[59] validation_0-rmse:0.14867 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.14819 validation_1-rmse:0.12183 +[61] validation_0-rmse:0.14763 validation_1-rmse:0.12108 +[62] validation_0-rmse:0.14706 validation_1-rmse:0.12035 +[63] validation_0-rmse:0.14648 validation_1-rmse:0.11946 +[64] validation_0-rmse:0.14601 validation_1-rmse:0.11876 +[65] validation_0-rmse:0.14553 validation_1-rmse:0.11808 +[66] validation_0-rmse:0.14506 validation_1-rmse:0.11742 +[67] validation_0-rmse:0.14469 validation_1-rmse:0.11671 +[68] validation_0-rmse:0.14422 validation_1-rmse:0.11604 +[69] validation_0-rmse:0.14381 validation_1-rmse:0.11543 +[70] validation_0-rmse:0.14337 validation_1-rmse:0.11485 +[71] validation_0-rmse:0.14294 validation_1-rmse:0.11398 +[72] validation_0-rmse:0.14260 validation_1-rmse:0.11335 +[73] validation_0-rmse:0.14223 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14190 validation_1-rmse:0.11225 +[75] validation_0-rmse:0.14144 validation_1-rmse:0.11143 +[76] validation_0-rmse:0.14098 validation_1-rmse:0.11052 +[77] validation_0-rmse:0.14062 validation_1-rmse:0.10998 +[78] validation_0-rmse:0.14029 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.13993 validation_1-rmse:0.10888 +[80] validation_0-rmse:0.13958 validation_1-rmse:0.10839 +[81] validation_0-rmse:0.13918 validation_1-rmse:0.10767 +[82] validation_0-rmse:0.13897 validation_1-rmse:0.10720 +[83] validation_0-rmse:0.13864 validation_1-rmse:0.10669 +[84] validation_0-rmse:0.13836 validation_1-rmse:0.10620 +[85] validation_0-rmse:0.13810 validation_1-rmse:0.10573 +[86] validation_0-rmse:0.13782 validation_1-rmse:0.10526 +[87] validation_0-rmse:0.13756 validation_1-rmse:0.10458 +[88] validation_0-rmse:0.13736 validation_1-rmse:0.10420 +[89] validation_0-rmse:0.13708 validation_1-rmse:0.10383 +[90] validation_0-rmse:0.13685 validation_1-rmse:0.10343 +[91] validation_0-rmse:0.13658 validation_1-rmse:0.10298 +[92] validation_0-rmse:0.13646 validation_1-rmse:0.10231 +[93] validation_0-rmse:0.13615 validation_1-rmse:0.10190 +[94] validation_0-rmse:0.13589 validation_1-rmse:0.10154 +[95] validation_0-rmse:0.13572 validation_1-rmse:0.10095 +[96] validation_0-rmse:0.13550 validation_1-rmse:0.10058 +[97] validation_0-rmse:0.13530 validation_1-rmse:0.10026 +[98] validation_0-rmse:0.13513 validation_1-rmse:0.09995 +[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950 +2025-04-29 01:55:08,221 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.09 secs) -------------------- +2025-04-29 01:55:08,222 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:08,903 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:55:08,904 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1736553600_prediction.feather +2025-04-29 01:55:08,907 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,912 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:55:08,914 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.202701 +1 0.003285 50.202701 +2 0.001898 50.202701 +3 0.000484 50.202701 +4 0.001688 50.202701 +2025-04-29 01:55:08,917 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:08,924 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:08,925 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:08,929 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1736553600/cb_btc_1736553600 +2025-04-29 01:55:08,930 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:08,946 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:08,947 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:55:13,908 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:55:13,911 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:13,912 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26037 validation_1-rmse:0.25324 +[1] validation_0-rmse:0.25572 validation_1-rmse:0.24787 +[2] validation_0-rmse:0.25117 validation_1-rmse:0.24281 +[3] validation_0-rmse:0.24697 validation_1-rmse:0.23802 +[4] validation_0-rmse:0.24328 validation_1-rmse:0.23332 +[5] validation_0-rmse:0.23939 validation_1-rmse:0.22905 +[6] validation_0-rmse:0.23522 validation_1-rmse:0.22484 +[7] validation_0-rmse:0.23148 validation_1-rmse:0.22085 +[8] validation_0-rmse:0.22873 validation_1-rmse:0.21697 +[9] validation_0-rmse:0.22519 validation_1-rmse:0.21317 +[10] validation_0-rmse:0.22206 validation_1-rmse:0.20963 +[11] validation_0-rmse:0.21866 validation_1-rmse:0.20626 +[12] validation_0-rmse:0.21563 validation_1-rmse:0.20296 +[13] validation_0-rmse:0.21313 validation_1-rmse:0.19956 +[14] validation_0-rmse:0.21062 validation_1-rmse:0.19636 +[15] validation_0-rmse:0.20808 validation_1-rmse:0.19339 +[16] validation_0-rmse:0.20570 validation_1-rmse:0.19058 +[17] validation_0-rmse:0.20318 validation_1-rmse:0.18781 +[18] validation_0-rmse:0.20113 validation_1-rmse:0.18518 +[19] validation_0-rmse:0.19934 validation_1-rmse:0.18248 +[20] validation_0-rmse:0.19735 validation_1-rmse:0.18006 +[21] validation_0-rmse:0.19541 validation_1-rmse:0.17744 +[22] validation_0-rmse:0.19336 validation_1-rmse:0.17517 +[23] validation_0-rmse:0.19145 validation_1-rmse:0.17301 +[24] validation_0-rmse:0.18989 validation_1-rmse:0.17058 +[25] validation_0-rmse:0.18782 validation_1-rmse:0.16854 +[26] validation_0-rmse:0.18634 validation_1-rmse:0.16625 +[27] validation_0-rmse:0.18471 validation_1-rmse:0.16430 +[28] validation_0-rmse:0.18312 validation_1-rmse:0.16236 +[29] validation_0-rmse:0.18157 validation_1-rmse:0.16053 +[30] validation_0-rmse:0.17991 validation_1-rmse:0.15849 +[31] validation_0-rmse:0.17839 validation_1-rmse:0.15677 +[32] validation_0-rmse:0.17693 validation_1-rmse:0.15498 +[33] validation_0-rmse:0.17574 validation_1-rmse:0.15336 +[34] validation_0-rmse:0.17469 validation_1-rmse:0.15168 +[35] validation_0-rmse:0.17352 validation_1-rmse:0.15015 +[36] validation_0-rmse:0.17228 validation_1-rmse:0.14868 +[37] validation_0-rmse:0.17127 validation_1-rmse:0.14692 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.14553 +[39] validation_0-rmse:0.16926 validation_1-rmse:0.14420 +[40] validation_0-rmse:0.16821 validation_1-rmse:0.14297 +[41] validation_0-rmse:0.16740 validation_1-rmse:0.14144 +[42] validation_0-rmse:0.16647 validation_1-rmse:0.14020 +[43] validation_0-rmse:0.16548 validation_1-rmse:0.13903 +[44] validation_0-rmse:0.16440 validation_1-rmse:0.13765 +[45] validation_0-rmse:0.16353 validation_1-rmse:0.13652 +[46] validation_0-rmse:0.16269 validation_1-rmse:0.13522 +[47] validation_0-rmse:0.16193 validation_1-rmse:0.13419 +[48] validation_0-rmse:0.16114 validation_1-rmse:0.13311 +[49] validation_0-rmse:0.16043 validation_1-rmse:0.13214 +[50] validation_0-rmse:0.15971 validation_1-rmse:0.13090 +[51] validation_0-rmse:0.15909 validation_1-rmse:0.12992 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.12899 +[53] validation_0-rmse:0.15763 validation_1-rmse:0.12809 +[54] validation_0-rmse:0.15697 validation_1-rmse:0.12724 +[55] validation_0-rmse:0.15631 validation_1-rmse:0.12637 +[56] validation_0-rmse:0.15553 validation_1-rmse:0.12535 +[57] validation_0-rmse:0.15494 validation_1-rmse:0.12456 +[58] validation_0-rmse:0.15452 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.15396 validation_1-rmse:0.12273 +[60] validation_0-rmse:0.15334 validation_1-rmse:0.12196 +[61] validation_0-rmse:0.15274 validation_1-rmse:0.12123 +[62] validation_0-rmse:0.15221 validation_1-rmse:0.12048 +[63] validation_0-rmse:0.15176 validation_1-rmse:0.11953 +[64] validation_0-rmse:0.15133 validation_1-rmse:0.11887 +[65] validation_0-rmse:0.15080 validation_1-rmse:0.11796 +[66] validation_0-rmse:0.15035 validation_1-rmse:0.11734 +[67] validation_0-rmse:0.14995 validation_1-rmse:0.11667 +[68] validation_0-rmse:0.14954 validation_1-rmse:0.11616 +[69] validation_0-rmse:0.14916 validation_1-rmse:0.11535 +[70] validation_0-rmse:0.14887 validation_1-rmse:0.11469 +[71] validation_0-rmse:0.14854 validation_1-rmse:0.11408 +[72] validation_0-rmse:0.14811 validation_1-rmse:0.11334 +[73] validation_0-rmse:0.14766 validation_1-rmse:0.11278 +[74] validation_0-rmse:0.14738 validation_1-rmse:0.11231 +[75] validation_0-rmse:0.14697 validation_1-rmse:0.11184 +[76] validation_0-rmse:0.14663 validation_1-rmse:0.11108 +[77] validation_0-rmse:0.14635 validation_1-rmse:0.11058 +[78] validation_0-rmse:0.14591 validation_1-rmse:0.10984 +[79] validation_0-rmse:0.14561 validation_1-rmse:0.10929 +[80] validation_0-rmse:0.14529 validation_1-rmse:0.10875 +[81] validation_0-rmse:0.14510 validation_1-rmse:0.10826 +[82] validation_0-rmse:0.14471 validation_1-rmse:0.10772 +[83] validation_0-rmse:0.14444 validation_1-rmse:0.10725 +[84] validation_0-rmse:0.14420 validation_1-rmse:0.10652 +[85] validation_0-rmse:0.14393 validation_1-rmse:0.10608 +[86] validation_0-rmse:0.14371 validation_1-rmse:0.10567 +[87] validation_0-rmse:0.14342 validation_1-rmse:0.10528 +[88] validation_0-rmse:0.14314 validation_1-rmse:0.10483 +[89] validation_0-rmse:0.14307 validation_1-rmse:0.10439 +[90] validation_0-rmse:0.14273 validation_1-rmse:0.10395 +[91] validation_0-rmse:0.14237 validation_1-rmse:0.10353 +[92] validation_0-rmse:0.14210 validation_1-rmse:0.10318 +[93] validation_0-rmse:0.14186 validation_1-rmse:0.10279 +[94] validation_0-rmse:0.14175 validation_1-rmse:0.10234 +[95] validation_0-rmse:0.14153 validation_1-rmse:0.10204 +[96] validation_0-rmse:0.14142 validation_1-rmse:0.10160 +[97] validation_0-rmse:0.14124 validation_1-rmse:0.10126 +[98] validation_0-rmse:0.14102 validation_1-rmse:0.10068 +[99] validation_0-rmse:0.14079 validation_1-rmse:0.10036 +2025-04-29 01:55:14,692 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.76 secs) -------------------- +2025-04-29 01:55:14,693 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:15,250 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:55:15,250 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1737417600_prediction.feather +2025-04-29 01:55:15,254 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,261 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:55:15,262 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.367593 +1 0.003285 50.367593 +2 0.001898 50.367593 +3 0.000484 50.367593 +4 0.001688 50.367593 +2025-04-29 01:55:15,268 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:15,275 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:15,276 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:15,281 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1737417600/cb_btc_1737417600 +2025-04-29 01:55:15,281 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:15,297 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:15,298 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:55:20,324 - datasieve.pipeline - INFO - DI tossed 1622 predictions for being too far from training data. +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25769 validation_1-rmse:0.25549 +[1] validation_0-rmse:0.25314 validation_1-rmse:0.24986 +[2] validation_0-rmse:0.24864 validation_1-rmse:0.24456 +[3] validation_0-rmse:0.24486 validation_1-rmse:0.23955 +[4] validation_0-rmse:0.24144 validation_1-rmse:0.23480 +[5] validation_0-rmse:0.23803 validation_1-rmse:0.23024 +[6] validation_0-rmse:0.23468 validation_1-rmse:0.22599 +[7] validation_0-rmse:0.23134 validation_1-rmse:0.22162 +[8] validation_0-rmse:0.22843 validation_1-rmse:0.21773 +[9] validation_0-rmse:0.22560 validation_1-rmse:0.21396 +[10] validation_0-rmse:0.22402 validation_1-rmse:0.21023 +[11] validation_0-rmse:0.22155 validation_1-rmse:0.20680 +[12] validation_0-rmse:0.21899 validation_1-rmse:0.20342 +[13] validation_0-rmse:0.21654 validation_1-rmse:0.20029 +[14] validation_0-rmse:0.21431 validation_1-rmse:0.19719 +[15] validation_0-rmse:0.21282 validation_1-rmse:0.19411 +[16] validation_0-rmse:0.21076 validation_1-rmse:0.19117 +[17] validation_0-rmse:0.20882 validation_1-rmse:0.18835 +[18] validation_0-rmse:0.20695 validation_1-rmse:0.18547 +[19] validation_0-rmse:0.20538 validation_1-rmse:0.18292 +[20] validation_0-rmse:0.20345 validation_1-rmse:0.18038 +[21] validation_0-rmse:0.20148 validation_1-rmse:0.17799 +[22] validation_0-rmse:0.19991 validation_1-rmse:0.17569 +[23] validation_0-rmse:0.19832 validation_1-rmse:0.17350 +[24] validation_0-rmse:0.19658 validation_1-rmse:0.17096 +[25] validation_0-rmse:0.19474 validation_1-rmse:0.16879 +[26] validation_0-rmse:0.19292 validation_1-rmse:0.16665 +[27] validation_0-rmse:0.19134 validation_1-rmse:0.16470 +[28] validation_0-rmse:0.19034 validation_1-rmse:0.16253 +[29] validation_0-rmse:0.18882 validation_1-rmse:0.16068 +[30] validation_0-rmse:0.18736 validation_1-rmse:0.15892 +[31] validation_0-rmse:0.18605 validation_1-rmse:0.15690 +[32] validation_0-rmse:0.18481 validation_1-rmse:0.15521 +[33] validation_0-rmse:0.18346 validation_1-rmse:0.15356 +[34] validation_0-rmse:0.18222 validation_1-rmse:0.15188 +[35] validation_0-rmse:0.18095 validation_1-rmse:0.15028 +[36] validation_0-rmse:0.18015 validation_1-rmse:0.14857 +[37] validation_0-rmse:0.17915 validation_1-rmse:0.14713 +[38] validation_0-rmse:0.17817 validation_1-rmse:0.14573 +[39] validation_0-rmse:0.17723 validation_1-rmse:0.14437 +[40] validation_0-rmse:0.17619 validation_1-rmse:0.14308 +[41] validation_0-rmse:0.17509 validation_1-rmse:0.14176 +[42] validation_0-rmse:0.17407 validation_1-rmse:0.14047 +[43] validation_0-rmse:0.17340 validation_1-rmse:0.13921 +[44] validation_0-rmse:0.17245 validation_1-rmse:0.13806 +[45] validation_0-rmse:0.17212 validation_1-rmse:0.13685 +[46] validation_0-rmse:0.17133 validation_1-rmse:0.13577 +[47] validation_0-rmse:0.17064 validation_1-rmse:0.13451 +[48] validation_0-rmse:0.17004 validation_1-rmse:0.13331 +[49] validation_0-rmse:0.16941 validation_1-rmse:0.13222 +[50] validation_0-rmse:0.16858 validation_1-rmse:0.13123 +[51] validation_0-rmse:0.16786 validation_1-rmse:0.13007 +[52] validation_0-rmse:0.16718 validation_1-rmse:0.12912 +[53] validation_0-rmse:0.16651 validation_1-rmse:0.12806 +[54] validation_0-rmse:0.16592 validation_1-rmse:0.12709 +[55] validation_0-rmse:0.16542 validation_1-rmse:0.12604 +[56] validation_0-rmse:0.16479 validation_1-rmse:0.12523 +[57] validation_0-rmse:0.16426 validation_1-rmse:0.12439 +[58] validation_0-rmse:0.16363 validation_1-rmse:0.12352 +[59] validation_0-rmse:0.16325 validation_1-rmse:0.12263 +[60] validation_0-rmse:0.16289 validation_1-rmse:0.12173 +[61] validation_0-rmse:0.16226 validation_1-rmse:0.12099 +[62] validation_0-rmse:0.16176 validation_1-rmse:0.12010 +[63] validation_0-rmse:0.16144 validation_1-rmse:0.11936 +[64] validation_0-rmse:0.16088 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.16030 validation_1-rmse:0.11786 +[66] validation_0-rmse:0.15991 validation_1-rmse:0.11714 +[67] validation_0-rmse:0.15947 validation_1-rmse:0.11640 +[68] validation_0-rmse:0.15912 validation_1-rmse:0.11574 +[69] validation_0-rmse:0.15874 validation_1-rmse:0.11507 +[70] validation_0-rmse:0.15837 validation_1-rmse:0.11430 +[71] validation_0-rmse:0.15798 validation_1-rmse:0.11365 +[72] validation_0-rmse:0.15763 validation_1-rmse:0.11305 +[73] validation_0-rmse:0.15713 validation_1-rmse:0.11250 +[74] validation_0-rmse:0.15648 validation_1-rmse:0.11177 +[75] validation_0-rmse:0.15619 validation_1-rmse:0.11122 +[76] validation_0-rmse:0.15593 validation_1-rmse:0.11066 +[77] validation_0-rmse:0.15562 validation_1-rmse:0.11007 +[78] validation_0-rmse:0.15519 validation_1-rmse:0.10953 +[79] validation_0-rmse:0.15500 validation_1-rmse:0.10883 +[80] validation_0-rmse:0.15461 validation_1-rmse:0.10835 +[81] validation_0-rmse:0.15417 validation_1-rmse:0.10780 +[82] validation_0-rmse:0.15393 validation_1-rmse:0.10742 +[83] validation_0-rmse:0.15395 validation_1-rmse:0.10634 +[84] validation_0-rmse:0.15359 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.15315 validation_1-rmse:0.10539 +[86] validation_0-rmse:0.15315 validation_1-rmse:0.10440 +[87] validation_0-rmse:0.15278 validation_1-rmse:0.10400 +[88] validation_0-rmse:0.15239 validation_1-rmse:0.10353 +[89] validation_0-rmse:0.15200 validation_1-rmse:0.10310 +[90] validation_0-rmse:0.15182 validation_1-rmse:0.10245 +[91] validation_0-rmse:0.15175 validation_1-rmse:0.10182 +[92] validation_0-rmse:0.15139 validation_1-rmse:0.10138 +[93] validation_0-rmse:0.15105 validation_1-rmse:0.10095 +[94] validation_0-rmse:0.15091 validation_1-rmse:0.10056 +[95] validation_0-rmse:0.15088 validation_1-rmse:0.09964 +[96] validation_0-rmse:0.15065 validation_1-rmse:0.09927 +[97] validation_0-rmse:0.15036 validation_1-rmse:0.09888 +[98] validation_0-rmse:0.15021 validation_1-rmse:0.09852 +[99] validation_0-rmse:0.15004 validation_1-rmse:0.09815 +2025-04-29 01:55:21,007 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.73 secs) -------------------- +2025-04-29 01:55:21,008 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:21,504 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:55:21,505 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1738281600_prediction.feather +2025-04-29 01:55:21,510 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,516 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:55:21,517 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.305589 +1 0.003285 50.305589 +2 0.001898 50.305589 +3 0.000484 50.305589 +4 0.001688 50.305589 +2025-04-29 01:55:21,522 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:21,528 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:21,529 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:21,533 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1738281600/cb_btc_1738281600 +2025-04-29 01:55:21,534 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:21,550 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:21,550 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:55:26,605 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:26,606 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25046 validation_1-rmse:0.26128 +[1] validation_0-rmse:0.24588 validation_1-rmse:0.25570 +[2] validation_0-rmse:0.24156 validation_1-rmse:0.25047 +[3] validation_0-rmse:0.23757 validation_1-rmse:0.24551 +[4] validation_0-rmse:0.23411 validation_1-rmse:0.24075 +[5] validation_0-rmse:0.23029 validation_1-rmse:0.23637 +[6] validation_0-rmse:0.22707 validation_1-rmse:0.23199 +[7] validation_0-rmse:0.22405 validation_1-rmse:0.22801 +[8] validation_0-rmse:0.22083 validation_1-rmse:0.22420 +[9] validation_0-rmse:0.21768 validation_1-rmse:0.22038 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.21674 +[11] validation_0-rmse:0.21187 validation_1-rmse:0.21322 +[12] validation_0-rmse:0.20911 validation_1-rmse:0.20996 +[13] validation_0-rmse:0.20669 validation_1-rmse:0.20679 +[14] validation_0-rmse:0.20441 validation_1-rmse:0.20366 +[15] validation_0-rmse:0.20250 validation_1-rmse:0.20054 +[16] validation_0-rmse:0.20017 validation_1-rmse:0.19757 +[17] validation_0-rmse:0.19804 validation_1-rmse:0.19490 +[18] validation_0-rmse:0.19618 validation_1-rmse:0.19221 +[19] validation_0-rmse:0.19404 validation_1-rmse:0.18954 +[20] validation_0-rmse:0.19209 validation_1-rmse:0.18666 +[21] validation_0-rmse:0.19014 validation_1-rmse:0.18430 +[22] validation_0-rmse:0.18845 validation_1-rmse:0.18197 +[23] validation_0-rmse:0.18653 validation_1-rmse:0.17972 +[24] validation_0-rmse:0.18468 validation_1-rmse:0.17722 +[25] validation_0-rmse:0.18325 validation_1-rmse:0.17491 +[26] validation_0-rmse:0.18152 validation_1-rmse:0.17284 +[27] validation_0-rmse:0.17999 validation_1-rmse:0.17092 +[28] validation_0-rmse:0.17846 validation_1-rmse:0.16892 +[29] validation_0-rmse:0.17696 validation_1-rmse:0.16709 +[30] validation_0-rmse:0.17558 validation_1-rmse:0.16510 +[31] validation_0-rmse:0.17418 validation_1-rmse:0.16335 +[32] validation_0-rmse:0.17293 validation_1-rmse:0.16161 +[33] validation_0-rmse:0.17159 validation_1-rmse:0.16003 +[34] validation_0-rmse:0.17030 validation_1-rmse:0.15831 +[35] validation_0-rmse:0.16907 validation_1-rmse:0.15681 +[36] validation_0-rmse:0.16796 validation_1-rmse:0.15513 +[37] validation_0-rmse:0.16690 validation_1-rmse:0.15349 +[38] validation_0-rmse:0.16580 validation_1-rmse:0.15204 +[39] validation_0-rmse:0.16492 validation_1-rmse:0.15050 +[40] validation_0-rmse:0.16383 validation_1-rmse:0.14918 +[41] validation_0-rmse:0.16281 validation_1-rmse:0.14788 +[42] validation_0-rmse:0.16176 validation_1-rmse:0.14660 +[43] validation_0-rmse:0.16082 validation_1-rmse:0.14516 +[44] validation_0-rmse:0.15990 validation_1-rmse:0.14395 +[45] validation_0-rmse:0.15891 validation_1-rmse:0.14281 +[46] validation_0-rmse:0.15797 validation_1-rmse:0.14168 +[47] validation_0-rmse:0.15712 validation_1-rmse:0.14040 +[48] validation_0-rmse:0.15632 validation_1-rmse:0.13933 +[49] validation_0-rmse:0.15542 validation_1-rmse:0.13821 +[50] validation_0-rmse:0.15458 validation_1-rmse:0.13705 +[51] validation_0-rmse:0.15404 validation_1-rmse:0.13583 +[52] validation_0-rmse:0.15334 validation_1-rmse:0.13483 +[53] validation_0-rmse:0.15256 validation_1-rmse:0.13387 +[54] validation_0-rmse:0.15190 validation_1-rmse:0.13290 +[55] validation_0-rmse:0.15122 validation_1-rmse:0.13174 +[56] validation_0-rmse:0.15065 validation_1-rmse:0.13080 +[57] validation_0-rmse:0.15006 validation_1-rmse:0.12993 +[58] validation_0-rmse:0.14955 validation_1-rmse:0.12897 +[59] validation_0-rmse:0.14893 validation_1-rmse:0.12814 +[60] validation_0-rmse:0.14843 validation_1-rmse:0.12735 +[61] validation_0-rmse:0.14789 validation_1-rmse:0.12642 +[62] validation_0-rmse:0.14718 validation_1-rmse:0.12561 +[63] validation_0-rmse:0.14659 validation_1-rmse:0.12486 +[64] validation_0-rmse:0.14600 validation_1-rmse:0.12397 +[65] validation_0-rmse:0.14547 validation_1-rmse:0.12324 +[66] validation_0-rmse:0.14499 validation_1-rmse:0.12255 +[67] validation_0-rmse:0.14451 validation_1-rmse:0.12188 +[68] validation_0-rmse:0.14393 validation_1-rmse:0.12114 +[69] validation_0-rmse:0.14346 validation_1-rmse:0.12048 +[70] validation_0-rmse:0.14293 validation_1-rmse:0.11974 +[71] validation_0-rmse:0.14256 validation_1-rmse:0.11893 +[72] validation_0-rmse:0.14212 validation_1-rmse:0.11830 +[73] validation_0-rmse:0.14177 validation_1-rmse:0.11748 +[74] validation_0-rmse:0.14134 validation_1-rmse:0.11686 +[75] validation_0-rmse:0.14101 validation_1-rmse:0.11609 +[76] validation_0-rmse:0.14060 validation_1-rmse:0.11536 +[77] validation_0-rmse:0.14020 validation_1-rmse:0.11484 +[78] validation_0-rmse:0.13983 validation_1-rmse:0.11412 +[79] validation_0-rmse:0.13951 validation_1-rmse:0.11357 +[80] validation_0-rmse:0.13928 validation_1-rmse:0.11273 +[81] validation_0-rmse:0.13889 validation_1-rmse:0.11221 +[82] validation_0-rmse:0.13855 validation_1-rmse:0.11166 +[83] validation_0-rmse:0.13824 validation_1-rmse:0.11114 +[84] validation_0-rmse:0.13808 validation_1-rmse:0.11050 +[85] validation_0-rmse:0.13767 validation_1-rmse:0.10998 +[86] validation_0-rmse:0.13731 validation_1-rmse:0.10947 +[87] validation_0-rmse:0.13716 validation_1-rmse:0.10876 +[88] validation_0-rmse:0.13678 validation_1-rmse:0.10832 +[89] validation_0-rmse:0.13659 validation_1-rmse:0.10782 +[90] validation_0-rmse:0.13629 validation_1-rmse:0.10736 +[91] validation_0-rmse:0.13600 validation_1-rmse:0.10662 +[92] validation_0-rmse:0.13577 validation_1-rmse:0.10613 +[93] validation_0-rmse:0.13541 validation_1-rmse:0.10565 +[94] validation_0-rmse:0.13534 validation_1-rmse:0.10501 +[95] validation_0-rmse:0.13511 validation_1-rmse:0.10453 +[96] validation_0-rmse:0.13483 validation_1-rmse:0.10401 +[97] validation_0-rmse:0.13455 validation_1-rmse:0.10362 +[98] validation_0-rmse:0.13425 validation_1-rmse:0.10323 +[99] validation_0-rmse:0.13402 validation_1-rmse:0.10289 +2025-04-29 01:55:27,556 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.02 secs) -------------------- +2025-04-29 01:55:27,557 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:28,076 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:55:28,077 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1739145600_prediction.feather +2025-04-29 01:55:28,081 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,088 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:55:28,089 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.168798 +1 0.003285 50.168798 +2 0.001898 50.168798 +3 0.000484 50.168798 +4 0.001688 50.168798 +2025-04-29 01:55:28,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:28,100 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:28,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:28,106 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1739145600/cb_btc_1739145600 +2025-04-29 01:55:28,107 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:28,123 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:28,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:55:33,123 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:33,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26428 validation_1-rmse:0.27464 +[1] validation_0-rmse:0.25911 validation_1-rmse:0.26865 +[2] validation_0-rmse:0.25427 validation_1-rmse:0.26296 +[3] validation_0-rmse:0.24970 validation_1-rmse:0.25748 +[4] validation_0-rmse:0.24525 validation_1-rmse:0.25222 +[5] validation_0-rmse:0.24140 validation_1-rmse:0.24725 +[6] validation_0-rmse:0.23748 validation_1-rmse:0.24264 +[7] validation_0-rmse:0.23368 validation_1-rmse:0.23792 +[8] validation_0-rmse:0.23022 validation_1-rmse:0.23363 +[9] validation_0-rmse:0.22695 validation_1-rmse:0.22945 +[10] validation_0-rmse:0.22381 validation_1-rmse:0.22543 +[11] validation_0-rmse:0.22105 validation_1-rmse:0.22154 +[12] validation_0-rmse:0.21818 validation_1-rmse:0.21797 +[13] validation_0-rmse:0.21526 validation_1-rmse:0.21430 +[14] validation_0-rmse:0.21284 validation_1-rmse:0.21101 +[15] validation_0-rmse:0.21034 validation_1-rmse:0.20769 +[16] validation_0-rmse:0.20802 validation_1-rmse:0.20438 +[17] validation_0-rmse:0.20590 validation_1-rmse:0.20136 +[18] validation_0-rmse:0.20386 validation_1-rmse:0.19837 +[19] validation_0-rmse:0.20219 validation_1-rmse:0.19549 +[20] validation_0-rmse:0.20037 validation_1-rmse:0.19283 +[21] validation_0-rmse:0.19826 validation_1-rmse:0.19005 +[22] validation_0-rmse:0.19657 validation_1-rmse:0.18750 +[23] validation_0-rmse:0.19525 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.19373 validation_1-rmse:0.18267 +[25] validation_0-rmse:0.19197 validation_1-rmse:0.18037 +[26] validation_0-rmse:0.19063 validation_1-rmse:0.17799 +[27] validation_0-rmse:0.18897 validation_1-rmse:0.17587 +[28] validation_0-rmse:0.18765 validation_1-rmse:0.17382 +[29] validation_0-rmse:0.18608 validation_1-rmse:0.17185 +[30] validation_0-rmse:0.18456 validation_1-rmse:0.16992 +[31] validation_0-rmse:0.18340 validation_1-rmse:0.16793 +[32] validation_0-rmse:0.18206 validation_1-rmse:0.16616 +[33] validation_0-rmse:0.18077 validation_1-rmse:0.16437 +[34] validation_0-rmse:0.17960 validation_1-rmse:0.16270 +[35] validation_0-rmse:0.17857 validation_1-rmse:0.16105 +[36] validation_0-rmse:0.17748 validation_1-rmse:0.15925 +[37] validation_0-rmse:0.17649 validation_1-rmse:0.15762 +[38] validation_0-rmse:0.17540 validation_1-rmse:0.15611 +[39] validation_0-rmse:0.17427 validation_1-rmse:0.15469 +[40] validation_0-rmse:0.17312 validation_1-rmse:0.15301 +[41] validation_0-rmse:0.17217 validation_1-rmse:0.15169 +[42] validation_0-rmse:0.17119 validation_1-rmse:0.15037 +[43] validation_0-rmse:0.17030 validation_1-rmse:0.14910 +[44] validation_0-rmse:0.16939 validation_1-rmse:0.14786 +[45] validation_0-rmse:0.16851 validation_1-rmse:0.14660 +[46] validation_0-rmse:0.16793 validation_1-rmse:0.14518 +[47] validation_0-rmse:0.16760 validation_1-rmse:0.14365 +[48] validation_0-rmse:0.16674 validation_1-rmse:0.14258 +[49] validation_0-rmse:0.16588 validation_1-rmse:0.14152 +[50] validation_0-rmse:0.16505 validation_1-rmse:0.14051 +[51] validation_0-rmse:0.16437 validation_1-rmse:0.13919 +[52] validation_0-rmse:0.16361 validation_1-rmse:0.13818 +[53] validation_0-rmse:0.16290 validation_1-rmse:0.13715 +[54] validation_0-rmse:0.16217 validation_1-rmse:0.13621 +[55] validation_0-rmse:0.16207 validation_1-rmse:0.13493 +[56] validation_0-rmse:0.16153 validation_1-rmse:0.13395 +[57] validation_0-rmse:0.16077 validation_1-rmse:0.13302 +[58] validation_0-rmse:0.16021 validation_1-rmse:0.13218 +[59] validation_0-rmse:0.15972 validation_1-rmse:0.13117 +[60] validation_0-rmse:0.15954 validation_1-rmse:0.13003 +[61] validation_0-rmse:0.15896 validation_1-rmse:0.12926 +[62] validation_0-rmse:0.15849 validation_1-rmse:0.12848 +[63] validation_0-rmse:0.15801 validation_1-rmse:0.12770 +[64] validation_0-rmse:0.15737 validation_1-rmse:0.12678 +[65] validation_0-rmse:0.15736 validation_1-rmse:0.12578 +[66] validation_0-rmse:0.15684 validation_1-rmse:0.12506 +[67] validation_0-rmse:0.15638 validation_1-rmse:0.12437 +[68] validation_0-rmse:0.15618 validation_1-rmse:0.12336 +[69] validation_0-rmse:0.15581 validation_1-rmse:0.12269 +[70] validation_0-rmse:0.15537 validation_1-rmse:0.12205 +[71] validation_0-rmse:0.15534 validation_1-rmse:0.12117 +[72] validation_0-rmse:0.15485 validation_1-rmse:0.12049 +[73] validation_0-rmse:0.15465 validation_1-rmse:0.11968 +[74] validation_0-rmse:0.15430 validation_1-rmse:0.11906 +[75] validation_0-rmse:0.15386 validation_1-rmse:0.11840 +[76] validation_0-rmse:0.15353 validation_1-rmse:0.11781 +[77] validation_0-rmse:0.15354 validation_1-rmse:0.11697 +[78] validation_0-rmse:0.15325 validation_1-rmse:0.11630 +[79] validation_0-rmse:0.15282 validation_1-rmse:0.11572 +[80] validation_0-rmse:0.15239 validation_1-rmse:0.11514 +[81] validation_0-rmse:0.15226 validation_1-rmse:0.11431 +[82] validation_0-rmse:0.15189 validation_1-rmse:0.11381 +[83] validation_0-rmse:0.15171 validation_1-rmse:0.11316 +[84] validation_0-rmse:0.15136 validation_1-rmse:0.11270 +[85] validation_0-rmse:0.15112 validation_1-rmse:0.11212 +[86] validation_0-rmse:0.15112 validation_1-rmse:0.11140 +[87] validation_0-rmse:0.15074 validation_1-rmse:0.11094 +[88] validation_0-rmse:0.15048 validation_1-rmse:0.11035 +[89] validation_0-rmse:0.15026 validation_1-rmse:0.10983 +[90] validation_0-rmse:0.14989 validation_1-rmse:0.10938 +[91] validation_0-rmse:0.14955 validation_1-rmse:0.10893 +[92] validation_0-rmse:0.14955 validation_1-rmse:0.10815 +[93] validation_0-rmse:0.14933 validation_1-rmse:0.10765 +[94] validation_0-rmse:0.14908 validation_1-rmse:0.10711 +[95] validation_0-rmse:0.14889 validation_1-rmse:0.10668 +[96] validation_0-rmse:0.14853 validation_1-rmse:0.10627 +[97] validation_0-rmse:0.14853 validation_1-rmse:0.10553 +[98] validation_0-rmse:0.14835 validation_1-rmse:0.10513 +[99] validation_0-rmse:0.14818 validation_1-rmse:0.10475 +2025-04-29 01:55:33,929 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.82 secs) -------------------- +2025-04-29 01:55:33,930 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:34,433 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:55:34,434 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740009600_prediction.feather +2025-04-29 01:55:34,440 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,447 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:55:34,448 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.167897 +1 0.003285 50.167897 +2 0.001898 50.167897 +3 0.000484 50.167897 +4 0.001688 50.167897 +2025-04-29 01:55:34,453 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:34,459 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:34,461 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:34,465 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740009600/cb_btc_1740009600 +2025-04-29 01:55:34,466 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:34,482 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:34,483 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:55:39,369 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:39,370 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27166 validation_1-rmse:0.27726 +[1] validation_0-rmse:0.26708 validation_1-rmse:0.27112 +[2] validation_0-rmse:0.26297 validation_1-rmse:0.26523 +[3] validation_0-rmse:0.25865 validation_1-rmse:0.25959 +[4] validation_0-rmse:0.25494 validation_1-rmse:0.25419 +[5] validation_0-rmse:0.25100 validation_1-rmse:0.24913 +[6] validation_0-rmse:0.24763 validation_1-rmse:0.24437 +[7] validation_0-rmse:0.24441 validation_1-rmse:0.23970 +[8] validation_0-rmse:0.24110 validation_1-rmse:0.23527 +[9] validation_0-rmse:0.23801 validation_1-rmse:0.23102 +[10] validation_0-rmse:0.23492 validation_1-rmse:0.22691 +[11] validation_0-rmse:0.23229 validation_1-rmse:0.22297 +[12] validation_0-rmse:0.22956 validation_1-rmse:0.21923 +[13] validation_0-rmse:0.22707 validation_1-rmse:0.21564 +[14] validation_0-rmse:0.22482 validation_1-rmse:0.21221 +[15] validation_0-rmse:0.22237 validation_1-rmse:0.20891 +[16] validation_0-rmse:0.22030 validation_1-rmse:0.20557 +[17] validation_0-rmse:0.21784 validation_1-rmse:0.20243 +[18] validation_0-rmse:0.21591 validation_1-rmse:0.19949 +[19] validation_0-rmse:0.21399 validation_1-rmse:0.19664 +[20] validation_0-rmse:0.21182 validation_1-rmse:0.19378 +[21] validation_0-rmse:0.20992 validation_1-rmse:0.19110 +[22] validation_0-rmse:0.20821 validation_1-rmse:0.18850 +[23] validation_0-rmse:0.20621 validation_1-rmse:0.18597 +[24] validation_0-rmse:0.20490 validation_1-rmse:0.18353 +[25] validation_0-rmse:0.20318 validation_1-rmse:0.18126 +[26] validation_0-rmse:0.20168 validation_1-rmse:0.17896 +[27] validation_0-rmse:0.19992 validation_1-rmse:0.17679 +[28] validation_0-rmse:0.19865 validation_1-rmse:0.17458 +[29] validation_0-rmse:0.19722 validation_1-rmse:0.17257 +[30] validation_0-rmse:0.19571 validation_1-rmse:0.17039 +[31] validation_0-rmse:0.19429 validation_1-rmse:0.16855 +[32] validation_0-rmse:0.19285 validation_1-rmse:0.16664 +[33] validation_0-rmse:0.19141 validation_1-rmse:0.16488 +[34] validation_0-rmse:0.19022 validation_1-rmse:0.16312 +[35] validation_0-rmse:0.18904 validation_1-rmse:0.16145 +[36] validation_0-rmse:0.18832 validation_1-rmse:0.15973 +[37] validation_0-rmse:0.18723 validation_1-rmse:0.15815 +[38] validation_0-rmse:0.18610 validation_1-rmse:0.15653 +[39] validation_0-rmse:0.18504 validation_1-rmse:0.15503 +[40] validation_0-rmse:0.18402 validation_1-rmse:0.15358 +[41] validation_0-rmse:0.18333 validation_1-rmse:0.15193 +[42] validation_0-rmse:0.18213 validation_1-rmse:0.15058 +[43] validation_0-rmse:0.18176 validation_1-rmse:0.14922 +[44] validation_0-rmse:0.18093 validation_1-rmse:0.14792 +[45] validation_0-rmse:0.18017 validation_1-rmse:0.14667 +[46] validation_0-rmse:0.17928 validation_1-rmse:0.14537 +[47] validation_0-rmse:0.17858 validation_1-rmse:0.14420 +[48] validation_0-rmse:0.17770 validation_1-rmse:0.14306 +[49] validation_0-rmse:0.17695 validation_1-rmse:0.14199 +[50] validation_0-rmse:0.17613 validation_1-rmse:0.14094 +[51] validation_0-rmse:0.17545 validation_1-rmse:0.13979 +[52] validation_0-rmse:0.17490 validation_1-rmse:0.13874 +[53] validation_0-rmse:0.17452 validation_1-rmse:0.13755 +[54] validation_0-rmse:0.17383 validation_1-rmse:0.13663 +[55] validation_0-rmse:0.17327 validation_1-rmse:0.13568 +[56] validation_0-rmse:0.17255 validation_1-rmse:0.13477 +[57] validation_0-rmse:0.17192 validation_1-rmse:0.13382 +[58] validation_0-rmse:0.17138 validation_1-rmse:0.13277 +[59] validation_0-rmse:0.17074 validation_1-rmse:0.13188 +[60] validation_0-rmse:0.17026 validation_1-rmse:0.13089 +[61] validation_0-rmse:0.16969 validation_1-rmse:0.13010 +[62] validation_0-rmse:0.16932 validation_1-rmse:0.12904 +[63] validation_0-rmse:0.16888 validation_1-rmse:0.12818 +[64] validation_0-rmse:0.16849 validation_1-rmse:0.12745 +[65] validation_0-rmse:0.16802 validation_1-rmse:0.12639 +[66] validation_0-rmse:0.16747 validation_1-rmse:0.12567 +[67] validation_0-rmse:0.16710 validation_1-rmse:0.12496 +[68] validation_0-rmse:0.16672 validation_1-rmse:0.12426 +[69] validation_0-rmse:0.16635 validation_1-rmse:0.12331 +[70] validation_0-rmse:0.16597 validation_1-rmse:0.12267 +[71] validation_0-rmse:0.16554 validation_1-rmse:0.12196 +[72] validation_0-rmse:0.16522 validation_1-rmse:0.12121 +[73] validation_0-rmse:0.16481 validation_1-rmse:0.12054 +[74] validation_0-rmse:0.16442 validation_1-rmse:0.11996 +[75] validation_0-rmse:0.16409 validation_1-rmse:0.11939 +[76] validation_0-rmse:0.16375 validation_1-rmse:0.11878 +[77] validation_0-rmse:0.16275 validation_1-rmse:0.11753 +[78] validation_0-rmse:0.16248 validation_1-rmse:0.11692 +[79] validation_0-rmse:0.16215 validation_1-rmse:0.11619 +[80] validation_0-rmse:0.16187 validation_1-rmse:0.11564 +[81] validation_0-rmse:0.16150 validation_1-rmse:0.11493 +[82] validation_0-rmse:0.16123 validation_1-rmse:0.11438 +[83] validation_0-rmse:0.16109 validation_1-rmse:0.11358 +[84] validation_0-rmse:0.16065 validation_1-rmse:0.11304 +[85] validation_0-rmse:0.16038 validation_1-rmse:0.11256 +[86] validation_0-rmse:0.16022 validation_1-rmse:0.11205 +[87] validation_0-rmse:0.16007 validation_1-rmse:0.11158 +[88] validation_0-rmse:0.15945 validation_1-rmse:0.11054 +[89] validation_0-rmse:0.15912 validation_1-rmse:0.11008 +[90] validation_0-rmse:0.15894 validation_1-rmse:0.10937 +[91] validation_0-rmse:0.15868 validation_1-rmse:0.10886 +[92] validation_0-rmse:0.15845 validation_1-rmse:0.10844 +[93] validation_0-rmse:0.15817 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.15789 validation_1-rmse:0.10758 +[95] validation_0-rmse:0.15772 validation_1-rmse:0.10721 +[96] validation_0-rmse:0.15763 validation_1-rmse:0.10676 +[97] validation_0-rmse:0.15751 validation_1-rmse:0.10609 +[98] validation_0-rmse:0.15731 validation_1-rmse:0.10574 +[99] validation_0-rmse:0.15738 validation_1-rmse:0.10531 +2025-04-29 01:55:40,266 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.80 secs) -------------------- +2025-04-29 01:55:40,267 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:40,801 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:55:40,802 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740873600_prediction.feather +2025-04-29 01:55:40,807 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,814 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:55:40,816 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.107698 +1 0.003285 50.107698 +2 0.001898 50.107698 +3 0.000484 50.107698 +4 0.001688 50.107698 +2025-04-29 01:55:40,821 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:40,827 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:40,829 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:40,833 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740873600/cb_btc_1740873600 +2025-04-29 01:55:40,834 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:40,849 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:40,850 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:55:45,643 - datasieve.pipeline - INFO - DI tossed 2275 predictions for being too far from training data. +2025-04-29 01:55:45,646 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:45,647 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.27618 validation_1-rmse:0.28955 +[1] validation_0-rmse:0.27005 validation_1-rmse:0.28323 +[2] validation_0-rmse:0.26414 validation_1-rmse:0.27722 +[3] validation_0-rmse:0.25897 validation_1-rmse:0.27161 +[4] validation_0-rmse:0.25425 validation_1-rmse:0.26622 +[5] validation_0-rmse:0.24886 validation_1-rmse:0.26100 +[6] validation_0-rmse:0.24522 validation_1-rmse:0.25606 +[7] validation_0-rmse:0.24137 validation_1-rmse:0.25132 +[8] validation_0-rmse:0.23765 validation_1-rmse:0.24687 +[9] validation_0-rmse:0.23323 validation_1-rmse:0.24254 +[10] validation_0-rmse:0.22900 validation_1-rmse:0.23827 +[11] validation_0-rmse:0.22588 validation_1-rmse:0.23450 +[12] validation_0-rmse:0.22228 validation_1-rmse:0.23055 +[13] validation_0-rmse:0.21872 validation_1-rmse:0.22698 +[14] validation_0-rmse:0.21492 validation_1-rmse:0.22348 +[15] validation_0-rmse:0.21329 validation_1-rmse:0.22011 +[16] validation_0-rmse:0.21024 validation_1-rmse:0.21686 +[17] validation_0-rmse:0.20823 validation_1-rmse:0.21380 +[18] validation_0-rmse:0.20544 validation_1-rmse:0.21075 +[19] validation_0-rmse:0.20415 validation_1-rmse:0.20787 +[20] validation_0-rmse:0.20143 validation_1-rmse:0.20515 +[21] validation_0-rmse:0.19917 validation_1-rmse:0.20247 +[22] validation_0-rmse:0.19745 validation_1-rmse:0.19994 +[23] validation_0-rmse:0.19508 validation_1-rmse:0.19746 +[24] validation_0-rmse:0.19300 validation_1-rmse:0.19490 +[25] validation_0-rmse:0.19085 validation_1-rmse:0.19254 +[26] validation_0-rmse:0.18898 validation_1-rmse:0.19031 +[27] validation_0-rmse:0.18720 validation_1-rmse:0.18794 +[28] validation_0-rmse:0.18503 validation_1-rmse:0.18584 +[29] validation_0-rmse:0.18314 validation_1-rmse:0.18382 +[30] validation_0-rmse:0.18132 validation_1-rmse:0.18164 +[31] validation_0-rmse:0.17984 validation_1-rmse:0.17967 +[32] validation_0-rmse:0.17818 validation_1-rmse:0.17779 +[33] validation_0-rmse:0.17637 validation_1-rmse:0.17572 +[34] validation_0-rmse:0.17473 validation_1-rmse:0.17399 +[35] validation_0-rmse:0.17338 validation_1-rmse:0.17229 +[36] validation_0-rmse:0.17253 validation_1-rmse:0.17055 +[37] validation_0-rmse:0.17149 validation_1-rmse:0.16883 +[38] validation_0-rmse:0.17030 validation_1-rmse:0.16730 +[39] validation_0-rmse:0.16950 validation_1-rmse:0.16556 +[40] validation_0-rmse:0.16815 validation_1-rmse:0.16412 +[41] validation_0-rmse:0.16704 validation_1-rmse:0.16268 +[42] validation_0-rmse:0.16617 validation_1-rmse:0.16128 +[43] validation_0-rmse:0.16542 validation_1-rmse:0.15970 +[44] validation_0-rmse:0.16438 validation_1-rmse:0.15840 +[45] validation_0-rmse:0.16356 validation_1-rmse:0.15692 +[46] validation_0-rmse:0.16239 validation_1-rmse:0.15574 +[47] validation_0-rmse:0.16153 validation_1-rmse:0.15456 +[48] validation_0-rmse:0.16076 validation_1-rmse:0.15314 +[49] validation_0-rmse:0.15998 validation_1-rmse:0.15201 +[50] validation_0-rmse:0.15946 validation_1-rmse:0.15084 +[51] validation_0-rmse:0.15891 validation_1-rmse:0.14954 +[52] validation_0-rmse:0.15834 validation_1-rmse:0.14847 +[53] validation_0-rmse:0.15764 validation_1-rmse:0.14722 +[54] validation_0-rmse:0.15707 validation_1-rmse:0.14623 +[55] validation_0-rmse:0.15653 validation_1-rmse:0.14527 +[56] validation_0-rmse:0.15583 validation_1-rmse:0.14434 +[57] validation_0-rmse:0.15549 validation_1-rmse:0.14329 +[58] validation_0-rmse:0.15507 validation_1-rmse:0.14241 +[59] validation_0-rmse:0.15468 validation_1-rmse:0.14053 +[60] validation_0-rmse:0.15398 validation_1-rmse:0.13968 +[61] validation_0-rmse:0.15390 validation_1-rmse:0.13864 +[62] validation_0-rmse:0.15360 validation_1-rmse:0.13783 +[63] validation_0-rmse:0.15368 validation_1-rmse:0.13704 +[64] validation_0-rmse:0.15338 validation_1-rmse:0.13624 +[65] validation_0-rmse:0.15273 validation_1-rmse:0.13551 +[66] validation_0-rmse:0.15238 validation_1-rmse:0.13451 +[67] validation_0-rmse:0.15212 validation_1-rmse:0.13290 +[68] validation_0-rmse:0.15191 validation_1-rmse:0.13217 +[69] validation_0-rmse:0.15138 validation_1-rmse:0.13143 +[70] validation_0-rmse:0.15090 validation_1-rmse:0.13071 +[71] validation_0-rmse:0.15082 validation_1-rmse:0.13001 +[72] validation_0-rmse:0.14988 validation_1-rmse:0.12847 +[73] validation_0-rmse:0.14953 validation_1-rmse:0.12783 +[74] validation_0-rmse:0.14924 validation_1-rmse:0.12709 +[75] validation_0-rmse:0.14926 validation_1-rmse:0.12578 +[76] validation_0-rmse:0.14903 validation_1-rmse:0.12499 +[77] validation_0-rmse:0.14851 validation_1-rmse:0.12435 +[78] validation_0-rmse:0.14808 validation_1-rmse:0.12368 +[79] validation_0-rmse:0.14768 validation_1-rmse:0.12305 +[80] validation_0-rmse:0.14741 validation_1-rmse:0.12217 +[81] validation_0-rmse:0.14712 validation_1-rmse:0.12165 +[82] validation_0-rmse:0.14696 validation_1-rmse:0.12110 +[83] validation_0-rmse:0.14686 validation_1-rmse:0.12045 +[84] validation_0-rmse:0.14648 validation_1-rmse:0.11984 +[85] validation_0-rmse:0.14623 validation_1-rmse:0.11923 +[86] validation_0-rmse:0.14606 validation_1-rmse:0.11869 +[87] validation_0-rmse:0.14583 validation_1-rmse:0.11754 +[88] validation_0-rmse:0.14572 validation_1-rmse:0.11710 +[89] validation_0-rmse:0.14537 validation_1-rmse:0.11660 +[90] validation_0-rmse:0.14510 validation_1-rmse:0.11614 +[91] validation_0-rmse:0.14516 validation_1-rmse:0.11514 +[92] validation_0-rmse:0.14480 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.14475 validation_1-rmse:0.11414 +[94] validation_0-rmse:0.14443 validation_1-rmse:0.11374 +[95] validation_0-rmse:0.14409 validation_1-rmse:0.11331 +[96] validation_0-rmse:0.14391 validation_1-rmse:0.11240 +[97] validation_0-rmse:0.14303 validation_1-rmse:0.11154 +[98] validation_0-rmse:0.14274 validation_1-rmse:0.11114 +[99] validation_0-rmse:0.14246 validation_1-rmse:0.11071 +2025-04-29 01:55:46,544 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.71 secs) -------------------- +2025-04-29 01:55:46,544 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:47,092 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:55:47,092 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1741737600_prediction.feather +2025-04-29 01:55:47,100 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,107 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:55:47,109 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079166 +1 0.003285 50.079166 +2 0.001898 50.079166 +3 0.000484 50.079166 +4 0.001688 50.079166 +2025-04-29 01:55:47,115 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:47,122 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:47,123 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:47,128 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1741737600/cb_btc_1741737600 +2025-04-29 01:55:47,129 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:47,145 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:47,145 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:55:51,987 - datasieve.pipeline - INFO - DI tossed 18 predictions for being too far from training data. +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26738 validation_1-rmse:0.26816 +[1] validation_0-rmse:0.26268 validation_1-rmse:0.26258 +[2] validation_0-rmse:0.25808 validation_1-rmse:0.25725 +[3] validation_0-rmse:0.25395 validation_1-rmse:0.25212 +[4] validation_0-rmse:0.24987 validation_1-rmse:0.24723 +[5] validation_0-rmse:0.24633 validation_1-rmse:0.24263 +[6] validation_0-rmse:0.24308 validation_1-rmse:0.23814 +[7] validation_0-rmse:0.23959 validation_1-rmse:0.23402 +[8] validation_0-rmse:0.23612 validation_1-rmse:0.22977 +[9] validation_0-rmse:0.23322 validation_1-rmse:0.22577 +[10] validation_0-rmse:0.23012 validation_1-rmse:0.22207 +[11] validation_0-rmse:0.22730 validation_1-rmse:0.21843 +[12] validation_0-rmse:0.22453 validation_1-rmse:0.21489 +[13] validation_0-rmse:0.22236 validation_1-rmse:0.21145 +[14] validation_0-rmse:0.22000 validation_1-rmse:0.20841 +[15] validation_0-rmse:0.21744 validation_1-rmse:0.20529 +[16] validation_0-rmse:0.21556 validation_1-rmse:0.20225 +[17] validation_0-rmse:0.21331 validation_1-rmse:0.19932 +[18] validation_0-rmse:0.21171 validation_1-rmse:0.19643 +[19] validation_0-rmse:0.21051 validation_1-rmse:0.19382 +[20] validation_0-rmse:0.20880 validation_1-rmse:0.19128 +[21] validation_0-rmse:0.20711 validation_1-rmse:0.18854 +[22] validation_0-rmse:0.20538 validation_1-rmse:0.18612 +[23] validation_0-rmse:0.20350 validation_1-rmse:0.18381 +[24] validation_0-rmse:0.20234 validation_1-rmse:0.18144 +[25] validation_0-rmse:0.20081 validation_1-rmse:0.17917 +[26] validation_0-rmse:0.19918 validation_1-rmse:0.17714 +[27] validation_0-rmse:0.19804 validation_1-rmse:0.17496 +[28] validation_0-rmse:0.19662 validation_1-rmse:0.17304 +[29] validation_0-rmse:0.19580 validation_1-rmse:0.17082 +[30] validation_0-rmse:0.19454 validation_1-rmse:0.16901 +[31] validation_0-rmse:0.19331 validation_1-rmse:0.16691 +[32] validation_0-rmse:0.19234 validation_1-rmse:0.16517 +[33] validation_0-rmse:0.19118 validation_1-rmse:0.16354 +[34] validation_0-rmse:0.19024 validation_1-rmse:0.16175 +[35] validation_0-rmse:0.18915 validation_1-rmse:0.16020 +[36] validation_0-rmse:0.18823 validation_1-rmse:0.15865 +[37] validation_0-rmse:0.18756 validation_1-rmse:0.15712 +[38] validation_0-rmse:0.18698 validation_1-rmse:0.15541 +[39] validation_0-rmse:0.18643 validation_1-rmse:0.15395 +[40] validation_0-rmse:0.18562 validation_1-rmse:0.15265 +[41] validation_0-rmse:0.18516 validation_1-rmse:0.15124 +[42] validation_0-rmse:0.18421 validation_1-rmse:0.14979 +[43] validation_0-rmse:0.18360 validation_1-rmse:0.14850 +[44] validation_0-rmse:0.18275 validation_1-rmse:0.14733 +[45] validation_0-rmse:0.18253 validation_1-rmse:0.14597 +[46] validation_0-rmse:0.18183 validation_1-rmse:0.14470 +[47] validation_0-rmse:0.18111 validation_1-rmse:0.14361 +[48] validation_0-rmse:0.18060 validation_1-rmse:0.14243 +[49] validation_0-rmse:0.18001 validation_1-rmse:0.14134 +[50] validation_0-rmse:0.17953 validation_1-rmse:0.14030 +[51] validation_0-rmse:0.17899 validation_1-rmse:0.13927 +[52] validation_0-rmse:0.17830 validation_1-rmse:0.13817 +[53] validation_0-rmse:0.17770 validation_1-rmse:0.13720 +[54] validation_0-rmse:0.17702 validation_1-rmse:0.13629 +[55] validation_0-rmse:0.17650 validation_1-rmse:0.13531 +[56] validation_0-rmse:0.17625 validation_1-rmse:0.13440 +[57] validation_0-rmse:0.17580 validation_1-rmse:0.13352 +[58] validation_0-rmse:0.17530 validation_1-rmse:0.13268 +[59] validation_0-rmse:0.17486 validation_1-rmse:0.13166 +[60] validation_0-rmse:0.17438 validation_1-rmse:0.13071 +[61] validation_0-rmse:0.17387 validation_1-rmse:0.12991 +[62] validation_0-rmse:0.17356 validation_1-rmse:0.12914 +[63] validation_0-rmse:0.17311 validation_1-rmse:0.12839 +[64] validation_0-rmse:0.17265 validation_1-rmse:0.12767 +[65] validation_0-rmse:0.17209 validation_1-rmse:0.12682 +[66] validation_0-rmse:0.17197 validation_1-rmse:0.12595 +[67] validation_0-rmse:0.17157 validation_1-rmse:0.12506 +[68] validation_0-rmse:0.17131 validation_1-rmse:0.12439 +[69] validation_0-rmse:0.17088 validation_1-rmse:0.12371 +[70] validation_0-rmse:0.17038 validation_1-rmse:0.12298 +[71] validation_0-rmse:0.17009 validation_1-rmse:0.12235 +[72] validation_0-rmse:0.16979 validation_1-rmse:0.12172 +[73] validation_0-rmse:0.16934 validation_1-rmse:0.12118 +[74] validation_0-rmse:0.16902 validation_1-rmse:0.12050 +[75] validation_0-rmse:0.16881 validation_1-rmse:0.11988 +[76] validation_0-rmse:0.16846 validation_1-rmse:0.11928 +[77] validation_0-rmse:0.16809 validation_1-rmse:0.11846 +[78] validation_0-rmse:0.16774 validation_1-rmse:0.11791 +[79] validation_0-rmse:0.16745 validation_1-rmse:0.11738 +[80] validation_0-rmse:0.16717 validation_1-rmse:0.11683 +[81] validation_0-rmse:0.16702 validation_1-rmse:0.11599 +[82] validation_0-rmse:0.16677 validation_1-rmse:0.11535 +[83] validation_0-rmse:0.16649 validation_1-rmse:0.11468 +[84] validation_0-rmse:0.16605 validation_1-rmse:0.11415 +[85] validation_0-rmse:0.16591 validation_1-rmse:0.11350 +[86] validation_0-rmse:0.16560 validation_1-rmse:0.11303 +[87] validation_0-rmse:0.16531 validation_1-rmse:0.11259 +[88] validation_0-rmse:0.16504 validation_1-rmse:0.11185 +[89] validation_0-rmse:0.16485 validation_1-rmse:0.11134 +[90] validation_0-rmse:0.16463 validation_1-rmse:0.11083 +[91] validation_0-rmse:0.16436 validation_1-rmse:0.11041 +[92] validation_0-rmse:0.16412 validation_1-rmse:0.10988 +[93] validation_0-rmse:0.16388 validation_1-rmse:0.10942 +[94] validation_0-rmse:0.16391 validation_1-rmse:0.10881 +[95] validation_0-rmse:0.16357 validation_1-rmse:0.10838 +[96] validation_0-rmse:0.16358 validation_1-rmse:0.10796 +[97] validation_0-rmse:0.16338 validation_1-rmse:0.10756 +[98] validation_0-rmse:0.16339 validation_1-rmse:0.10688 +[99] validation_0-rmse:0.16321 validation_1-rmse:0.10649 +2025-04-29 01:55:52,741 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.61 secs) -------------------- +2025-04-29 01:55:52,742 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:53,285 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:55:53,286 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1742601600_prediction.feather +2025-04-29 01:55:53,291 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,298 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:55:53,300 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.102027 +1 0.003285 50.102027 +2 0.001898 50.102027 +3 0.000484 50.102027 +4 0.001688 50.102027 +2025-04-29 01:55:53,309 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:53,316 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:53,318 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:53,322 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1742601600/cb_btc_1742601600 +2025-04-29 01:55:53,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:53,339 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:53,340 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:55:58,184 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:55:58,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.26992 validation_1-rmse:0.26756 +[1] validation_0-rmse:0.26551 validation_1-rmse:0.26201 +[2] validation_0-rmse:0.26111 validation_1-rmse:0.25656 +[3] validation_0-rmse:0.25690 validation_1-rmse:0.25154 +[4] validation_0-rmse:0.25291 validation_1-rmse:0.24683 +[5] validation_0-rmse:0.24933 validation_1-rmse:0.24228 +[6] validation_0-rmse:0.24598 validation_1-rmse:0.23796 +[7] validation_0-rmse:0.24252 validation_1-rmse:0.23392 +[8] validation_0-rmse:0.23953 validation_1-rmse:0.22978 +[9] validation_0-rmse:0.23634 validation_1-rmse:0.22592 +[10] validation_0-rmse:0.23330 validation_1-rmse:0.22229 +[11] validation_0-rmse:0.23059 validation_1-rmse:0.21875 +[12] validation_0-rmse:0.22799 validation_1-rmse:0.21546 +[13] validation_0-rmse:0.22565 validation_1-rmse:0.21212 +[14] validation_0-rmse:0.22329 validation_1-rmse:0.20904 +[15] validation_0-rmse:0.22111 validation_1-rmse:0.20604 +[16] validation_0-rmse:0.21894 validation_1-rmse:0.20318 +[17] validation_0-rmse:0.21715 validation_1-rmse:0.20021 +[18] validation_0-rmse:0.21499 validation_1-rmse:0.19735 +[19] validation_0-rmse:0.21283 validation_1-rmse:0.19480 +[20] validation_0-rmse:0.21109 validation_1-rmse:0.19209 +[21] validation_0-rmse:0.20904 validation_1-rmse:0.18969 +[22] validation_0-rmse:0.20762 validation_1-rmse:0.18718 +[23] validation_0-rmse:0.20580 validation_1-rmse:0.18498 +[24] validation_0-rmse:0.20434 validation_1-rmse:0.18262 +[25] validation_0-rmse:0.20267 validation_1-rmse:0.18048 +[26] validation_0-rmse:0.20106 validation_1-rmse:0.17844 +[27] validation_0-rmse:0.19945 validation_1-rmse:0.17647 +[28] validation_0-rmse:0.19813 validation_1-rmse:0.17443 +[29] validation_0-rmse:0.19669 validation_1-rmse:0.17264 +[30] validation_0-rmse:0.19541 validation_1-rmse:0.17054 +[31] validation_0-rmse:0.19401 validation_1-rmse:0.16881 +[32] validation_0-rmse:0.19263 validation_1-rmse:0.16719 +[33] validation_0-rmse:0.19134 validation_1-rmse:0.16560 +[34] validation_0-rmse:0.18996 validation_1-rmse:0.16365 +[35] validation_0-rmse:0.18864 validation_1-rmse:0.16211 +[36] validation_0-rmse:0.18752 validation_1-rmse:0.16069 +[37] validation_0-rmse:0.18652 validation_1-rmse:0.15898 +[38] validation_0-rmse:0.18540 validation_1-rmse:0.15751 +[39] validation_0-rmse:0.18429 validation_1-rmse:0.15616 +[40] validation_0-rmse:0.18317 validation_1-rmse:0.15475 +[41] validation_0-rmse:0.18215 validation_1-rmse:0.15324 +[42] validation_0-rmse:0.18119 validation_1-rmse:0.15199 +[43] validation_0-rmse:0.18008 validation_1-rmse:0.15057 +[44] validation_0-rmse:0.17926 validation_1-rmse:0.14942 +[45] validation_0-rmse:0.17841 validation_1-rmse:0.14813 +[46] validation_0-rmse:0.17755 validation_1-rmse:0.14700 +[47] validation_0-rmse:0.17672 validation_1-rmse:0.14572 +[48] validation_0-rmse:0.17586 validation_1-rmse:0.14466 +[49] validation_0-rmse:0.17511 validation_1-rmse:0.14354 +[50] validation_0-rmse:0.17440 validation_1-rmse:0.14236 +[51] validation_0-rmse:0.17354 validation_1-rmse:0.14130 +[52] validation_0-rmse:0.17281 validation_1-rmse:0.14035 +[53] validation_0-rmse:0.17210 validation_1-rmse:0.13942 +[54] validation_0-rmse:0.17136 validation_1-rmse:0.13843 +[55] validation_0-rmse:0.17045 validation_1-rmse:0.13715 +[56] validation_0-rmse:0.16971 validation_1-rmse:0.13629 +[57] validation_0-rmse:0.16900 validation_1-rmse:0.13511 +[58] validation_0-rmse:0.16834 validation_1-rmse:0.13426 +[59] validation_0-rmse:0.16763 validation_1-rmse:0.13323 +[60] validation_0-rmse:0.16702 validation_1-rmse:0.13242 +[61] validation_0-rmse:0.16639 validation_1-rmse:0.13164 +[62] validation_0-rmse:0.16586 validation_1-rmse:0.13079 +[63] validation_0-rmse:0.16527 validation_1-rmse:0.13006 +[64] validation_0-rmse:0.16458 validation_1-rmse:0.12914 +[65] validation_0-rmse:0.16396 validation_1-rmse:0.12841 +[66] validation_0-rmse:0.16332 validation_1-rmse:0.12742 +[67] validation_0-rmse:0.16290 validation_1-rmse:0.12665 +[68] validation_0-rmse:0.16248 validation_1-rmse:0.12584 +[69] validation_0-rmse:0.16192 validation_1-rmse:0.12503 +[70] validation_0-rmse:0.16128 validation_1-rmse:0.12435 +[71] validation_0-rmse:0.16078 validation_1-rmse:0.12371 +[72] validation_0-rmse:0.16032 validation_1-rmse:0.12311 +[73] validation_0-rmse:0.15998 validation_1-rmse:0.12241 +[74] validation_0-rmse:0.15959 validation_1-rmse:0.12184 +[75] validation_0-rmse:0.15922 validation_1-rmse:0.12121 +[76] validation_0-rmse:0.15877 validation_1-rmse:0.12064 +[77] validation_0-rmse:0.15830 validation_1-rmse:0.11981 +[78] validation_0-rmse:0.15791 validation_1-rmse:0.11927 +[79] validation_0-rmse:0.15751 validation_1-rmse:0.11859 +[80] validation_0-rmse:0.15716 validation_1-rmse:0.11795 +[81] validation_0-rmse:0.15680 validation_1-rmse:0.11740 +[82] validation_0-rmse:0.15624 validation_1-rmse:0.11683 +[83] validation_0-rmse:0.15578 validation_1-rmse:0.11632 +[84] validation_0-rmse:0.15553 validation_1-rmse:0.11586 +[85] validation_0-rmse:0.15471 validation_1-rmse:0.11513 +[86] validation_0-rmse:0.15444 validation_1-rmse:0.11465 +[87] validation_0-rmse:0.15417 validation_1-rmse:0.11406 +[88] validation_0-rmse:0.15387 validation_1-rmse:0.11359 +[89] validation_0-rmse:0.15359 validation_1-rmse:0.11319 +[90] validation_0-rmse:0.15332 validation_1-rmse:0.11269 +[91] validation_0-rmse:0.15301 validation_1-rmse:0.11221 +[92] validation_0-rmse:0.15258 validation_1-rmse:0.11176 +[93] validation_0-rmse:0.15231 validation_1-rmse:0.11135 +[94] validation_0-rmse:0.15202 validation_1-rmse:0.11093 +[95] validation_0-rmse:0.15185 validation_1-rmse:0.11041 +[96] validation_0-rmse:0.15173 validation_1-rmse:0.11000 +[97] validation_0-rmse:0.15150 validation_1-rmse:0.10961 +[98] validation_0-rmse:0.15114 validation_1-rmse:0.10917 +[99] validation_0-rmse:0.15096 validation_1-rmse:0.10882 +2025-04-29 01:55:59,097 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:55:59,098 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:55:59,706 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:55:59,706 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather +2025-04-29 01:55:59,715 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,723 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:55:59,725 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.079967 +1 0.003285 50.079967 +2 0.001898 50.079967 +3 0.000484 50.079967 +4 0.001688 50.079967 +2025-04-29 01:55:59,732 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:55:59,739 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:55:59,741 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:55:59,745 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600 +2025-04-29 01:55:59,746 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:55:59,762 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:55:59,762 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.28468 validation_1-rmse:0.28491 +[1] validation_0-rmse:0.27961 validation_1-rmse:0.27914 +[2] validation_0-rmse:0.27524 validation_1-rmse:0.27370 +[3] validation_0-rmse:0.27054 validation_1-rmse:0.26858 +[4] validation_0-rmse:0.26601 validation_1-rmse:0.26362 +[5] validation_0-rmse:0.26219 validation_1-rmse:0.25896 +[6] validation_0-rmse:0.25814 validation_1-rmse:0.25450 +[7] validation_0-rmse:0.25500 validation_1-rmse:0.25026 +[8] validation_0-rmse:0.25145 validation_1-rmse:0.24602 +[9] validation_0-rmse:0.24843 validation_1-rmse:0.24213 +[10] validation_0-rmse:0.24527 validation_1-rmse:0.23824 +[11] validation_0-rmse:0.24238 validation_1-rmse:0.23440 +[12] validation_0-rmse:0.23940 validation_1-rmse:0.23096 +[13] validation_0-rmse:0.23630 validation_1-rmse:0.22764 +[14] validation_0-rmse:0.23385 validation_1-rmse:0.22440 +[15] validation_0-rmse:0.23099 validation_1-rmse:0.22128 +[16] validation_0-rmse:0.22865 validation_1-rmse:0.21801 +[17] validation_0-rmse:0.22620 validation_1-rmse:0.21515 +[18] validation_0-rmse:0.22375 validation_1-rmse:0.21210 +[19] validation_0-rmse:0.22142 validation_1-rmse:0.20925 +[20] validation_0-rmse:0.21927 validation_1-rmse:0.20663 +[21] validation_0-rmse:0.21720 validation_1-rmse:0.20416 +[22] validation_0-rmse:0.21528 validation_1-rmse:0.20170 +[23] validation_0-rmse:0.21330 validation_1-rmse:0.19913 +[24] validation_0-rmse:0.21136 validation_1-rmse:0.19693 +[25] validation_0-rmse:0.21002 validation_1-rmse:0.19438 +[26] validation_0-rmse:0.20807 validation_1-rmse:0.19222 +[27] validation_0-rmse:0.20636 validation_1-rmse:0.19016 +[28] validation_0-rmse:0.20439 validation_1-rmse:0.18763 +[29] validation_0-rmse:0.20276 validation_1-rmse:0.18559 +[30] validation_0-rmse:0.20114 validation_1-rmse:0.18380 +[31] validation_0-rmse:0.19965 validation_1-rmse:0.18163 +[32] validation_0-rmse:0.19833 validation_1-rmse:0.17955 +[33] validation_0-rmse:0.19688 validation_1-rmse:0.17782 +[34] validation_0-rmse:0.19558 validation_1-rmse:0.17614 +[35] validation_0-rmse:0.19420 validation_1-rmse:0.17451 +[36] validation_0-rmse:0.19297 validation_1-rmse:0.17293 +[37] validation_0-rmse:0.19169 validation_1-rmse:0.17111 +[38] validation_0-rmse:0.19038 validation_1-rmse:0.16943 +[39] validation_0-rmse:0.18941 validation_1-rmse:0.16798 +[40] validation_0-rmse:0.18828 validation_1-rmse:0.16657 +[41] validation_0-rmse:0.18724 validation_1-rmse:0.16485 +[42] validation_0-rmse:0.18620 validation_1-rmse:0.16347 +[43] validation_0-rmse:0.18525 validation_1-rmse:0.16204 +[44] validation_0-rmse:0.18429 validation_1-rmse:0.16073 +[45] validation_0-rmse:0.18324 validation_1-rmse:0.15951 +[46] validation_0-rmse:0.18250 validation_1-rmse:0.15797 +[47] validation_0-rmse:0.18157 validation_1-rmse:0.15682 +[48] validation_0-rmse:0.18069 validation_1-rmse:0.15566 +[49] validation_0-rmse:0.18002 validation_1-rmse:0.15440 +[50] validation_0-rmse:0.17914 validation_1-rmse:0.15322 +[51] validation_0-rmse:0.17842 validation_1-rmse:0.15220 +[52] validation_0-rmse:0.17756 validation_1-rmse:0.15107 +[53] validation_0-rmse:0.17668 validation_1-rmse:0.15007 +[54] validation_0-rmse:0.17596 validation_1-rmse:0.14866 +[55] validation_0-rmse:0.17525 validation_1-rmse:0.14775 +[56] validation_0-rmse:0.17467 validation_1-rmse:0.14653 +[57] validation_0-rmse:0.17390 validation_1-rmse:0.14564 +[58] validation_0-rmse:0.17326 validation_1-rmse:0.14478 +[59] validation_0-rmse:0.17273 validation_1-rmse:0.14356 +[60] validation_0-rmse:0.17218 validation_1-rmse:0.14269 +[61] validation_0-rmse:0.17157 validation_1-rmse:0.14186 +[62] validation_0-rmse:0.17120 validation_1-rmse:0.14083 +[63] validation_0-rmse:0.17069 validation_1-rmse:0.14002 +[64] validation_0-rmse:0.17012 validation_1-rmse:0.13912 +[65] validation_0-rmse:0.16942 validation_1-rmse:0.13834 +[66] validation_0-rmse:0.16914 validation_1-rmse:0.13720 +[67] validation_0-rmse:0.16856 validation_1-rmse:0.13648 +[68] validation_0-rmse:0.16800 validation_1-rmse:0.13569 +[69] validation_0-rmse:0.16796 validation_1-rmse:0.13472 +[70] validation_0-rmse:0.16737 validation_1-rmse:0.13405 +[71] validation_0-rmse:0.16686 validation_1-rmse:0.13342 +[72] validation_0-rmse:0.16639 validation_1-rmse:0.13270 +[73] validation_0-rmse:0.16648 validation_1-rmse:0.13149 +[74] validation_0-rmse:0.16609 validation_1-rmse:0.13086 +[75] validation_0-rmse:0.16560 validation_1-rmse:0.13025 +[76] validation_0-rmse:0.16530 validation_1-rmse:0.12925 +[77] validation_0-rmse:0.16492 validation_1-rmse:0.12824 +[78] validation_0-rmse:0.16451 validation_1-rmse:0.12770 +[79] validation_0-rmse:0.16414 validation_1-rmse:0.12710 +[80] validation_0-rmse:0.16377 validation_1-rmse:0.12654 +[81] validation_0-rmse:0.16338 validation_1-rmse:0.12595 +[82] validation_0-rmse:0.16317 validation_1-rmse:0.12490 +[83] validation_0-rmse:0.16249 validation_1-rmse:0.12361 +[84] validation_0-rmse:0.16217 validation_1-rmse:0.12307 +[85] validation_0-rmse:0.16178 validation_1-rmse:0.12255 +[86] validation_0-rmse:0.16149 validation_1-rmse:0.12206 +[87] validation_0-rmse:0.16113 validation_1-rmse:0.12155 +[88] validation_0-rmse:0.16049 validation_1-rmse:0.12061 +[89] validation_0-rmse:0.16008 validation_1-rmse:0.11990 +[90] validation_0-rmse:0.15955 validation_1-rmse:0.11882 +[91] validation_0-rmse:0.15927 validation_1-rmse:0.11842 +[92] validation_0-rmse:0.15891 validation_1-rmse:0.11796 +[93] validation_0-rmse:0.15880 validation_1-rmse:0.11730 +[94] validation_0-rmse:0.15829 validation_1-rmse:0.11631 +[95] validation_0-rmse:0.15809 validation_1-rmse:0.11584 +[96] validation_0-rmse:0.15778 validation_1-rmse:0.11544 +[97] validation_0-rmse:0.15763 validation_1-rmse:0.11504 +[98] validation_0-rmse:0.15724 validation_1-rmse:0.11438 +[99] validation_0-rmse:0.15694 validation_1-rmse:0.11396 +2025-04-29 01:56:05,520 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) -------------------- +2025-04-29 01:56:05,521 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:06,027 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:56:06,027 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather +2025-04-29 01:56:06,037 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,045 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:56:06,046 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.024153 +1 0.003285 50.024153 +2 0.001898 50.024153 +3 0.000484 50.024153 +4 0.001688 50.024153 +2025-04-29 01:56:06,057 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT +2025-04-29 01:56:06,064 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:56:06,065 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.003572 50.093162 +1 0.003285 50.093162 +2 0.001898 50.093162 +3 0.000484 50.093162 +4 0.001688 50.093162 +2025-04-29 01:56:06,070 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600 +2025-04-29 01:56:06,071 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT -------------------- +2025-04-29 01:56:06,087 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:06,088 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:56:10,904 - datasieve.pipeline - INFO - DI tossed 2001 predictions for being too far from training data. +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features +2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.32950 validation_1-rmse:0.29220 +[1] validation_0-rmse:0.32402 validation_1-rmse:0.28580 +[2] validation_0-rmse:0.31922 validation_1-rmse:0.27974 +[3] validation_0-rmse:0.31450 validation_1-rmse:0.27409 +[4] validation_0-rmse:0.30969 validation_1-rmse:0.26866 +[5] validation_0-rmse:0.30585 validation_1-rmse:0.26346 +[6] validation_0-rmse:0.30202 validation_1-rmse:0.25855 +[7] validation_0-rmse:0.29888 validation_1-rmse:0.25375 +[8] validation_0-rmse:0.29520 validation_1-rmse:0.24919 +[9] validation_0-rmse:0.29164 validation_1-rmse:0.24487 +[10] validation_0-rmse:0.28843 validation_1-rmse:0.24072 +[11] validation_0-rmse:0.28514 validation_1-rmse:0.23667 +[12] validation_0-rmse:0.28114 validation_1-rmse:0.23279 +[13] validation_0-rmse:0.27740 validation_1-rmse:0.22909 +[14] validation_0-rmse:0.27421 validation_1-rmse:0.22543 +[15] validation_0-rmse:0.27115 validation_1-rmse:0.22210 +[16] validation_0-rmse:0.26820 validation_1-rmse:0.21859 +[17] validation_0-rmse:0.26549 validation_1-rmse:0.21528 +[18] validation_0-rmse:0.26254 validation_1-rmse:0.21226 +[19] validation_0-rmse:0.25967 validation_1-rmse:0.20927 +[20] validation_0-rmse:0.25735 validation_1-rmse:0.20641 +[21] validation_0-rmse:0.25470 validation_1-rmse:0.20366 +[22] validation_0-rmse:0.25265 validation_1-rmse:0.20073 +[23] validation_0-rmse:0.25054 validation_1-rmse:0.19819 +[24] validation_0-rmse:0.24806 validation_1-rmse:0.19573 +[25] validation_0-rmse:0.24570 validation_1-rmse:0.19304 +[26] validation_0-rmse:0.24361 validation_1-rmse:0.19076 +[27] validation_0-rmse:0.24148 validation_1-rmse:0.18853 +[28] validation_0-rmse:0.24014 validation_1-rmse:0.18621 +[29] validation_0-rmse:0.23792 validation_1-rmse:0.18410 +[30] validation_0-rmse:0.23603 validation_1-rmse:0.18203 +[31] validation_0-rmse:0.23421 validation_1-rmse:0.17990 +[32] validation_0-rmse:0.23264 validation_1-rmse:0.17800 +[33] validation_0-rmse:0.23087 validation_1-rmse:0.17616 +[34] validation_0-rmse:0.22949 validation_1-rmse:0.17427 +[35] validation_0-rmse:0.22857 validation_1-rmse:0.17234 +[36] validation_0-rmse:0.22690 validation_1-rmse:0.17065 +[37] validation_0-rmse:0.22566 validation_1-rmse:0.16898 +[38] validation_0-rmse:0.22462 validation_1-rmse:0.16738 +[39] validation_0-rmse:0.22376 validation_1-rmse:0.16567 +[40] validation_0-rmse:0.22232 validation_1-rmse:0.16410 +[41] validation_0-rmse:0.22105 validation_1-rmse:0.16265 +[42] validation_0-rmse:0.22006 validation_1-rmse:0.16111 +[43] validation_0-rmse:0.21847 validation_1-rmse:0.15976 +[44] validation_0-rmse:0.21782 validation_1-rmse:0.15824 +[45] validation_0-rmse:0.21641 validation_1-rmse:0.15686 +[46] validation_0-rmse:0.21552 validation_1-rmse:0.15554 +[47] validation_0-rmse:0.21459 validation_1-rmse:0.15417 +[48] validation_0-rmse:0.21339 validation_1-rmse:0.15293 +[49] validation_0-rmse:0.21255 validation_1-rmse:0.15176 +[50] validation_0-rmse:0.21192 validation_1-rmse:0.15047 +[51] validation_0-rmse:0.21115 validation_1-rmse:0.14910 +[52] validation_0-rmse:0.21072 validation_1-rmse:0.14774 +[53] validation_0-rmse:0.20992 validation_1-rmse:0.14670 +[54] validation_0-rmse:0.20839 validation_1-rmse:0.14541 +[55] validation_0-rmse:0.20753 validation_1-rmse:0.14442 +[56] validation_0-rmse:0.20648 validation_1-rmse:0.14328 +[57] validation_0-rmse:0.20564 validation_1-rmse:0.14229 +[58] validation_0-rmse:0.20473 validation_1-rmse:0.14137 +[59] validation_0-rmse:0.20418 validation_1-rmse:0.14011 +[60] validation_0-rmse:0.20341 validation_1-rmse:0.13923 +[61] validation_0-rmse:0.20258 validation_1-rmse:0.13839 +[62] validation_0-rmse:0.20230 validation_1-rmse:0.13723 +[63] validation_0-rmse:0.20075 validation_1-rmse:0.13546 +[64] validation_0-rmse:0.20007 validation_1-rmse:0.13467 +[65] validation_0-rmse:0.19937 validation_1-rmse:0.13387 +[66] validation_0-rmse:0.19875 validation_1-rmse:0.13296 +[67] validation_0-rmse:0.19709 validation_1-rmse:0.13137 +[68] validation_0-rmse:0.19675 validation_1-rmse:0.13042 +[69] validation_0-rmse:0.19617 validation_1-rmse:0.12968 +[70] validation_0-rmse:0.19560 validation_1-rmse:0.12900 +[71] validation_0-rmse:0.19492 validation_1-rmse:0.12834 +[72] validation_0-rmse:0.19319 validation_1-rmse:0.12681 +[73] validation_0-rmse:0.19272 validation_1-rmse:0.12612 +[74] validation_0-rmse:0.19230 validation_1-rmse:0.12535 +[75] validation_0-rmse:0.19170 validation_1-rmse:0.12474 +[76] validation_0-rmse:0.19058 validation_1-rmse:0.12338 +[77] validation_0-rmse:0.19010 validation_1-rmse:0.12279 +[78] validation_0-rmse:0.18961 validation_1-rmse:0.12223 +[79] validation_0-rmse:0.18960 validation_1-rmse:0.12156 +[80] validation_0-rmse:0.18882 validation_1-rmse:0.12038 +[81] validation_0-rmse:0.18819 validation_1-rmse:0.11975 +[82] validation_0-rmse:0.18789 validation_1-rmse:0.11916 +[83] validation_0-rmse:0.18738 validation_1-rmse:0.11864 +[84] validation_0-rmse:0.18718 validation_1-rmse:0.11801 +[85] validation_0-rmse:0.18600 validation_1-rmse:0.11698 +[86] validation_0-rmse:0.18572 validation_1-rmse:0.11653 +[87] validation_0-rmse:0.18534 validation_1-rmse:0.11603 +[88] validation_0-rmse:0.18478 validation_1-rmse:0.11508 +[89] validation_0-rmse:0.18430 validation_1-rmse:0.11459 +[90] validation_0-rmse:0.18447 validation_1-rmse:0.11396 +[91] validation_0-rmse:0.18424 validation_1-rmse:0.11352 +[92] validation_0-rmse:0.18367 validation_1-rmse:0.11307 +[93] validation_0-rmse:0.18333 validation_1-rmse:0.11265 +[94] validation_0-rmse:0.18313 validation_1-rmse:0.11216 +[95] validation_0-rmse:0.18275 validation_1-rmse:0.11157 +[96] validation_0-rmse:0.18275 validation_1-rmse:0.11106 +[97] validation_0-rmse:0.18248 validation_1-rmse:0.11068 +[98] validation_0-rmse:0.18220 validation_1-rmse:0.11033 +[99] validation_0-rmse:0.18198 validation_1-rmse:0.10994 +2025-04-29 01:56:11,705 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.63 secs) -------------------- +2025-04-29 01:56:11,706 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:12,255 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:56:12,275 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +1 33535 +0 33236 +2025-04-29 01:56:12,276 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 35773 +1.0 30998 +2025-04-29 01:56:12,279 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT +2025-04-29 01:56:12,281 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges +2025-04-29 01:56:12,282 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains +2025-04-29 01:56:12,283 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1735689600_prediction.feather +2025-04-29 01:56:12,334 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690 +2025-04-29 01:56:12,335 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770 +2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:12,518 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450 +2025-04-29 01:56:12,519 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00 +2025-04-29 01:56:13,040 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,046 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,) +2025-04-29 01:56:13,047 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.58814 +1 0.003044 49.58814 +2 0.000465 49.58814 +3 -0.000380 49.58814 +4 0.002829 49.58814 +2025-04-29 01:56:13,052 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:13,057 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:13,059 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:13,066 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1735689600/cb_sol_1735689600 +2025-04-29 01:56:13,066 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:13,095 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:13,096 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 -------------------- +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30164 validation_1-rmse:0.29585 +[1] validation_0-rmse:0.29609 validation_1-rmse:0.28921 +[2] validation_0-rmse:0.29103 validation_1-rmse:0.28298 +[3] validation_0-rmse:0.28604 validation_1-rmse:0.27706 +[4] validation_0-rmse:0.28108 validation_1-rmse:0.27129 +[5] validation_0-rmse:0.27670 validation_1-rmse:0.26609 +[6] validation_0-rmse:0.27234 validation_1-rmse:0.26092 +[7] validation_0-rmse:0.26874 validation_1-rmse:0.25593 +[8] validation_0-rmse:0.26461 validation_1-rmse:0.25118 +[9] validation_0-rmse:0.26074 validation_1-rmse:0.24677 +[10] validation_0-rmse:0.25745 validation_1-rmse:0.24239 +[11] validation_0-rmse:0.25460 validation_1-rmse:0.23832 +[12] validation_0-rmse:0.25121 validation_1-rmse:0.23441 +[13] validation_0-rmse:0.24825 validation_1-rmse:0.23068 +[14] validation_0-rmse:0.24580 validation_1-rmse:0.22694 +[15] validation_0-rmse:0.24286 validation_1-rmse:0.22346 +[16] validation_0-rmse:0.24051 validation_1-rmse:0.22006 +[17] validation_0-rmse:0.23821 validation_1-rmse:0.21690 +[18] validation_0-rmse:0.23549 validation_1-rmse:0.21383 +[19] validation_0-rmse:0.23335 validation_1-rmse:0.21087 +[20] validation_0-rmse:0.23089 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22918 validation_1-rmse:0.20505 +[22] validation_0-rmse:0.22716 validation_1-rmse:0.20240 +[23] validation_0-rmse:0.22562 validation_1-rmse:0.19981 +[24] validation_0-rmse:0.22385 validation_1-rmse:0.19723 +[25] validation_0-rmse:0.22201 validation_1-rmse:0.19473 +[26] validation_0-rmse:0.22016 validation_1-rmse:0.19245 +[27] validation_0-rmse:0.21834 validation_1-rmse:0.19024 +[28] validation_0-rmse:0.21671 validation_1-rmse:0.18789 +[29] validation_0-rmse:0.21493 validation_1-rmse:0.18579 +[30] validation_0-rmse:0.21385 validation_1-rmse:0.18351 +[31] validation_0-rmse:0.21216 validation_1-rmse:0.18156 +[32] validation_0-rmse:0.21088 validation_1-rmse:0.17941 +[33] validation_0-rmse:0.20953 validation_1-rmse:0.17754 +[34] validation_0-rmse:0.20805 validation_1-rmse:0.17575 +[35] validation_0-rmse:0.20648 validation_1-rmse:0.17399 +[36] validation_0-rmse:0.20515 validation_1-rmse:0.17220 +[37] validation_0-rmse:0.20382 validation_1-rmse:0.17031 +[38] validation_0-rmse:0.20257 validation_1-rmse:0.16871 +[39] validation_0-rmse:0.20125 validation_1-rmse:0.16718 +[40] validation_0-rmse:0.20005 validation_1-rmse:0.16574 +[41] validation_0-rmse:0.19885 validation_1-rmse:0.16415 +[42] validation_0-rmse:0.19789 validation_1-rmse:0.16270 +[43] validation_0-rmse:0.19680 validation_1-rmse:0.16130 +[44] validation_0-rmse:0.19564 validation_1-rmse:0.15993 +[45] validation_0-rmse:0.19480 validation_1-rmse:0.15854 +[46] validation_0-rmse:0.19376 validation_1-rmse:0.15728 +[47] validation_0-rmse:0.19290 validation_1-rmse:0.15568 +[48] validation_0-rmse:0.19223 validation_1-rmse:0.15445 +[49] validation_0-rmse:0.19129 validation_1-rmse:0.15330 +[50] validation_0-rmse:0.19035 validation_1-rmse:0.15194 +[51] validation_0-rmse:0.18948 validation_1-rmse:0.15082 +[52] validation_0-rmse:0.18882 validation_1-rmse:0.14945 +[53] validation_0-rmse:0.18801 validation_1-rmse:0.14840 +[54] validation_0-rmse:0.18707 validation_1-rmse:0.14736 +[55] validation_0-rmse:0.18637 validation_1-rmse:0.14635 +[56] validation_0-rmse:0.18571 validation_1-rmse:0.14542 +[57] validation_0-rmse:0.18497 validation_1-rmse:0.14413 +[58] validation_0-rmse:0.18443 validation_1-rmse:0.14297 +[59] validation_0-rmse:0.18375 validation_1-rmse:0.14203 +[60] validation_0-rmse:0.18319 validation_1-rmse:0.14111 +[61] validation_0-rmse:0.18266 validation_1-rmse:0.14030 +[62] validation_0-rmse:0.18185 validation_1-rmse:0.13914 +[63] validation_0-rmse:0.18145 validation_1-rmse:0.13831 +[64] validation_0-rmse:0.18135 validation_1-rmse:0.13720 +[65] validation_0-rmse:0.18075 validation_1-rmse:0.13643 +[66] validation_0-rmse:0.18020 validation_1-rmse:0.13560 +[67] validation_0-rmse:0.17951 validation_1-rmse:0.13485 +[68] validation_0-rmse:0.17888 validation_1-rmse:0.13414 +[69] validation_0-rmse:0.17850 validation_1-rmse:0.13343 +[70] validation_0-rmse:0.17798 validation_1-rmse:0.13224 +[71] validation_0-rmse:0.17751 validation_1-rmse:0.13133 +[72] validation_0-rmse:0.17711 validation_1-rmse:0.13062 +[73] validation_0-rmse:0.17701 validation_1-rmse:0.12966 +[74] validation_0-rmse:0.17648 validation_1-rmse:0.12872 +[75] validation_0-rmse:0.17611 validation_1-rmse:0.12806 +[76] validation_0-rmse:0.17573 validation_1-rmse:0.12732 +[77] validation_0-rmse:0.17528 validation_1-rmse:0.12664 +[78] validation_0-rmse:0.17478 validation_1-rmse:0.12605 +[79] validation_0-rmse:0.17432 validation_1-rmse:0.12518 +[80] validation_0-rmse:0.17391 validation_1-rmse:0.12466 +[81] validation_0-rmse:0.17358 validation_1-rmse:0.12398 +[82] validation_0-rmse:0.17315 validation_1-rmse:0.12342 +[83] validation_0-rmse:0.17260 validation_1-rmse:0.12276 +[84] validation_0-rmse:0.17220 validation_1-rmse:0.12222 +[85] validation_0-rmse:0.17182 validation_1-rmse:0.12176 +[86] validation_0-rmse:0.17152 validation_1-rmse:0.12124 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12046 +[88] validation_0-rmse:0.17085 validation_1-rmse:0.11974 +[89] validation_0-rmse:0.17053 validation_1-rmse:0.11930 +[90] validation_0-rmse:0.17018 validation_1-rmse:0.11888 +[91] validation_0-rmse:0.17011 validation_1-rmse:0.11810 +[92] validation_0-rmse:0.16980 validation_1-rmse:0.11762 +[93] validation_0-rmse:0.16956 validation_1-rmse:0.11689 +[94] validation_0-rmse:0.16923 validation_1-rmse:0.11641 +[95] validation_0-rmse:0.16912 validation_1-rmse:0.11579 +[96] validation_0-rmse:0.16878 validation_1-rmse:0.11530 +[97] validation_0-rmse:0.16857 validation_1-rmse:0.11489 +[98] validation_0-rmse:0.16824 validation_1-rmse:0.11442 +[99] validation_0-rmse:0.16824 validation_1-rmse:0.11403 +2025-04-29 01:56:19,586 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.52 secs) -------------------- +2025-04-29 01:56:19,587 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:20,174 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains +2025-04-29 01:56:20,175 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1736553600_prediction.feather +2025-04-29 01:56:20,179 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,185 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,) +2025-04-29 01:56:20,186 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.68088 +1 0.003044 49.68088 +2 0.000465 49.68088 +3 -0.000380 49.68088 +4 0.002829 49.68088 +2025-04-29 01:56:20,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:20,197 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:20,199 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:20,204 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1736553600/cb_sol_1736553600 +2025-04-29 01:56:20,205 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:20,227 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:20,228 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 -------------------- +2025-04-29 01:56:25,109 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data. +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29597 validation_1-rmse:0.29016 +[1] validation_0-rmse:0.29075 validation_1-rmse:0.28391 +[2] validation_0-rmse:0.28602 validation_1-rmse:0.27798 +[3] validation_0-rmse:0.28062 validation_1-rmse:0.27213 +[4] validation_0-rmse:0.27647 validation_1-rmse:0.26682 +[5] validation_0-rmse:0.27188 validation_1-rmse:0.26144 +[6] validation_0-rmse:0.26781 validation_1-rmse:0.25655 +[7] validation_0-rmse:0.26412 validation_1-rmse:0.25180 +[8] validation_0-rmse:0.25994 validation_1-rmse:0.24709 +[9] validation_0-rmse:0.25649 validation_1-rmse:0.24277 +[10] validation_0-rmse:0.25332 validation_1-rmse:0.23850 +[11] validation_0-rmse:0.24999 validation_1-rmse:0.23452 +[12] validation_0-rmse:0.24687 validation_1-rmse:0.23072 +[13] validation_0-rmse:0.24432 validation_1-rmse:0.22694 +[14] validation_0-rmse:0.24128 validation_1-rmse:0.22341 +[15] validation_0-rmse:0.23869 validation_1-rmse:0.21969 +[16] validation_0-rmse:0.23628 validation_1-rmse:0.21635 +[17] validation_0-rmse:0.23354 validation_1-rmse:0.21326 +[18] validation_0-rmse:0.23123 validation_1-rmse:0.21007 +[19] validation_0-rmse:0.22919 validation_1-rmse:0.20707 +[20] validation_0-rmse:0.22705 validation_1-rmse:0.20418 +[21] validation_0-rmse:0.22505 validation_1-rmse:0.20149 +[22] validation_0-rmse:0.22285 validation_1-rmse:0.19887 +[23] validation_0-rmse:0.22084 validation_1-rmse:0.19631 +[24] validation_0-rmse:0.21877 validation_1-rmse:0.19389 +[25] validation_0-rmse:0.21748 validation_1-rmse:0.19133 +[26] validation_0-rmse:0.21557 validation_1-rmse:0.18870 +[27] validation_0-rmse:0.21374 validation_1-rmse:0.18648 +[28] validation_0-rmse:0.21183 validation_1-rmse:0.18432 +[29] validation_0-rmse:0.21047 validation_1-rmse:0.18209 +[30] validation_0-rmse:0.20873 validation_1-rmse:0.17990 +[31] validation_0-rmse:0.20717 validation_1-rmse:0.17795 +[32] validation_0-rmse:0.20564 validation_1-rmse:0.17599 +[33] validation_0-rmse:0.20428 validation_1-rmse:0.17421 +[34] validation_0-rmse:0.20290 validation_1-rmse:0.17229 +[35] validation_0-rmse:0.20161 validation_1-rmse:0.17047 +[36] validation_0-rmse:0.20018 validation_1-rmse:0.16878 +[37] validation_0-rmse:0.19923 validation_1-rmse:0.16688 +[38] validation_0-rmse:0.19796 validation_1-rmse:0.16534 +[39] validation_0-rmse:0.19668 validation_1-rmse:0.16355 +[40] validation_0-rmse:0.19543 validation_1-rmse:0.16204 +[41] validation_0-rmse:0.19441 validation_1-rmse:0.16062 +[42] validation_0-rmse:0.19344 validation_1-rmse:0.15910 +[43] validation_0-rmse:0.19256 validation_1-rmse:0.15759 +[44] validation_0-rmse:0.19154 validation_1-rmse:0.15625 +[45] validation_0-rmse:0.19048 validation_1-rmse:0.15494 +[46] validation_0-rmse:0.18937 validation_1-rmse:0.15366 +[47] validation_0-rmse:0.18865 validation_1-rmse:0.15236 +[48] validation_0-rmse:0.18784 validation_1-rmse:0.15112 +[49] validation_0-rmse:0.18704 validation_1-rmse:0.14998 +[50] validation_0-rmse:0.18625 validation_1-rmse:0.14874 +[51] validation_0-rmse:0.18541 validation_1-rmse:0.14763 +[52] validation_0-rmse:0.18456 validation_1-rmse:0.14659 +[53] validation_0-rmse:0.18383 validation_1-rmse:0.14530 +[54] validation_0-rmse:0.18315 validation_1-rmse:0.14420 +[55] validation_0-rmse:0.18234 validation_1-rmse:0.14321 +[56] validation_0-rmse:0.18181 validation_1-rmse:0.14206 +[57] validation_0-rmse:0.18109 validation_1-rmse:0.14106 +[58] validation_0-rmse:0.18033 validation_1-rmse:0.13996 +[59] validation_0-rmse:0.17964 validation_1-rmse:0.13905 +[60] validation_0-rmse:0.17921 validation_1-rmse:0.13820 +[61] validation_0-rmse:0.17865 validation_1-rmse:0.13731 +[62] validation_0-rmse:0.17795 validation_1-rmse:0.13648 +[63] validation_0-rmse:0.17737 validation_1-rmse:0.13559 +[64] validation_0-rmse:0.17680 validation_1-rmse:0.13483 +[65] validation_0-rmse:0.17628 validation_1-rmse:0.13408 +[66] validation_0-rmse:0.17588 validation_1-rmse:0.13303 +[67] validation_0-rmse:0.17530 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.17478 validation_1-rmse:0.13153 +[69] validation_0-rmse:0.17439 validation_1-rmse:0.13081 +[70] validation_0-rmse:0.17401 validation_1-rmse:0.12991 +[71] validation_0-rmse:0.17347 validation_1-rmse:0.12911 +[72] validation_0-rmse:0.17304 validation_1-rmse:0.12838 +[73] validation_0-rmse:0.17254 validation_1-rmse:0.12774 +[74] validation_0-rmse:0.17207 validation_1-rmse:0.12656 +[75] validation_0-rmse:0.17185 validation_1-rmse:0.12571 +[76] validation_0-rmse:0.17126 validation_1-rmse:0.12512 +[77] validation_0-rmse:0.17096 validation_1-rmse:0.12447 +[78] validation_0-rmse:0.17064 validation_1-rmse:0.12381 +[79] validation_0-rmse:0.17024 validation_1-rmse:0.12300 +[80] validation_0-rmse:0.16989 validation_1-rmse:0.12244 +[81] validation_0-rmse:0.16955 validation_1-rmse:0.12180 +[82] validation_0-rmse:0.16924 validation_1-rmse:0.12129 +[83] validation_0-rmse:0.16931 validation_1-rmse:0.12037 +[84] validation_0-rmse:0.16888 validation_1-rmse:0.11970 +[85] validation_0-rmse:0.16845 validation_1-rmse:0.11914 +[86] validation_0-rmse:0.16809 validation_1-rmse:0.11840 +[87] validation_0-rmse:0.16766 validation_1-rmse:0.11760 +[88] validation_0-rmse:0.16741 validation_1-rmse:0.11714 +[89] validation_0-rmse:0.16707 validation_1-rmse:0.11667 +[90] validation_0-rmse:0.16683 validation_1-rmse:0.11592 +[91] validation_0-rmse:0.16643 validation_1-rmse:0.11537 +[92] validation_0-rmse:0.16621 validation_1-rmse:0.11455 +[93] validation_0-rmse:0.16611 validation_1-rmse:0.11396 +[94] validation_0-rmse:0.16587 validation_1-rmse:0.11350 +[95] validation_0-rmse:0.16563 validation_1-rmse:0.11308 +[96] validation_0-rmse:0.16535 validation_1-rmse:0.11237 +[97] validation_0-rmse:0.16487 validation_1-rmse:0.11173 +[98] validation_0-rmse:0.16461 validation_1-rmse:0.11133 +[99] validation_0-rmse:0.16437 validation_1-rmse:0.11096 +2025-04-29 01:56:26,510 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.30 secs) -------------------- +2025-04-29 01:56:26,511 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:27,072 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains +2025-04-29 01:56:27,073 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1737417600_prediction.feather +2025-04-29 01:56:27,079 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,085 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,) +2025-04-29 01:56:27,086 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.97721 +1 0.003044 49.97721 +2 0.000465 49.97721 +3 -0.000380 49.97721 +4 0.002829 49.97721 +2025-04-29 01:56:27,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:27,100 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:27,102 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:27,108 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1737417600/cb_sol_1737417600 +2025-04-29 01:56:27,109 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:27,130 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:27,131 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 -------------------- +2025-04-29 01:56:32,206 - datasieve.pipeline - INFO - DI tossed 1523 predictions for being too far from training data. +2025-04-29 01:56:32,209 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:32,210 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30838 validation_1-rmse:0.28356 +[1] validation_0-rmse:0.30280 validation_1-rmse:0.27752 +[2] validation_0-rmse:0.29759 validation_1-rmse:0.27179 +[3] validation_0-rmse:0.29330 validation_1-rmse:0.26614 +[4] validation_0-rmse:0.28936 validation_1-rmse:0.26091 +[5] validation_0-rmse:0.28544 validation_1-rmse:0.25581 +[6] validation_0-rmse:0.28151 validation_1-rmse:0.25102 +[7] validation_0-rmse:0.27790 validation_1-rmse:0.24636 +[8] validation_0-rmse:0.27429 validation_1-rmse:0.24196 +[9] validation_0-rmse:0.27104 validation_1-rmse:0.23770 +[10] validation_0-rmse:0.26762 validation_1-rmse:0.23356 +[11] validation_0-rmse:0.26472 validation_1-rmse:0.22966 +[12] validation_0-rmse:0.26219 validation_1-rmse:0.22601 +[13] validation_0-rmse:0.25924 validation_1-rmse:0.22234 +[14] validation_0-rmse:0.25634 validation_1-rmse:0.21888 +[15] validation_0-rmse:0.25379 validation_1-rmse:0.21545 +[16] validation_0-rmse:0.25117 validation_1-rmse:0.21221 +[17] validation_0-rmse:0.24877 validation_1-rmse:0.20902 +[18] validation_0-rmse:0.24653 validation_1-rmse:0.20604 +[19] validation_0-rmse:0.24404 validation_1-rmse:0.20315 +[20] validation_0-rmse:0.24194 validation_1-rmse:0.20032 +[21] validation_0-rmse:0.23966 validation_1-rmse:0.19765 +[22] validation_0-rmse:0.23804 validation_1-rmse:0.19481 +[23] validation_0-rmse:0.23599 validation_1-rmse:0.19230 +[24] validation_0-rmse:0.23384 validation_1-rmse:0.18993 +[25] validation_0-rmse:0.23196 validation_1-rmse:0.18756 +[26] validation_0-rmse:0.23057 validation_1-rmse:0.18506 +[27] validation_0-rmse:0.22854 validation_1-rmse:0.18283 +[28] validation_0-rmse:0.22705 validation_1-rmse:0.18071 +[29] validation_0-rmse:0.22557 validation_1-rmse:0.17851 +[30] validation_0-rmse:0.22394 validation_1-rmse:0.17644 +[31] validation_0-rmse:0.22213 validation_1-rmse:0.17452 +[32] validation_0-rmse:0.22064 validation_1-rmse:0.17267 +[33] validation_0-rmse:0.21905 validation_1-rmse:0.17084 +[34] validation_0-rmse:0.21806 validation_1-rmse:0.16880 +[35] validation_0-rmse:0.21693 validation_1-rmse:0.16700 +[36] validation_0-rmse:0.21537 validation_1-rmse:0.16520 +[37] validation_0-rmse:0.21417 validation_1-rmse:0.16362 +[38] validation_0-rmse:0.21282 validation_1-rmse:0.16204 +[39] validation_0-rmse:0.21137 validation_1-rmse:0.16047 +[40] validation_0-rmse:0.20994 validation_1-rmse:0.15897 +[41] validation_0-rmse:0.20878 validation_1-rmse:0.15747 +[42] validation_0-rmse:0.20766 validation_1-rmse:0.15604 +[43] validation_0-rmse:0.20666 validation_1-rmse:0.15444 +[44] validation_0-rmse:0.20566 validation_1-rmse:0.15316 +[45] validation_0-rmse:0.20496 validation_1-rmse:0.15162 +[46] validation_0-rmse:0.20394 validation_1-rmse:0.15038 +[47] validation_0-rmse:0.20277 validation_1-rmse:0.14909 +[48] validation_0-rmse:0.20176 validation_1-rmse:0.14793 +[49] validation_0-rmse:0.20072 validation_1-rmse:0.14681 +[50] validation_0-rmse:0.20058 validation_1-rmse:0.14528 +[51] validation_0-rmse:0.19970 validation_1-rmse:0.14419 +[52] validation_0-rmse:0.19887 validation_1-rmse:0.14284 +[53] validation_0-rmse:0.19809 validation_1-rmse:0.14182 +[54] validation_0-rmse:0.19725 validation_1-rmse:0.14076 +[55] validation_0-rmse:0.19636 validation_1-rmse:0.13981 +[56] validation_0-rmse:0.19615 validation_1-rmse:0.13853 +[57] validation_0-rmse:0.19540 validation_1-rmse:0.13757 +[58] validation_0-rmse:0.19460 validation_1-rmse:0.13664 +[59] validation_0-rmse:0.19418 validation_1-rmse:0.13553 +[60] validation_0-rmse:0.19382 validation_1-rmse:0.13445 +[61] validation_0-rmse:0.19302 validation_1-rmse:0.13363 +[62] validation_0-rmse:0.19218 validation_1-rmse:0.13270 +[63] validation_0-rmse:0.19154 validation_1-rmse:0.13183 +[64] validation_0-rmse:0.19083 validation_1-rmse:0.13105 +[65] validation_0-rmse:0.19005 validation_1-rmse:0.13008 +[66] validation_0-rmse:0.18929 validation_1-rmse:0.12932 +[67] validation_0-rmse:0.18885 validation_1-rmse:0.12851 +[68] validation_0-rmse:0.18837 validation_1-rmse:0.12781 +[69] validation_0-rmse:0.18790 validation_1-rmse:0.12711 +[70] validation_0-rmse:0.18732 validation_1-rmse:0.12617 +[71] validation_0-rmse:0.18682 validation_1-rmse:0.12552 +[72] validation_0-rmse:0.18669 validation_1-rmse:0.12448 +[73] validation_0-rmse:0.18617 validation_1-rmse:0.12382 +[74] validation_0-rmse:0.18587 validation_1-rmse:0.12322 +[75] validation_0-rmse:0.18544 validation_1-rmse:0.12261 +[76] validation_0-rmse:0.18524 validation_1-rmse:0.12162 +[77] validation_0-rmse:0.18486 validation_1-rmse:0.12098 +[78] validation_0-rmse:0.18443 validation_1-rmse:0.12021 +[79] validation_0-rmse:0.18415 validation_1-rmse:0.11963 +[80] validation_0-rmse:0.18393 validation_1-rmse:0.11866 +[81] validation_0-rmse:0.18344 validation_1-rmse:0.11809 +[82] validation_0-rmse:0.18307 validation_1-rmse:0.11748 +[83] validation_0-rmse:0.18257 validation_1-rmse:0.11699 +[84] validation_0-rmse:0.18216 validation_1-rmse:0.11643 +[85] validation_0-rmse:0.18188 validation_1-rmse:0.11595 +[86] validation_0-rmse:0.18168 validation_1-rmse:0.11502 +[87] validation_0-rmse:0.18148 validation_1-rmse:0.11451 +[88] validation_0-rmse:0.18093 validation_1-rmse:0.11378 +[89] validation_0-rmse:0.18054 validation_1-rmse:0.11332 +[90] validation_0-rmse:0.18024 validation_1-rmse:0.11285 +[91] validation_0-rmse:0.17982 validation_1-rmse:0.11241 +[92] validation_0-rmse:0.17950 validation_1-rmse:0.11185 +[93] validation_0-rmse:0.17918 validation_1-rmse:0.11123 +[94] validation_0-rmse:0.17882 validation_1-rmse:0.11072 +[95] validation_0-rmse:0.17881 validation_1-rmse:0.10986 +[96] validation_0-rmse:0.17832 validation_1-rmse:0.10941 +[97] validation_0-rmse:0.17800 validation_1-rmse:0.10897 +[98] validation_0-rmse:0.17774 validation_1-rmse:0.10859 +[99] validation_0-rmse:0.17746 validation_1-rmse:0.10819 +2025-04-29 01:56:33,558 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:33,558 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:34,118 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains +2025-04-29 01:56:34,119 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1738281600_prediction.feather +2025-04-29 01:56:34,124 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,130 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,) +2025-04-29 01:56:34,131 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.941408 +1 0.003044 49.941408 +2 0.000465 49.941408 +3 -0.000380 49.941408 +4 0.002829 49.941408 +2025-04-29 01:56:34,137 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:34,143 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:34,144 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:34,149 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1738281600/cb_sol_1738281600 +2025-04-29 01:56:34,150 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:34,173 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:34,173 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 -------------------- +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29494 validation_1-rmse:0.28739 +[1] validation_0-rmse:0.28930 validation_1-rmse:0.28164 +[2] validation_0-rmse:0.28437 validation_1-rmse:0.27613 +[3] validation_0-rmse:0.27990 validation_1-rmse:0.27106 +[4] validation_0-rmse:0.27541 validation_1-rmse:0.26617 +[5] validation_0-rmse:0.27070 validation_1-rmse:0.26147 +[6] validation_0-rmse:0.26683 validation_1-rmse:0.25687 +[7] validation_0-rmse:0.26280 validation_1-rmse:0.25263 +[8] validation_0-rmse:0.25916 validation_1-rmse:0.24830 +[9] validation_0-rmse:0.25540 validation_1-rmse:0.24420 +[10] validation_0-rmse:0.25186 validation_1-rmse:0.24022 +[11] validation_0-rmse:0.24829 validation_1-rmse:0.23647 +[12] validation_0-rmse:0.24504 validation_1-rmse:0.23286 +[13] validation_0-rmse:0.24183 validation_1-rmse:0.22943 +[14] validation_0-rmse:0.23870 validation_1-rmse:0.22619 +[15] validation_0-rmse:0.23587 validation_1-rmse:0.22274 +[16] validation_0-rmse:0.23325 validation_1-rmse:0.21951 +[17] validation_0-rmse:0.23045 validation_1-rmse:0.21650 +[18] validation_0-rmse:0.22792 validation_1-rmse:0.21367 +[19] validation_0-rmse:0.22524 validation_1-rmse:0.21092 +[20] validation_0-rmse:0.22293 validation_1-rmse:0.20804 +[21] validation_0-rmse:0.22055 validation_1-rmse:0.20549 +[22] validation_0-rmse:0.21831 validation_1-rmse:0.20307 +[23] validation_0-rmse:0.21601 validation_1-rmse:0.20062 +[24] validation_0-rmse:0.21372 validation_1-rmse:0.19810 +[25] validation_0-rmse:0.21154 validation_1-rmse:0.19580 +[26] validation_0-rmse:0.20966 validation_1-rmse:0.19369 +[27] validation_0-rmse:0.20790 validation_1-rmse:0.19130 +[28] validation_0-rmse:0.20602 validation_1-rmse:0.18921 +[29] validation_0-rmse:0.20418 validation_1-rmse:0.18723 +[30] validation_0-rmse:0.20236 validation_1-rmse:0.18525 +[31] validation_0-rmse:0.20057 validation_1-rmse:0.18324 +[32] validation_0-rmse:0.19900 validation_1-rmse:0.18144 +[33] validation_0-rmse:0.19744 validation_1-rmse:0.17941 +[34] validation_0-rmse:0.19608 validation_1-rmse:0.17767 +[35] validation_0-rmse:0.19467 validation_1-rmse:0.17605 +[36] validation_0-rmse:0.19313 validation_1-rmse:0.17422 +[37] validation_0-rmse:0.19156 validation_1-rmse:0.17260 +[38] validation_0-rmse:0.19020 validation_1-rmse:0.17103 +[39] validation_0-rmse:0.18884 validation_1-rmse:0.16948 +[40] validation_0-rmse:0.18767 validation_1-rmse:0.16797 +[41] validation_0-rmse:0.18636 validation_1-rmse:0.16647 +[42] validation_0-rmse:0.18512 validation_1-rmse:0.16505 +[43] validation_0-rmse:0.18403 validation_1-rmse:0.16340 +[44] validation_0-rmse:0.18290 validation_1-rmse:0.16210 +[45] validation_0-rmse:0.18189 validation_1-rmse:0.16085 +[46] validation_0-rmse:0.18090 validation_1-rmse:0.15966 +[47] validation_0-rmse:0.17992 validation_1-rmse:0.15841 +[48] validation_0-rmse:0.17901 validation_1-rmse:0.15728 +[49] validation_0-rmse:0.17817 validation_1-rmse:0.15582 +[50] validation_0-rmse:0.17697 validation_1-rmse:0.15458 +[51] validation_0-rmse:0.17607 validation_1-rmse:0.15349 +[52] validation_0-rmse:0.17516 validation_1-rmse:0.15235 +[53] validation_0-rmse:0.17425 validation_1-rmse:0.15131 +[54] validation_0-rmse:0.17347 validation_1-rmse:0.15032 +[55] validation_0-rmse:0.17275 validation_1-rmse:0.14932 +[56] validation_0-rmse:0.17211 validation_1-rmse:0.14834 +[57] validation_0-rmse:0.17131 validation_1-rmse:0.14741 +[58] validation_0-rmse:0.17072 validation_1-rmse:0.14617 +[59] validation_0-rmse:0.16999 validation_1-rmse:0.14528 +[60] validation_0-rmse:0.16934 validation_1-rmse:0.14416 +[61] validation_0-rmse:0.16887 validation_1-rmse:0.14321 +[62] validation_0-rmse:0.16842 validation_1-rmse:0.14213 +[63] validation_0-rmse:0.16765 validation_1-rmse:0.14130 +[64] validation_0-rmse:0.16691 validation_1-rmse:0.14048 +[65] validation_0-rmse:0.16629 validation_1-rmse:0.13956 +[66] validation_0-rmse:0.16565 validation_1-rmse:0.13882 +[67] validation_0-rmse:0.16530 validation_1-rmse:0.13793 +[68] validation_0-rmse:0.16467 validation_1-rmse:0.13710 +[69] validation_0-rmse:0.16436 validation_1-rmse:0.13621 +[70] validation_0-rmse:0.16377 validation_1-rmse:0.13542 +[71] validation_0-rmse:0.16334 validation_1-rmse:0.13463 +[72] validation_0-rmse:0.16280 validation_1-rmse:0.13394 +[73] validation_0-rmse:0.16230 validation_1-rmse:0.13328 +[74] validation_0-rmse:0.16156 validation_1-rmse:0.13246 +[75] validation_0-rmse:0.16122 validation_1-rmse:0.13151 +[76] validation_0-rmse:0.16080 validation_1-rmse:0.13080 +[77] validation_0-rmse:0.16033 validation_1-rmse:0.13015 +[78] validation_0-rmse:0.15992 validation_1-rmse:0.12951 +[79] validation_0-rmse:0.15950 validation_1-rmse:0.12888 +[80] validation_0-rmse:0.15909 validation_1-rmse:0.12822 +[81] validation_0-rmse:0.15875 validation_1-rmse:0.12744 +[82] validation_0-rmse:0.15831 validation_1-rmse:0.12683 +[83] validation_0-rmse:0.15786 validation_1-rmse:0.12626 +[84] validation_0-rmse:0.15747 validation_1-rmse:0.12572 +[85] validation_0-rmse:0.15724 validation_1-rmse:0.12495 +[86] validation_0-rmse:0.15695 validation_1-rmse:0.12442 +[87] validation_0-rmse:0.15664 validation_1-rmse:0.12382 +[88] validation_0-rmse:0.15651 validation_1-rmse:0.12326 +[89] validation_0-rmse:0.15629 validation_1-rmse:0.12256 +[90] validation_0-rmse:0.15596 validation_1-rmse:0.12196 +[91] validation_0-rmse:0.15559 validation_1-rmse:0.12141 +[92] validation_0-rmse:0.15511 validation_1-rmse:0.12088 +[93] validation_0-rmse:0.15487 validation_1-rmse:0.12033 +[94] validation_0-rmse:0.15472 validation_1-rmse:0.11975 +[95] validation_0-rmse:0.15438 validation_1-rmse:0.11924 +[96] validation_0-rmse:0.15408 validation_1-rmse:0.11882 +[97] validation_0-rmse:0.15382 validation_1-rmse:0.11819 +[98] validation_0-rmse:0.15350 validation_1-rmse:0.11777 +[99] validation_0-rmse:0.15331 validation_1-rmse:0.11727 +2025-04-29 01:56:40,600 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) -------------------- +2025-04-29 01:56:40,601 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:41,171 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains +2025-04-29 01:56:41,172 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1739145600_prediction.feather +2025-04-29 01:56:41,177 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,183 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,) +2025-04-29 01:56:41,185 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.830756 +1 0.003044 49.830756 +2 0.000465 49.830756 +3 -0.000380 49.830756 +4 0.002829 49.830756 +2025-04-29 01:56:41,193 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:41,200 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:41,201 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:41,206 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1739145600/cb_sol_1739145600 +2025-04-29 01:56:41,207 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:41,228 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:41,229 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 -------------------- +2025-04-29 01:56:46,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:46,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29889 validation_1-rmse:0.30153 +[1] validation_0-rmse:0.29317 validation_1-rmse:0.29483 +[2] validation_0-rmse:0.28819 validation_1-rmse:0.28860 +[3] validation_0-rmse:0.28336 validation_1-rmse:0.28273 +[4] validation_0-rmse:0.27885 validation_1-rmse:0.27694 +[5] validation_0-rmse:0.27448 validation_1-rmse:0.27155 +[6] validation_0-rmse:0.27020 validation_1-rmse:0.26634 +[7] validation_0-rmse:0.26629 validation_1-rmse:0.26134 +[8] validation_0-rmse:0.26241 validation_1-rmse:0.25653 +[9] validation_0-rmse:0.25876 validation_1-rmse:0.25192 +[10] validation_0-rmse:0.25559 validation_1-rmse:0.24747 +[11] validation_0-rmse:0.25223 validation_1-rmse:0.24337 +[12] validation_0-rmse:0.24904 validation_1-rmse:0.23934 +[13] validation_0-rmse:0.24639 validation_1-rmse:0.23548 +[14] validation_0-rmse:0.24353 validation_1-rmse:0.23187 +[15] validation_0-rmse:0.24076 validation_1-rmse:0.22837 +[16] validation_0-rmse:0.23849 validation_1-rmse:0.22484 +[17] validation_0-rmse:0.23581 validation_1-rmse:0.22147 +[18] validation_0-rmse:0.23342 validation_1-rmse:0.21814 +[19] validation_0-rmse:0.23133 validation_1-rmse:0.21509 +[20] validation_0-rmse:0.22937 validation_1-rmse:0.21187 +[21] validation_0-rmse:0.22713 validation_1-rmse:0.20902 +[22] validation_0-rmse:0.22509 validation_1-rmse:0.20631 +[23] validation_0-rmse:0.22312 validation_1-rmse:0.20373 +[24] validation_0-rmse:0.22123 validation_1-rmse:0.20076 +[25] validation_0-rmse:0.21951 validation_1-rmse:0.19837 +[26] validation_0-rmse:0.21751 validation_1-rmse:0.19562 +[27] validation_0-rmse:0.21589 validation_1-rmse:0.19309 +[28] validation_0-rmse:0.21422 validation_1-rmse:0.19091 +[29] validation_0-rmse:0.21272 validation_1-rmse:0.18879 +[30] validation_0-rmse:0.21119 validation_1-rmse:0.18660 +[31] validation_0-rmse:0.20982 validation_1-rmse:0.18468 +[32] validation_0-rmse:0.20829 validation_1-rmse:0.18239 +[33] validation_0-rmse:0.20681 validation_1-rmse:0.18048 +[34] validation_0-rmse:0.20548 validation_1-rmse:0.17869 +[35] validation_0-rmse:0.20431 validation_1-rmse:0.17665 +[36] validation_0-rmse:0.20297 validation_1-rmse:0.17483 +[37] validation_0-rmse:0.20174 validation_1-rmse:0.17311 +[38] validation_0-rmse:0.20060 validation_1-rmse:0.17153 +[39] validation_0-rmse:0.19951 validation_1-rmse:0.16958 +[40] validation_0-rmse:0.19848 validation_1-rmse:0.16805 +[41] validation_0-rmse:0.19745 validation_1-rmse:0.16652 +[42] validation_0-rmse:0.19647 validation_1-rmse:0.16509 +[43] validation_0-rmse:0.19570 validation_1-rmse:0.16325 +[44] validation_0-rmse:0.19473 validation_1-rmse:0.16187 +[45] validation_0-rmse:0.19397 validation_1-rmse:0.16012 +[46] validation_0-rmse:0.19314 validation_1-rmse:0.15887 +[47] validation_0-rmse:0.19196 validation_1-rmse:0.15723 +[48] validation_0-rmse:0.19096 validation_1-rmse:0.15595 +[49] validation_0-rmse:0.19009 validation_1-rmse:0.15468 +[50] validation_0-rmse:0.18931 validation_1-rmse:0.15355 +[51] validation_0-rmse:0.18864 validation_1-rmse:0.15207 +[52] validation_0-rmse:0.18786 validation_1-rmse:0.15101 +[53] validation_0-rmse:0.18690 validation_1-rmse:0.14960 +[54] validation_0-rmse:0.18614 validation_1-rmse:0.14859 +[55] validation_0-rmse:0.18550 validation_1-rmse:0.14756 +[56] validation_0-rmse:0.18475 validation_1-rmse:0.14647 +[57] validation_0-rmse:0.18405 validation_1-rmse:0.14545 +[58] validation_0-rmse:0.18346 validation_1-rmse:0.14415 +[59] validation_0-rmse:0.18277 validation_1-rmse:0.14321 +[60] validation_0-rmse:0.18219 validation_1-rmse:0.14221 +[61] validation_0-rmse:0.18158 validation_1-rmse:0.14129 +[62] validation_0-rmse:0.18100 validation_1-rmse:0.14043 +[63] validation_0-rmse:0.18059 validation_1-rmse:0.13920 +[64] validation_0-rmse:0.17997 validation_1-rmse:0.13842 +[65] validation_0-rmse:0.17941 validation_1-rmse:0.13754 +[66] validation_0-rmse:0.17881 validation_1-rmse:0.13652 +[67] validation_0-rmse:0.17823 validation_1-rmse:0.13576 +[68] validation_0-rmse:0.17784 validation_1-rmse:0.13468 +[69] validation_0-rmse:0.17735 validation_1-rmse:0.13396 +[70] validation_0-rmse:0.17687 validation_1-rmse:0.13311 +[71] validation_0-rmse:0.17628 validation_1-rmse:0.13225 +[72] validation_0-rmse:0.17599 validation_1-rmse:0.13154 +[73] validation_0-rmse:0.17542 validation_1-rmse:0.13080 +[74] validation_0-rmse:0.17497 validation_1-rmse:0.13013 +[75] validation_0-rmse:0.17456 validation_1-rmse:0.12954 +[76] validation_0-rmse:0.17416 validation_1-rmse:0.12864 +[77] validation_0-rmse:0.17369 validation_1-rmse:0.12802 +[78] validation_0-rmse:0.17345 validation_1-rmse:0.12735 +[79] validation_0-rmse:0.17302 validation_1-rmse:0.12672 +[80] validation_0-rmse:0.17254 validation_1-rmse:0.12609 +[81] validation_0-rmse:0.17248 validation_1-rmse:0.12527 +[82] validation_0-rmse:0.17210 validation_1-rmse:0.12470 +[83] validation_0-rmse:0.17196 validation_1-rmse:0.12398 +[84] validation_0-rmse:0.17189 validation_1-rmse:0.12334 +[85] validation_0-rmse:0.17155 validation_1-rmse:0.12280 +[86] validation_0-rmse:0.17124 validation_1-rmse:0.12230 +[87] validation_0-rmse:0.17103 validation_1-rmse:0.12178 +[88] validation_0-rmse:0.17086 validation_1-rmse:0.12118 +[89] validation_0-rmse:0.17064 validation_1-rmse:0.12049 +[90] validation_0-rmse:0.17029 validation_1-rmse:0.11993 +[91] validation_0-rmse:0.16981 validation_1-rmse:0.11942 +[92] validation_0-rmse:0.16950 validation_1-rmse:0.11894 +[93] validation_0-rmse:0.16937 validation_1-rmse:0.11833 +[94] validation_0-rmse:0.16928 validation_1-rmse:0.11786 +[95] validation_0-rmse:0.16899 validation_1-rmse:0.11735 +[96] validation_0-rmse:0.16869 validation_1-rmse:0.11693 +[97] validation_0-rmse:0.16843 validation_1-rmse:0.11650 +[98] validation_0-rmse:0.16829 validation_1-rmse:0.11591 +[99] validation_0-rmse:0.16802 validation_1-rmse:0.11547 +2025-04-29 01:56:47,778 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.57 secs) -------------------- +2025-04-29 01:56:47,779 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:48,320 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains +2025-04-29 01:56:48,321 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740009600_prediction.feather +2025-04-29 01:56:48,327 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,333 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,) +2025-04-29 01:56:48,334 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.714422 +1 0.003044 49.714422 +2 0.000465 49.714422 +3 -0.000380 49.714422 +4 0.002829 49.714422 +2025-04-29 01:56:48,346 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:48,353 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:48,354 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:48,361 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740009600/cb_sol_1740009600 +2025-04-29 01:56:48,361 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:48,383 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:48,383 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 -------------------- +2025-04-29 01:56:53,532 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:56:53,533 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.29357 validation_1-rmse:0.28852 +[1] validation_0-rmse:0.28850 validation_1-rmse:0.28219 +[2] validation_0-rmse:0.28292 validation_1-rmse:0.27618 +[3] validation_0-rmse:0.27862 validation_1-rmse:0.27046 +[4] validation_0-rmse:0.27383 validation_1-rmse:0.26510 +[5] validation_0-rmse:0.27018 validation_1-rmse:0.25989 +[6] validation_0-rmse:0.26615 validation_1-rmse:0.25477 +[7] validation_0-rmse:0.26234 validation_1-rmse:0.25007 +[8] validation_0-rmse:0.25794 validation_1-rmse:0.24560 +[9] validation_0-rmse:0.25417 validation_1-rmse:0.24109 +[10] validation_0-rmse:0.25083 validation_1-rmse:0.23700 +[11] validation_0-rmse:0.24683 validation_1-rmse:0.23303 +[12] validation_0-rmse:0.24384 validation_1-rmse:0.22908 +[13] validation_0-rmse:0.24093 validation_1-rmse:0.22542 +[14] validation_0-rmse:0.23743 validation_1-rmse:0.22186 +[15] validation_0-rmse:0.23484 validation_1-rmse:0.21841 +[16] validation_0-rmse:0.23215 validation_1-rmse:0.21525 +[17] validation_0-rmse:0.22951 validation_1-rmse:0.21206 +[18] validation_0-rmse:0.22658 validation_1-rmse:0.20906 +[19] validation_0-rmse:0.22440 validation_1-rmse:0.20615 +[20] validation_0-rmse:0.22193 validation_1-rmse:0.20314 +[21] validation_0-rmse:0.22009 validation_1-rmse:0.20016 +[22] validation_0-rmse:0.21755 validation_1-rmse:0.19751 +[23] validation_0-rmse:0.21578 validation_1-rmse:0.19498 +[24] validation_0-rmse:0.21440 validation_1-rmse:0.19241 +[25] validation_0-rmse:0.21229 validation_1-rmse:0.19006 +[26] validation_0-rmse:0.21038 validation_1-rmse:0.18780 +[27] validation_0-rmse:0.20897 validation_1-rmse:0.18529 +[28] validation_0-rmse:0.20703 validation_1-rmse:0.18313 +[29] validation_0-rmse:0.20556 validation_1-rmse:0.18091 +[30] validation_0-rmse:0.20384 validation_1-rmse:0.17884 +[31] validation_0-rmse:0.20281 validation_1-rmse:0.17690 +[32] validation_0-rmse:0.20169 validation_1-rmse:0.17483 +[33] validation_0-rmse:0.20012 validation_1-rmse:0.17300 +[34] validation_0-rmse:0.19876 validation_1-rmse:0.17106 +[35] validation_0-rmse:0.19755 validation_1-rmse:0.16934 +[36] validation_0-rmse:0.19649 validation_1-rmse:0.16752 +[37] validation_0-rmse:0.19501 validation_1-rmse:0.16586 +[38] validation_0-rmse:0.19423 validation_1-rmse:0.16418 +[39] validation_0-rmse:0.19297 validation_1-rmse:0.16264 +[40] validation_0-rmse:0.19162 validation_1-rmse:0.16092 +[41] validation_0-rmse:0.19049 validation_1-rmse:0.15952 +[42] validation_0-rmse:0.18925 validation_1-rmse:0.15810 +[43] validation_0-rmse:0.18845 validation_1-rmse:0.15638 +[44] validation_0-rmse:0.18730 validation_1-rmse:0.15506 +[45] validation_0-rmse:0.18661 validation_1-rmse:0.15357 +[46] validation_0-rmse:0.18563 validation_1-rmse:0.15226 +[47] validation_0-rmse:0.18473 validation_1-rmse:0.15101 +[48] validation_0-rmse:0.18399 validation_1-rmse:0.14957 +[49] validation_0-rmse:0.18304 validation_1-rmse:0.14841 +[50] validation_0-rmse:0.18219 validation_1-rmse:0.14717 +[51] validation_0-rmse:0.18131 validation_1-rmse:0.14599 +[52] validation_0-rmse:0.18043 validation_1-rmse:0.14492 +[53] validation_0-rmse:0.17966 validation_1-rmse:0.14388 +[54] validation_0-rmse:0.17901 validation_1-rmse:0.14274 +[55] validation_0-rmse:0.17850 validation_1-rmse:0.14134 +[56] validation_0-rmse:0.17764 validation_1-rmse:0.14035 +[57] validation_0-rmse:0.17682 validation_1-rmse:0.13937 +[58] validation_0-rmse:0.17604 validation_1-rmse:0.13844 +[59] validation_0-rmse:0.17526 validation_1-rmse:0.13754 +[60] validation_0-rmse:0.17488 validation_1-rmse:0.13621 +[61] validation_0-rmse:0.17432 validation_1-rmse:0.13530 +[62] validation_0-rmse:0.17345 validation_1-rmse:0.13439 +[63] validation_0-rmse:0.17284 validation_1-rmse:0.13358 +[64] validation_0-rmse:0.17213 validation_1-rmse:0.13278 +[65] validation_0-rmse:0.17164 validation_1-rmse:0.13175 +[66] validation_0-rmse:0.17098 validation_1-rmse:0.13088 +[67] validation_0-rmse:0.17049 validation_1-rmse:0.13002 +[68] validation_0-rmse:0.17000 validation_1-rmse:0.12918 +[69] validation_0-rmse:0.16969 validation_1-rmse:0.12815 +[70] validation_0-rmse:0.16917 validation_1-rmse:0.12746 +[71] validation_0-rmse:0.16857 validation_1-rmse:0.12678 +[72] validation_0-rmse:0.16830 validation_1-rmse:0.12595 +[73] validation_0-rmse:0.16793 validation_1-rmse:0.12522 +[74] validation_0-rmse:0.16752 validation_1-rmse:0.12457 +[75] validation_0-rmse:0.16704 validation_1-rmse:0.12395 +[76] validation_0-rmse:0.16668 validation_1-rmse:0.12316 +[77] validation_0-rmse:0.16621 validation_1-rmse:0.12251 +[78] validation_0-rmse:0.16591 validation_1-rmse:0.12185 +[79] validation_0-rmse:0.16550 validation_1-rmse:0.12115 +[80] validation_0-rmse:0.16506 validation_1-rmse:0.12055 +[81] validation_0-rmse:0.16467 validation_1-rmse:0.12001 +[82] validation_0-rmse:0.16422 validation_1-rmse:0.11944 +[83] validation_0-rmse:0.16379 validation_1-rmse:0.11892 +[84] validation_0-rmse:0.16344 validation_1-rmse:0.11825 +[85] validation_0-rmse:0.16317 validation_1-rmse:0.11766 +[86] validation_0-rmse:0.16289 validation_1-rmse:0.11712 +[87] validation_0-rmse:0.16271 validation_1-rmse:0.11639 +[88] validation_0-rmse:0.16236 validation_1-rmse:0.11591 +[89] validation_0-rmse:0.16210 validation_1-rmse:0.11515 +[90] validation_0-rmse:0.16170 validation_1-rmse:0.11457 +[91] validation_0-rmse:0.16149 validation_1-rmse:0.11411 +[92] validation_0-rmse:0.16132 validation_1-rmse:0.11360 +[93] validation_0-rmse:0.16108 validation_1-rmse:0.11292 +[94] validation_0-rmse:0.16077 validation_1-rmse:0.11247 +[95] validation_0-rmse:0.16040 validation_1-rmse:0.11205 +[96] validation_0-rmse:0.16017 validation_1-rmse:0.11157 +[97] validation_0-rmse:0.15988 validation_1-rmse:0.11117 +[98] validation_0-rmse:0.15964 validation_1-rmse:0.11074 +[99] validation_0-rmse:0.15958 validation_1-rmse:0.11029 +2025-04-29 01:56:54,862 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.50 secs) -------------------- +2025-04-29 01:56:54,863 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:56:55,419 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains +2025-04-29 01:56:55,420 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740873600_prediction.feather +2025-04-29 01:56:55,426 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,433 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,) +2025-04-29 01:56:55,435 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.626186 +1 0.003044 49.626186 +2 0.000465 49.626186 +3 -0.000380 49.626186 +4 0.002829 49.626186 +2025-04-29 01:56:55,445 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:56:55,452 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:56:55,453 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:56:55,459 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740873600/cb_sol_1740873600 +2025-04-29 01:56:55,459 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:56:55,481 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:56:55,482 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 -------------------- +2025-04-29 01:57:00,566 - datasieve.pipeline - INFO - DI tossed 2417 predictions for being too far from training data. +2025-04-29 01:57:00,569 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:00,570 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.33058 validation_1-rmse:0.29214 +[1] validation_0-rmse:0.32414 validation_1-rmse:0.28558 +[2] validation_0-rmse:0.31832 validation_1-rmse:0.27962 +[3] validation_0-rmse:0.31280 validation_1-rmse:0.27380 +[4] validation_0-rmse:0.30679 validation_1-rmse:0.26829 +[5] validation_0-rmse:0.30182 validation_1-rmse:0.26306 +[6] validation_0-rmse:0.29686 validation_1-rmse:0.25797 +[7] validation_0-rmse:0.29291 validation_1-rmse:0.25323 +[8] validation_0-rmse:0.28868 validation_1-rmse:0.24871 +[9] validation_0-rmse:0.28559 validation_1-rmse:0.24437 +[10] validation_0-rmse:0.28262 validation_1-rmse:0.24018 +[11] validation_0-rmse:0.27890 validation_1-rmse:0.23606 +[12] validation_0-rmse:0.27663 validation_1-rmse:0.23225 +[13] validation_0-rmse:0.27306 validation_1-rmse:0.22870 +[14] validation_0-rmse:0.26948 validation_1-rmse:0.22493 +[15] validation_0-rmse:0.26663 validation_1-rmse:0.22157 +[16] validation_0-rmse:0.26427 validation_1-rmse:0.21817 +[17] validation_0-rmse:0.26195 validation_1-rmse:0.21492 +[18] validation_0-rmse:0.25856 validation_1-rmse:0.21177 +[19] validation_0-rmse:0.25606 validation_1-rmse:0.20861 +[20] validation_0-rmse:0.25343 validation_1-rmse:0.20580 +[21] validation_0-rmse:0.25243 validation_1-rmse:0.20301 +[22] validation_0-rmse:0.25066 validation_1-rmse:0.20027 +[23] validation_0-rmse:0.24864 validation_1-rmse:0.19761 +[24] validation_0-rmse:0.24630 validation_1-rmse:0.19522 +[25] validation_0-rmse:0.24491 validation_1-rmse:0.19283 +[26] validation_0-rmse:0.24339 validation_1-rmse:0.19036 +[27] validation_0-rmse:0.24108 validation_1-rmse:0.18818 +[28] validation_0-rmse:0.23976 validation_1-rmse:0.18592 +[29] validation_0-rmse:0.23882 validation_1-rmse:0.18348 +[30] validation_0-rmse:0.23676 validation_1-rmse:0.18142 +[31] validation_0-rmse:0.23520 validation_1-rmse:0.17945 +[32] validation_0-rmse:0.23395 validation_1-rmse:0.17754 +[33] validation_0-rmse:0.23229 validation_1-rmse:0.17545 +[34] validation_0-rmse:0.23073 validation_1-rmse:0.17360 +[35] validation_0-rmse:0.22951 validation_1-rmse:0.17182 +[36] validation_0-rmse:0.22806 validation_1-rmse:0.16995 +[37] validation_0-rmse:0.22713 validation_1-rmse:0.16834 +[38] validation_0-rmse:0.22541 validation_1-rmse:0.16668 +[39] validation_0-rmse:0.22393 validation_1-rmse:0.16509 +[40] validation_0-rmse:0.22282 validation_1-rmse:0.16343 +[41] validation_0-rmse:0.22168 validation_1-rmse:0.16185 +[42] validation_0-rmse:0.22085 validation_1-rmse:0.16046 +[43] validation_0-rmse:0.21991 validation_1-rmse:0.15907 +[44] validation_0-rmse:0.21833 validation_1-rmse:0.15756 +[45] validation_0-rmse:0.21710 validation_1-rmse:0.15618 +[46] validation_0-rmse:0.21619 validation_1-rmse:0.15490 +[47] validation_0-rmse:0.21518 validation_1-rmse:0.15345 +[48] validation_0-rmse:0.21402 validation_1-rmse:0.15221 +[49] validation_0-rmse:0.21305 validation_1-rmse:0.15086 +[50] validation_0-rmse:0.21229 validation_1-rmse:0.14968 +[51] validation_0-rmse:0.21119 validation_1-rmse:0.14854 +[52] validation_0-rmse:0.21019 validation_1-rmse:0.14745 +[53] validation_0-rmse:0.20924 validation_1-rmse:0.14637 +[54] validation_0-rmse:0.20982 validation_1-rmse:0.14517 +[55] validation_0-rmse:0.20888 validation_1-rmse:0.14405 +[56] validation_0-rmse:0.20806 validation_1-rmse:0.14305 +[57] validation_0-rmse:0.20822 validation_1-rmse:0.14169 +[58] validation_0-rmse:0.20741 validation_1-rmse:0.14071 +[59] validation_0-rmse:0.20663 validation_1-rmse:0.13976 +[60] validation_0-rmse:0.20602 validation_1-rmse:0.13882 +[61] validation_0-rmse:0.20523 validation_1-rmse:0.13776 +[62] validation_0-rmse:0.20558 validation_1-rmse:0.13689 +[63] validation_0-rmse:0.20501 validation_1-rmse:0.13605 +[64] validation_0-rmse:0.20348 validation_1-rmse:0.13462 +[65] validation_0-rmse:0.20273 validation_1-rmse:0.13382 +[66] validation_0-rmse:0.20203 validation_1-rmse:0.13306 +[67] validation_0-rmse:0.20166 validation_1-rmse:0.13228 +[68] validation_0-rmse:0.20002 validation_1-rmse:0.13102 +[69] validation_0-rmse:0.19928 validation_1-rmse:0.13021 +[70] validation_0-rmse:0.19870 validation_1-rmse:0.12946 +[71] validation_0-rmse:0.19830 validation_1-rmse:0.12876 +[72] validation_0-rmse:0.19814 validation_1-rmse:0.12801 +[73] validation_0-rmse:0.19798 validation_1-rmse:0.12711 +[74] validation_0-rmse:0.19746 validation_1-rmse:0.12649 +[75] validation_0-rmse:0.19701 validation_1-rmse:0.12588 +[76] validation_0-rmse:0.19555 validation_1-rmse:0.12467 +[77] validation_0-rmse:0.19514 validation_1-rmse:0.12407 +[78] validation_0-rmse:0.19468 validation_1-rmse:0.12347 +[79] validation_0-rmse:0.19439 validation_1-rmse:0.12277 +[80] validation_0-rmse:0.19473 validation_1-rmse:0.12220 +[81] validation_0-rmse:0.19448 validation_1-rmse:0.12154 +[82] validation_0-rmse:0.19418 validation_1-rmse:0.12086 +[83] validation_0-rmse:0.19370 validation_1-rmse:0.12030 +[84] validation_0-rmse:0.19346 validation_1-rmse:0.11976 +[85] validation_0-rmse:0.19322 validation_1-rmse:0.11879 +[86] validation_0-rmse:0.19282 validation_1-rmse:0.11819 +[87] validation_0-rmse:0.19226 validation_1-rmse:0.11770 +[88] validation_0-rmse:0.19187 validation_1-rmse:0.11719 +[89] validation_0-rmse:0.19145 validation_1-rmse:0.11671 +[90] validation_0-rmse:0.19134 validation_1-rmse:0.11619 +[91] validation_0-rmse:0.19030 validation_1-rmse:0.11531 +[92] validation_0-rmse:0.18998 validation_1-rmse:0.11487 +[93] validation_0-rmse:0.18945 validation_1-rmse:0.11445 +[94] validation_0-rmse:0.18919 validation_1-rmse:0.11395 +[95] validation_0-rmse:0.18862 validation_1-rmse:0.11324 +[96] validation_0-rmse:0.18824 validation_1-rmse:0.11283 +[97] validation_0-rmse:0.18778 validation_1-rmse:0.11225 +[98] validation_0-rmse:0.18755 validation_1-rmse:0.11186 +[99] validation_0-rmse:0.18742 validation_1-rmse:0.11149 +2025-04-29 01:57:02,441 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.98 secs) -------------------- +2025-04-29 01:57:02,442 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:02,968 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains +2025-04-29 01:57:02,968 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1741737600_prediction.feather +2025-04-29 01:57:02,980 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:02,987 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,) +2025-04-29 01:57:02,989 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.568812 +1 0.003044 49.568812 +2 0.000465 49.568812 +3 -0.000380 49.568812 +4 0.002829 49.568812 +2025-04-29 01:57:03,001 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:03,007 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:03,009 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:03,014 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1741737600/cb_sol_1741737600 +2025-04-29 01:57:03,015 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:03,042 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:03,042 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 -------------------- +2025-04-29 01:57:08,138 - datasieve.pipeline - INFO - DI tossed 3 predictions for being too far from training data. +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.25025 validation_1-rmse:0.24842 +[1] validation_0-rmse:0.24557 validation_1-rmse:0.24302 +[2] validation_0-rmse:0.24132 validation_1-rmse:0.23806 +[3] validation_0-rmse:0.23733 validation_1-rmse:0.23332 +[4] validation_0-rmse:0.23376 validation_1-rmse:0.22859 +[5] validation_0-rmse:0.23049 validation_1-rmse:0.22444 +[6] validation_0-rmse:0.22702 validation_1-rmse:0.22025 +[7] validation_0-rmse:0.22380 validation_1-rmse:0.21631 +[8] validation_0-rmse:0.22066 validation_1-rmse:0.21250 +[9] validation_0-rmse:0.21772 validation_1-rmse:0.20892 +[10] validation_0-rmse:0.21473 validation_1-rmse:0.20536 +[11] validation_0-rmse:0.21219 validation_1-rmse:0.20211 +[12] validation_0-rmse:0.20968 validation_1-rmse:0.19897 +[13] validation_0-rmse:0.20730 validation_1-rmse:0.19597 +[14] validation_0-rmse:0.20552 validation_1-rmse:0.19281 +[15] validation_0-rmse:0.20361 validation_1-rmse:0.18992 +[16] validation_0-rmse:0.20165 validation_1-rmse:0.18721 +[17] validation_0-rmse:0.19948 validation_1-rmse:0.18463 +[18] validation_0-rmse:0.19807 validation_1-rmse:0.18193 +[19] validation_0-rmse:0.19703 validation_1-rmse:0.17939 +[20] validation_0-rmse:0.19569 validation_1-rmse:0.17683 +[21] validation_0-rmse:0.19388 validation_1-rmse:0.17462 +[22] validation_0-rmse:0.19219 validation_1-rmse:0.17240 +[23] validation_0-rmse:0.19062 validation_1-rmse:0.17026 +[24] validation_0-rmse:0.18933 validation_1-rmse:0.16813 +[25] validation_0-rmse:0.18829 validation_1-rmse:0.16598 +[26] validation_0-rmse:0.18704 validation_1-rmse:0.16411 +[27] validation_0-rmse:0.18563 validation_1-rmse:0.16221 +[28] validation_0-rmse:0.18446 validation_1-rmse:0.16034 +[29] validation_0-rmse:0.18316 validation_1-rmse:0.15841 +[30] validation_0-rmse:0.18192 validation_1-rmse:0.15674 +[31] validation_0-rmse:0.18091 validation_1-rmse:0.15479 +[32] validation_0-rmse:0.18003 validation_1-rmse:0.15312 +[33] validation_0-rmse:0.17886 validation_1-rmse:0.15150 +[34] validation_0-rmse:0.17786 validation_1-rmse:0.14997 +[35] validation_0-rmse:0.17692 validation_1-rmse:0.14855 +[36] validation_0-rmse:0.17613 validation_1-rmse:0.14709 +[37] validation_0-rmse:0.17547 validation_1-rmse:0.14549 +[38] validation_0-rmse:0.17467 validation_1-rmse:0.14404 +[39] validation_0-rmse:0.17393 validation_1-rmse:0.14267 +[40] validation_0-rmse:0.17348 validation_1-rmse:0.14118 +[41] validation_0-rmse:0.17258 validation_1-rmse:0.13993 +[42] validation_0-rmse:0.17168 validation_1-rmse:0.13871 +[43] validation_0-rmse:0.17077 validation_1-rmse:0.13757 +[44] validation_0-rmse:0.17015 validation_1-rmse:0.13621 +[45] validation_0-rmse:0.16924 validation_1-rmse:0.13509 +[46] validation_0-rmse:0.16833 validation_1-rmse:0.13401 +[47] validation_0-rmse:0.16756 validation_1-rmse:0.13297 +[48] validation_0-rmse:0.16717 validation_1-rmse:0.13198 +[49] validation_0-rmse:0.16664 validation_1-rmse:0.13081 +[50] validation_0-rmse:0.16615 validation_1-rmse:0.12979 +[51] validation_0-rmse:0.16541 validation_1-rmse:0.12879 +[52] validation_0-rmse:0.16478 validation_1-rmse:0.12767 +[53] validation_0-rmse:0.16408 validation_1-rmse:0.12675 +[54] validation_0-rmse:0.16363 validation_1-rmse:0.12571 +[55] validation_0-rmse:0.16320 validation_1-rmse:0.12485 +[56] validation_0-rmse:0.16253 validation_1-rmse:0.12398 +[57] validation_0-rmse:0.16192 validation_1-rmse:0.12307 +[58] validation_0-rmse:0.16149 validation_1-rmse:0.12229 +[59] validation_0-rmse:0.16137 validation_1-rmse:0.12128 +[60] validation_0-rmse:0.16117 validation_1-rmse:0.12045 +[61] validation_0-rmse:0.16064 validation_1-rmse:0.11966 +[62] validation_0-rmse:0.16050 validation_1-rmse:0.11890 +[63] validation_0-rmse:0.16003 validation_1-rmse:0.11809 +[64] validation_0-rmse:0.15969 validation_1-rmse:0.11739 +[65] validation_0-rmse:0.15922 validation_1-rmse:0.11661 +[66] validation_0-rmse:0.15868 validation_1-rmse:0.11577 +[67] validation_0-rmse:0.15830 validation_1-rmse:0.11509 +[68] validation_0-rmse:0.15789 validation_1-rmse:0.11446 +[69] validation_0-rmse:0.15733 validation_1-rmse:0.11372 +[70] validation_0-rmse:0.15694 validation_1-rmse:0.11307 +[71] validation_0-rmse:0.15692 validation_1-rmse:0.11224 +[72] validation_0-rmse:0.15659 validation_1-rmse:0.11166 +[73] validation_0-rmse:0.15634 validation_1-rmse:0.11111 +[74] validation_0-rmse:0.15595 validation_1-rmse:0.11056 +[75] validation_0-rmse:0.15579 validation_1-rmse:0.10985 +[76] validation_0-rmse:0.15543 validation_1-rmse:0.10903 +[77] validation_0-rmse:0.15500 validation_1-rmse:0.10848 +[78] validation_0-rmse:0.15499 validation_1-rmse:0.10778 +[79] validation_0-rmse:0.15471 validation_1-rmse:0.10721 +[80] validation_0-rmse:0.15442 validation_1-rmse:0.10666 +[81] validation_0-rmse:0.15416 validation_1-rmse:0.10608 +[82] validation_0-rmse:0.15388 validation_1-rmse:0.10560 +[83] validation_0-rmse:0.15368 validation_1-rmse:0.10498 +[84] validation_0-rmse:0.15346 validation_1-rmse:0.10449 +[85] validation_0-rmse:0.15329 validation_1-rmse:0.10392 +[86] validation_0-rmse:0.15302 validation_1-rmse:0.10347 +[87] validation_0-rmse:0.15270 validation_1-rmse:0.10303 +[88] validation_0-rmse:0.15259 validation_1-rmse:0.10258 +[89] validation_0-rmse:0.15269 validation_1-rmse:0.10204 +[90] validation_0-rmse:0.15239 validation_1-rmse:0.10159 +[91] validation_0-rmse:0.15204 validation_1-rmse:0.10116 +[92] validation_0-rmse:0.15175 validation_1-rmse:0.10070 +[93] validation_0-rmse:0.15167 validation_1-rmse:0.10017 +[94] validation_0-rmse:0.15154 validation_1-rmse:0.09982 +[95] validation_0-rmse:0.15122 validation_1-rmse:0.09932 +[96] validation_0-rmse:0.15119 validation_1-rmse:0.09880 +[97] validation_0-rmse:0.15112 validation_1-rmse:0.09842 +[98] validation_0-rmse:0.15095 validation_1-rmse:0.09807 +[99] validation_0-rmse:0.15075 validation_1-rmse:0.09770 +2025-04-29 01:57:09,614 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:09,615 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:10,150 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains +2025-04-29 01:57:10,151 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1742601600_prediction.feather +2025-04-29 01:57:10,159 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,167 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,) +2025-04-29 01:57:10,168 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.623338 +1 0.003044 49.623338 +2 0.000465 49.623338 +3 -0.000380 49.623338 +4 0.002829 49.623338 +2025-04-29 01:57:10,181 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:10,188 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:10,190 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:10,195 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1742601600/cb_sol_1742601600 +2025-04-29 01:57:10,196 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:10,218 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:10,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 -------------------- +2025-04-29 01:57:15,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:15,186 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.24126 validation_1-rmse:0.24985 +[1] validation_0-rmse:0.23710 validation_1-rmse:0.24457 +[2] validation_0-rmse:0.23323 validation_1-rmse:0.23968 +[3] validation_0-rmse:0.22960 validation_1-rmse:0.23491 +[4] validation_0-rmse:0.22600 validation_1-rmse:0.23038 +[5] validation_0-rmse:0.22255 validation_1-rmse:0.22616 +[6] validation_0-rmse:0.21946 validation_1-rmse:0.22213 +[7] validation_0-rmse:0.21640 validation_1-rmse:0.21827 +[8] validation_0-rmse:0.21381 validation_1-rmse:0.21453 +[9] validation_0-rmse:0.21110 validation_1-rmse:0.21092 +[10] validation_0-rmse:0.20854 validation_1-rmse:0.20742 +[11] validation_0-rmse:0.20586 validation_1-rmse:0.20418 +[12] validation_0-rmse:0.20373 validation_1-rmse:0.20097 +[13] validation_0-rmse:0.20156 validation_1-rmse:0.19779 +[14] validation_0-rmse:0.19934 validation_1-rmse:0.19493 +[15] validation_0-rmse:0.19739 validation_1-rmse:0.19211 +[16] validation_0-rmse:0.19537 validation_1-rmse:0.18929 +[17] validation_0-rmse:0.19333 validation_1-rmse:0.18671 +[18] validation_0-rmse:0.19163 validation_1-rmse:0.18396 +[19] validation_0-rmse:0.18975 validation_1-rmse:0.18157 +[20] validation_0-rmse:0.18799 validation_1-rmse:0.17903 +[21] validation_0-rmse:0.18612 validation_1-rmse:0.17673 +[22] validation_0-rmse:0.18451 validation_1-rmse:0.17454 +[23] validation_0-rmse:0.18299 validation_1-rmse:0.17225 +[24] validation_0-rmse:0.18150 validation_1-rmse:0.17025 +[25] validation_0-rmse:0.18016 validation_1-rmse:0.16803 +[26] validation_0-rmse:0.17866 validation_1-rmse:0.16614 +[27] validation_0-rmse:0.17732 validation_1-rmse:0.16429 +[28] validation_0-rmse:0.17619 validation_1-rmse:0.16247 +[29] validation_0-rmse:0.17494 validation_1-rmse:0.16080 +[30] validation_0-rmse:0.17391 validation_1-rmse:0.15889 +[31] validation_0-rmse:0.17282 validation_1-rmse:0.15724 +[32] validation_0-rmse:0.17156 validation_1-rmse:0.15548 +[33] validation_0-rmse:0.17054 validation_1-rmse:0.15393 +[34] validation_0-rmse:0.16943 validation_1-rmse:0.15244 +[35] validation_0-rmse:0.16841 validation_1-rmse:0.15088 +[36] validation_0-rmse:0.16736 validation_1-rmse:0.14950 +[37] validation_0-rmse:0.16647 validation_1-rmse:0.14797 +[38] validation_0-rmse:0.16544 validation_1-rmse:0.14641 +[39] validation_0-rmse:0.16454 validation_1-rmse:0.14508 +[40] validation_0-rmse:0.16357 validation_1-rmse:0.14380 +[41] validation_0-rmse:0.16266 validation_1-rmse:0.14261 +[42] validation_0-rmse:0.16198 validation_1-rmse:0.14134 +[43] validation_0-rmse:0.16105 validation_1-rmse:0.14019 +[44] validation_0-rmse:0.16046 validation_1-rmse:0.13896 +[45] validation_0-rmse:0.15963 validation_1-rmse:0.13773 +[46] validation_0-rmse:0.15899 validation_1-rmse:0.13662 +[47] validation_0-rmse:0.15822 validation_1-rmse:0.13555 +[48] validation_0-rmse:0.15757 validation_1-rmse:0.13452 +[49] validation_0-rmse:0.15688 validation_1-rmse:0.13322 +[50] validation_0-rmse:0.15627 validation_1-rmse:0.13206 +[51] validation_0-rmse:0.15558 validation_1-rmse:0.13110 +[52] validation_0-rmse:0.15493 validation_1-rmse:0.13017 +[53] validation_0-rmse:0.15429 validation_1-rmse:0.12924 +[54] validation_0-rmse:0.15365 validation_1-rmse:0.12838 +[55] validation_0-rmse:0.15303 validation_1-rmse:0.12741 +[56] validation_0-rmse:0.15258 validation_1-rmse:0.12653 +[57] validation_0-rmse:0.15202 validation_1-rmse:0.12569 +[58] validation_0-rmse:0.15142 validation_1-rmse:0.12478 +[59] validation_0-rmse:0.15106 validation_1-rmse:0.12392 +[60] validation_0-rmse:0.15049 validation_1-rmse:0.12297 +[61] validation_0-rmse:0.14990 validation_1-rmse:0.12223 +[62] validation_0-rmse:0.14932 validation_1-rmse:0.12144 +[63] validation_0-rmse:0.14876 validation_1-rmse:0.12071 +[64] validation_0-rmse:0.14826 validation_1-rmse:0.12000 +[65] validation_0-rmse:0.14788 validation_1-rmse:0.11931 +[66] validation_0-rmse:0.14753 validation_1-rmse:0.11842 +[67] validation_0-rmse:0.14714 validation_1-rmse:0.11776 +[68] validation_0-rmse:0.14665 validation_1-rmse:0.11706 +[69] validation_0-rmse:0.14655 validation_1-rmse:0.11614 +[70] validation_0-rmse:0.14616 validation_1-rmse:0.11556 +[71] validation_0-rmse:0.14579 validation_1-rmse:0.11478 +[72] validation_0-rmse:0.14533 validation_1-rmse:0.11418 +[73] validation_0-rmse:0.14491 validation_1-rmse:0.11358 +[74] validation_0-rmse:0.14448 validation_1-rmse:0.11300 +[75] validation_0-rmse:0.14446 validation_1-rmse:0.11235 +[76] validation_0-rmse:0.14414 validation_1-rmse:0.11173 +[77] validation_0-rmse:0.14371 validation_1-rmse:0.11116 +[78] validation_0-rmse:0.14344 validation_1-rmse:0.11066 +[79] validation_0-rmse:0.14321 validation_1-rmse:0.10996 +[80] validation_0-rmse:0.14280 validation_1-rmse:0.10942 +[81] validation_0-rmse:0.14250 validation_1-rmse:0.10885 +[82] validation_0-rmse:0.14222 validation_1-rmse:0.10837 +[83] validation_0-rmse:0.14184 validation_1-rmse:0.10787 +[84] validation_0-rmse:0.14140 validation_1-rmse:0.10731 +[85] validation_0-rmse:0.14114 validation_1-rmse:0.10683 +[86] validation_0-rmse:0.14100 validation_1-rmse:0.10625 +[87] validation_0-rmse:0.14077 validation_1-rmse:0.10574 +[88] validation_0-rmse:0.14048 validation_1-rmse:0.10534 +[89] validation_0-rmse:0.14010 validation_1-rmse:0.10485 +[90] validation_0-rmse:0.13990 validation_1-rmse:0.10443 +[91] validation_0-rmse:0.13956 validation_1-rmse:0.10400 +[92] validation_0-rmse:0.13949 validation_1-rmse:0.10341 +[93] validation_0-rmse:0.13930 validation_1-rmse:0.10298 +[94] validation_0-rmse:0.13905 validation_1-rmse:0.10254 +[95] validation_0-rmse:0.13884 validation_1-rmse:0.10211 +[96] validation_0-rmse:0.13867 validation_1-rmse:0.10167 +[97] validation_0-rmse:0.13859 validation_1-rmse:0.10114 +[98] validation_0-rmse:0.13839 validation_1-rmse:0.10078 +[99] validation_0-rmse:0.13818 validation_1-rmse:0.10038 +2025-04-29 01:57:16,538 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.34 secs) -------------------- +2025-04-29 01:57:16,539 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:17,068 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains +2025-04-29 01:57:17,069 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather +2025-04-29 01:57:17,084 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,092 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,) +2025-04-29 01:57:17,094 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.644115 +1 0.003044 49.644115 +2 0.000465 49.644115 +3 -0.000380 49.644115 +4 0.002829 49.644115 +2025-04-29 01:57:17,108 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:17,115 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:17,117 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:17,124 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600 +2025-04-29 01:57:17,125 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:17,151 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:17,151 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 -------------------- +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.23717 validation_1-rmse:0.24954 +[1] validation_0-rmse:0.23311 validation_1-rmse:0.24432 +[2] validation_0-rmse:0.22894 validation_1-rmse:0.23928 +[3] validation_0-rmse:0.22483 validation_1-rmse:0.23452 +[4] validation_0-rmse:0.22141 validation_1-rmse:0.23005 +[5] validation_0-rmse:0.21750 validation_1-rmse:0.22575 +[6] validation_0-rmse:0.21419 validation_1-rmse:0.22161 +[7] validation_0-rmse:0.21074 validation_1-rmse:0.21764 +[8] validation_0-rmse:0.20776 validation_1-rmse:0.21374 +[9] validation_0-rmse:0.20479 validation_1-rmse:0.21020 +[10] validation_0-rmse:0.20193 validation_1-rmse:0.20664 +[11] validation_0-rmse:0.19915 validation_1-rmse:0.20326 +[12] validation_0-rmse:0.19683 validation_1-rmse:0.20013 +[13] validation_0-rmse:0.19459 validation_1-rmse:0.19705 +[14] validation_0-rmse:0.19243 validation_1-rmse:0.19415 +[15] validation_0-rmse:0.19013 validation_1-rmse:0.19115 +[16] validation_0-rmse:0.18828 validation_1-rmse:0.18836 +[17] validation_0-rmse:0.18621 validation_1-rmse:0.18557 +[18] validation_0-rmse:0.18402 validation_1-rmse:0.18302 +[19] validation_0-rmse:0.18198 validation_1-rmse:0.18050 +[20] validation_0-rmse:0.18015 validation_1-rmse:0.17803 +[21] validation_0-rmse:0.17857 validation_1-rmse:0.17575 +[22] validation_0-rmse:0.17681 validation_1-rmse:0.17350 +[23] validation_0-rmse:0.17537 validation_1-rmse:0.17132 +[24] validation_0-rmse:0.17377 validation_1-rmse:0.16919 +[25] validation_0-rmse:0.17225 validation_1-rmse:0.16720 +[26] validation_0-rmse:0.17072 validation_1-rmse:0.16529 +[27] validation_0-rmse:0.16931 validation_1-rmse:0.16310 +[28] validation_0-rmse:0.16784 validation_1-rmse:0.16126 +[29] validation_0-rmse:0.16650 validation_1-rmse:0.15940 +[30] validation_0-rmse:0.16512 validation_1-rmse:0.15771 +[31] validation_0-rmse:0.16392 validation_1-rmse:0.15605 +[32] validation_0-rmse:0.16287 validation_1-rmse:0.15428 +[33] validation_0-rmse:0.16159 validation_1-rmse:0.15277 +[34] validation_0-rmse:0.16033 validation_1-rmse:0.15125 +[35] validation_0-rmse:0.15910 validation_1-rmse:0.14974 +[36] validation_0-rmse:0.15821 validation_1-rmse:0.14832 +[37] validation_0-rmse:0.15733 validation_1-rmse:0.14664 +[38] validation_0-rmse:0.15624 validation_1-rmse:0.14525 +[39] validation_0-rmse:0.15518 validation_1-rmse:0.14395 +[40] validation_0-rmse:0.15451 validation_1-rmse:0.14267 +[41] validation_0-rmse:0.15396 validation_1-rmse:0.14127 +[42] validation_0-rmse:0.15309 validation_1-rmse:0.14006 +[43] validation_0-rmse:0.15219 validation_1-rmse:0.13890 +[44] validation_0-rmse:0.15156 validation_1-rmse:0.13749 +[45] validation_0-rmse:0.15061 validation_1-rmse:0.13637 +[46] validation_0-rmse:0.14982 validation_1-rmse:0.13528 +[47] validation_0-rmse:0.14918 validation_1-rmse:0.13414 +[48] validation_0-rmse:0.14840 validation_1-rmse:0.13312 +[49] validation_0-rmse:0.14802 validation_1-rmse:0.13212 +[50] validation_0-rmse:0.14738 validation_1-rmse:0.13089 +[51] validation_0-rmse:0.14671 validation_1-rmse:0.12994 +[52] validation_0-rmse:0.14604 validation_1-rmse:0.12894 +[53] validation_0-rmse:0.14534 validation_1-rmse:0.12802 +[54] validation_0-rmse:0.14464 validation_1-rmse:0.12718 +[55] validation_0-rmse:0.14423 validation_1-rmse:0.12625 +[56] validation_0-rmse:0.14371 validation_1-rmse:0.12531 +[57] validation_0-rmse:0.14321 validation_1-rmse:0.12446 +[58] validation_0-rmse:0.14279 validation_1-rmse:0.12346 +[59] validation_0-rmse:0.14234 validation_1-rmse:0.12257 +[60] validation_0-rmse:0.14194 validation_1-rmse:0.12181 +[61] validation_0-rmse:0.14176 validation_1-rmse:0.12077 +[62] validation_0-rmse:0.14120 validation_1-rmse:0.12003 +[63] validation_0-rmse:0.14073 validation_1-rmse:0.11932 +[64] validation_0-rmse:0.14023 validation_1-rmse:0.11862 +[65] validation_0-rmse:0.14001 validation_1-rmse:0.11791 +[66] validation_0-rmse:0.13966 validation_1-rmse:0.11720 +[67] validation_0-rmse:0.13920 validation_1-rmse:0.11644 +[68] validation_0-rmse:0.13872 validation_1-rmse:0.11560 +[69] validation_0-rmse:0.13831 validation_1-rmse:0.11494 +[70] validation_0-rmse:0.13808 validation_1-rmse:0.11425 +[71] validation_0-rmse:0.13762 validation_1-rmse:0.11348 +[72] validation_0-rmse:0.13725 validation_1-rmse:0.11284 +[73] validation_0-rmse:0.13681 validation_1-rmse:0.11225 +[74] validation_0-rmse:0.13629 validation_1-rmse:0.11165 +[75] validation_0-rmse:0.13595 validation_1-rmse:0.11109 +[76] validation_0-rmse:0.13585 validation_1-rmse:0.11023 +[77] validation_0-rmse:0.13541 validation_1-rmse:0.10972 +[78] validation_0-rmse:0.13505 validation_1-rmse:0.10920 +[79] validation_0-rmse:0.13465 validation_1-rmse:0.10861 +[80] validation_0-rmse:0.13433 validation_1-rmse:0.10810 +[81] validation_0-rmse:0.13409 validation_1-rmse:0.10744 +[82] validation_0-rmse:0.13377 validation_1-rmse:0.10695 +[83] validation_0-rmse:0.13353 validation_1-rmse:0.10641 +[84] validation_0-rmse:0.13337 validation_1-rmse:0.10588 +[85] validation_0-rmse:0.13329 validation_1-rmse:0.10533 +[86] validation_0-rmse:0.13296 validation_1-rmse:0.10488 +[87] validation_0-rmse:0.13264 validation_1-rmse:0.10442 +[88] validation_0-rmse:0.13247 validation_1-rmse:0.10394 +[89] validation_0-rmse:0.13216 validation_1-rmse:0.10351 +[90] validation_0-rmse:0.13188 validation_1-rmse:0.10297 +[91] validation_0-rmse:0.13145 validation_1-rmse:0.10203 +[92] validation_0-rmse:0.13122 validation_1-rmse:0.10157 +[93] validation_0-rmse:0.13102 validation_1-rmse:0.10118 +[94] validation_0-rmse:0.13060 validation_1-rmse:0.10033 +[95] validation_0-rmse:0.13033 validation_1-rmse:0.09981 +[96] validation_0-rmse:0.13016 validation_1-rmse:0.09933 +[97] validation_0-rmse:0.12995 validation_1-rmse:0.09894 +[98] validation_0-rmse:0.12972 validation_1-rmse:0.09860 +[99] validation_0-rmse:0.12954 validation_1-rmse:0.09825 +2025-04-29 01:57:23,725 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) -------------------- +2025-04-29 01:57:23,726 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:24,305 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains +2025-04-29 01:57:24,305 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at +/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather +2025-04-29 01:57:24,318 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,325 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,) +2025-04-29 01:57:24,327 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.601082 +1 0.003044 49.601082 +2 0.000465 49.601082 +3 -0.000380 49.601082 +4 0.002829 49.601082 +2025-04-29 01:57:24,337 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT +2025-04-29 01:57:24,345 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,) +2025-04-29 01:57:24,346 - FreqaiExampleStrategy - INFO - 目标列预览： + up_or_down &-buy_rsi +0 0.002704 49.729824 +1 0.003044 49.729824 +2 0.000465 49.729824 +3 -0.000380 49.729824 +4 0.002829 49.729824 +2025-04-29 01:57:24,352 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600 +2025-04-29 01:57:24,353 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT -------------------- +2025-04-29 01:57:24,376 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400. +2025-04-29 01:57:24,376 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 -------------------- +2025-04-29 01:57:29,392 - datasieve.pipeline - INFO - DI tossed 1948 predictions for being too far from training data. +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features +2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points +[0] validation_0-rmse:0.30616 validation_1-rmse:0.27906 +[1] validation_0-rmse:0.30021 validation_1-rmse:0.27322 +[2] validation_0-rmse:0.29443 validation_1-rmse:0.26757 +[3] validation_0-rmse:0.28911 validation_1-rmse:0.26205 +[4] validation_0-rmse:0.28365 validation_1-rmse:0.25699 +[5] validation_0-rmse:0.27823 validation_1-rmse:0.25219 +[6] validation_0-rmse:0.27295 validation_1-rmse:0.24748 +[7] validation_0-rmse:0.26797 validation_1-rmse:0.24295 +[8] validation_0-rmse:0.26320 validation_1-rmse:0.23854 +[9] validation_0-rmse:0.25898 validation_1-rmse:0.23437 +[10] validation_0-rmse:0.25517 validation_1-rmse:0.23021 +[11] validation_0-rmse:0.25113 validation_1-rmse:0.22639 +[12] validation_0-rmse:0.24762 validation_1-rmse:0.22270 +[13] validation_0-rmse:0.24393 validation_1-rmse:0.21915 +[14] validation_0-rmse:0.24169 validation_1-rmse:0.21579 +[15] validation_0-rmse:0.23898 validation_1-rmse:0.21236 +[16] validation_0-rmse:0.23539 validation_1-rmse:0.20924 +[17] validation_0-rmse:0.23364 validation_1-rmse:0.20621 +[18] validation_0-rmse:0.23062 validation_1-rmse:0.20322 +[19] validation_0-rmse:0.22764 validation_1-rmse:0.20024 +[20] validation_0-rmse:0.22488 validation_1-rmse:0.19731 +[21] validation_0-rmse:0.22211 validation_1-rmse:0.19445 +[22] validation_0-rmse:0.21952 validation_1-rmse:0.19188 +[23] validation_0-rmse:0.21699 validation_1-rmse:0.18935 +[24] validation_0-rmse:0.21549 validation_1-rmse:0.18686 +[25] validation_0-rmse:0.21310 validation_1-rmse:0.18454 +[26] validation_0-rmse:0.21118 validation_1-rmse:0.18198 +[27] validation_0-rmse:0.20904 validation_1-rmse:0.17979 +[28] validation_0-rmse:0.20726 validation_1-rmse:0.17755 +[29] validation_0-rmse:0.20511 validation_1-rmse:0.17547 +[30] validation_0-rmse:0.20336 validation_1-rmse:0.17335 +[31] validation_0-rmse:0.20172 validation_1-rmse:0.17144 +[32] validation_0-rmse:0.19983 validation_1-rmse:0.16961 +[33] validation_0-rmse:0.19794 validation_1-rmse:0.16759 +[34] validation_0-rmse:0.19658 validation_1-rmse:0.16581 +[35] validation_0-rmse:0.19492 validation_1-rmse:0.16409 +[36] validation_0-rmse:0.19347 validation_1-rmse:0.16229 +[37] validation_0-rmse:0.19225 validation_1-rmse:0.16064 +[38] validation_0-rmse:0.19083 validation_1-rmse:0.15877 +[39] validation_0-rmse:0.18921 validation_1-rmse:0.15720 +[40] validation_0-rmse:0.18766 validation_1-rmse:0.15572 +[41] validation_0-rmse:0.18652 validation_1-rmse:0.15414 +[42] validation_0-rmse:0.18519 validation_1-rmse:0.15277 +[43] validation_0-rmse:0.18396 validation_1-rmse:0.15125 +[44] validation_0-rmse:0.18264 validation_1-rmse:0.14968 +[45] validation_0-rmse:0.18134 validation_1-rmse:0.14841 +[46] validation_0-rmse:0.18026 validation_1-rmse:0.14717 +[47] validation_0-rmse:0.17900 validation_1-rmse:0.14594 +[48] validation_0-rmse:0.17815 validation_1-rmse:0.14460 +[49] validation_0-rmse:0.17713 validation_1-rmse:0.14344 +[50] validation_0-rmse:0.17609 validation_1-rmse:0.14232 +[51] validation_0-rmse:0.17502 validation_1-rmse:0.14112 +[52] validation_0-rmse:0.17414 validation_1-rmse:0.13991 +[53] validation_0-rmse:0.17317 validation_1-rmse:0.13889 +[54] validation_0-rmse:0.17267 validation_1-rmse:0.13770 +[55] validation_0-rmse:0.17175 validation_1-rmse:0.13665 +[56] validation_0-rmse:0.17087 validation_1-rmse:0.13573 +[57] validation_0-rmse:0.17001 validation_1-rmse:0.13483 +[58] validation_0-rmse:0.16920 validation_1-rmse:0.13384 +[59] validation_0-rmse:0.16869 validation_1-rmse:0.13280 +[60] validation_0-rmse:0.16790 validation_1-rmse:0.13189 +[61] validation_0-rmse:0.16689 validation_1-rmse:0.13093 +[62] validation_0-rmse:0.16600 validation_1-rmse:0.13007 +[63] validation_0-rmse:0.16548 validation_1-rmse:0.12921 +[64] validation_0-rmse:0.16482 validation_1-rmse:0.12837 +[65] validation_0-rmse:0.16397 validation_1-rmse:0.12747 +[66] validation_0-rmse:0.16316 validation_1-rmse:0.12669 +[67] validation_0-rmse:0.16267 validation_1-rmse:0.12587 +[68] validation_0-rmse:0.16204 validation_1-rmse:0.12501 +[69] validation_0-rmse:0.16159 validation_1-rmse:0.12422 +[70] validation_0-rmse:0.16090 validation_1-rmse:0.12354 +[71] validation_0-rmse:0.16026 validation_1-rmse:0.12282 +[72] validation_0-rmse:0.15986 validation_1-rmse:0.12206 +[73] validation_0-rmse:0.15919 validation_1-rmse:0.12129 +[74] validation_0-rmse:0.15875 validation_1-rmse:0.12061 +[75] validation_0-rmse:0.15829 validation_1-rmse:0.11966 +[76] validation_0-rmse:0.15790 validation_1-rmse:0.11864 +[77] validation_0-rmse:0.15732 validation_1-rmse:0.11802 +[78] validation_0-rmse:0.15696 validation_1-rmse:0.11739 +[79] validation_0-rmse:0.15615 validation_1-rmse:0.11660 +[80] validation_0-rmse:0.15556 validation_1-rmse:0.11593 +[81] validation_0-rmse:0.15516 validation_1-rmse:0.11531 +[82] validation_0-rmse:0.15466 validation_1-rmse:0.11437 +[83] validation_0-rmse:0.15422 validation_1-rmse:0.11383 +[84] validation_0-rmse:0.15382 validation_1-rmse:0.11332 +[85] validation_0-rmse:0.15350 validation_1-rmse:0.11244 +[86] validation_0-rmse:0.15310 validation_1-rmse:0.11180 +[87] validation_0-rmse:0.15277 validation_1-rmse:0.11119 +[88] validation_0-rmse:0.15228 validation_1-rmse:0.11060 +[89] validation_0-rmse:0.15192 validation_1-rmse:0.11011 +[90] validation_0-rmse:0.15144 validation_1-rmse:0.10956 +[91] validation_0-rmse:0.15092 validation_1-rmse:0.10913 +[92] validation_0-rmse:0.15058 validation_1-rmse:0.10847 +[93] validation_0-rmse:0.15017 validation_1-rmse:0.10803 +[94] validation_0-rmse:0.14984 validation_1-rmse:0.10702 +[95] validation_0-rmse:0.14967 validation_1-rmse:0.10629 +[96] validation_0-rmse:0.14914 validation_1-rmse:0.10587 +[97] validation_0-rmse:0.14882 validation_1-rmse:0.10545 +[98] validation_0-rmse:0.14853 validation_1-rmse:0.10454 +[99] validation_0-rmse:0.14837 validation_1-rmse:0.10398 +2025-04-29 01:57:30,474 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.12 secs) -------------------- +2025-04-29 01:57:30,475 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk. +2025-04-29 01:57:31,077 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05 +2025-04-29 01:57:31,096 - FreqaiExampleStrategy - INFO - up_or_down 值统计： +up_or_down +0 33825 +1 32946 +2025-04-29 01:57:31,097 - FreqaiExampleStrategy - INFO - do_predict 值统计： +do_predict +0.0 36730 +1.0 30041 +2025-04-29 01:57:31,105 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-01-01 00:00:00 up to 2025-04-20 00:00:00 (109 days). +2025-04-29 01:57:31,109 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,111 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:31,193 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。 +2025-04-29 01:57:31,195 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。 +2025-04-29 01:57:33,776 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_01-57-33.meta.json" +Result for strategy FreqaiExampleStrategy + BACKTESTING REPORT +┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ BTC/USDT │ 38 │ -0.39 │ -22.029 │ -2.2 │ 22:13:00 │ 5 32 1 13.2 │ +│ SOL/USDT │ 44 │ -1.94 │ -128.236 │ -12.82 │ 16:35:00 │ 12 26 6 27.3 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + LEFT OPEN TRADES REPORT +┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │ +└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + ENTER TAG STATS +┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ long │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘ + EXIT REASON STATS +┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ roi │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ trailing_stop_loss │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + MIXED TAG STATS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ('long', 'roi') │ │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │ +│ ('long', 'trailing_stop_loss') │ │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │ +│ TOTAL │ │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ +└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘ + SUMMARY METRICS +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Metric ┃ Value ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ Backtesting from │ 2025-01-01 00:00:00 │ +│ Backtesting to │ 2025-04-20 00:00:00 │ +│ Trading Mode │ Spot │ +│ Max open trades │ 2 │ +│ │ │ +│ Total/Daily Avg Trades │ 82 / 0.75 │ +│ Starting balance │ 1000 USDT │ +│ Final balance │ 849.735 USDT │ +│ Absolute profit │ -150.265 USDT │ +│ Total profit % │ -15.03% │ +│ CAGR % │ -42.03% │ +│ Sortino │ -252.56 │ +│ Sharpe │ -4.15 │ +│ Calmar │ -17.48 │ +│ SQN │ -2.60 │ +│ Profit factor │ 0.05 │ +│ Expectancy (Ratio) │ -1.83 (-0.79) │ +│ Avg. daily profit % │ -0.14% │ +│ Avg. stake amount │ 150 USDT │ +│ Total trade volume │ 24523.15 USDT │ +│ │ │ +│ Best Pair │ BTC/USDT -2.20% │ +│ Worst Pair │ SOL/USDT -12.82% │ +│ Best trade │ SOL/USDT 0.90% │ +│ Worst trade │ SOL/USDT -15.19% │ +│ Best day │ 1.76 USDT │ +│ Worst day │ -22.827 USDT │ +│ Days win/draw/lose │ 14 / 80 / 7 │ +│ Avg. Duration Winners │ 0:55:00 │ +│ Avg. Duration Loser │ 2 days, 18:13:00 │ +│ Max Consecutive Wins / Loss │ 2 / 16 │ +│ Rejected Entry signals │ 0 │ +│ Entry/Exit Timeouts │ 0 / 0 │ +│ │ │ +│ Min balance │ 849.735 USDT │ +│ Max balance │ 1000.508 USDT │ +│ Max % of account underwater │ 15.07% │ +│ Absolute Drawdown (Account) │ 15.07% │ +│ Absolute Drawdown │ 150.773 USDT │ +│ Drawdown high │ 0.508 USDT │ +│ Drawdown low │ -150.265 USDT │ +│ Drawdown Start │ 2025-01-06 19:48:00 │ +│ Drawdown End │ 2025-04-06 23:15:00 │ +│ Market change │ -26.79% │ +└─────────────────────────────┴─────────────────────┘ + +Backtested 2025-01-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2 + STRATEGY SUMMARY +┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ +│ FreqaiExampleStrategy │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ 150.773 USDT 15.07% │ +└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴──────────────────────┘