run.sh
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zhangkun9038@dingtalk.com
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e6402d08dd
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6
#### **3. 检查目标变量**
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6
#### **3. 检查目标变量**
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@ -0,0 +1,6 @@
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<<<<<<< HEAD
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=======
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dataframe['target'] = np.where(short_ma > long_ma, 2,
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np.where(short_ma < long_ma, 0, 1))
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>>>>>>> Snippet
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2
.gitignore
vendored
2
.gitignore
vendored
@ -120,3 +120,5 @@ target/
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docker-compose-*.yml
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data/
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!result/
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126
config_examples/basic.json
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126
config_examples/basic.json
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@ -0,0 +1,126 @@
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{
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"$schema": "https://schema.freqtrade.io/schema.json",
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"trading_mode": "spot",
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"margin_mode": "isolated",
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"max_open_trades": 4,
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"stake_currency": "USDT",
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"stake_amount": 150,
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"startup_candle_count": 30,
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"tradable_balance_ratio": 1,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"timeframe": "3m",
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"dry_run_wallet": 1000,
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"cancel_open_orders_on_exit": true,
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"stoploss": -0.05,
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"unfilledtimeout": {
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"entry": 5,
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"exit": 15
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},
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"exchange": {
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"name": "okx",
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"key": "eca767d4-fda5-4a1b-bb28-49ae18093307",
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"secret": "8CA3628A556ED137977DB298D37BC7F3",
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"enable_ws": false,
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"ccxt_config": {
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"enableRateLimit": true,
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"rateLimit": 500,
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"options": {
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"defaultType": "spot"
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}
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},
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"ccxt_async_config": {
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"enableRateLimit": true,
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"rateLimit": 500,
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"timeout": 20000
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},
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"pair_whitelist": [
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"BTC/USDT",
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"SOL/USDT"
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],
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"pair_blacklist": []
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},
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"entry_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1,
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"price_last_balance": 0.0,
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"check_depth_of_market": {
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"enabled": false,
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"bids_to_ask_delta": 1
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}
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},
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"exit_pricing": {
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"price_side": "other",
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"use_order_book": true,
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"order_book_top": 1
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},
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"pairlists": [
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{
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"method": "StaticPairList"
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}
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],
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"freqai": {
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"enabled": true,
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"data_kitchen": {
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"fillna": "ffill",
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"feature_parameters": {
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"DI_threshold": 0.9,
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"weight_factor": 0.9
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}
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},
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"freqaimodel": "XGBoostRegressor",
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"purge_old_models": 2,
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"identifier": "test175",
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"train_period_days": 30,
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"backtest_period_days": 10,
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"live_retrain_hours": 0,
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"feature_selection": {
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"method": "recursive_elimination",
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"threshold": 0.01
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},
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"feature_parameters": {
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"include_timeframes": ["5m", "1h"],
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"include_corr_pairlist": ["BTC/USDT"],
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"label_period_candles": 10,
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"use_SVM_to_remove_outliers": true,
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"principal_component_analysis": true
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},
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"model": "XGBoostRegressor",
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"data_split_parameters": {
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"test_size": 0.2,
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"shuffle": true,
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"random_state": 42
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},
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"model_training_parameters": {
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"n_estimators": 200,
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"learning_rate": 0.05,
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"max_depth": 5,
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"subsample": 0.8,
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"colsample_bytree": 0.8,
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"objective": "reg:squarederror",
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"eval_metric": "rmse",
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"early_stopping_rounds": 50
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}
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},
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"api_server": {
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"enabled": true,
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"listen_ip_address": "0.0.0.0",
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"listen_port": 8080,
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"verbosity": "error",
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"enable_openapi": false,
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"jwt_secret_key": "6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca",
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"ws_token": "6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ",
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"CORS_origins": [],
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"username": "freqAdmin",
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"password": "admin"
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},
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"bot_name": "freqtrade",
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"initial_state": "running",
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"force_entry_enable": false,
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"internals": {
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"process_throttle_secs": 5,
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"heartbeat_interval": 20,
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"loglevel": "DEBUG"
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}
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}
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@ -60,13 +60,14 @@ services:
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# --hyperopt-loss SharpeHyperOptLoss
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# --spaces roi stoploss
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# -e 200
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#--config /freqtrade/templates/FreqaiExampleStrategy.json
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command: >
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backtesting
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--logfile /freqtrade/user_data/logs/freqtrade.log
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--freqaimodel XGBoostRegressor
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--config /freqtrade/config_examples/config_freqai.okx.json
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--config /freqtrade/templates/FreqaiExampleStrategy.json
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--strategy-path /freqtrade/templates
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--strategy FreqaiExampleStrategy
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--timerange 20250401-20250420
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407
e6402d0.diff
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407
e6402d0.diff
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diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
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index 00ff1d2..40a1adc 100644
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--- a/freqtrade/templates/FreqaiExampleStrategy.py
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+++ b/freqtrade/templates/FreqaiExampleStrategy.py
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@@ -9,44 +9,47 @@ from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
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logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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- # 移除硬编码的 minimal_roi 和 stoploss，改为动态适配
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- minimal_roi = {} # 将在 populate_indicators 中动态生成
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- stoploss = 0.0 # 将在 populate_indicators 中动态设置
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+ # 动态适配 minimal_roi 和 stoploss
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+ minimal_roi = {} # populate_indicators 中动态设定
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+ stoploss = -0.15 # 默认固定止损
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trailing_stop = True
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+ trailing_stop_positive = 0.05
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+ trailing_stop_positive_offset = 0.1
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process_only_new_candles = True
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use_exit_signal = True
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startup_candle_count: int = 40
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can_short = False
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- # 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化
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+ # 可训练参数（用于 Hyperopt）
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buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True)
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- # 为 Hyperopt 优化添加 ROI 和 stoploss 参数
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roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
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roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
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roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
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- stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True)
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+ stoploss_param = DecimalParameter(
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+ low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True
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+ )
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# FreqAI 配置
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freqai_info = {
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- "model": "CatboostClassifier", # 与config保持一致
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+ "model": "CatboostClassifier",
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"feature_parameters": {
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- "include_timeframes": ["3m", "15m", "1h"], # 与config一致
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- "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对
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- "label_period_candles": 20, # 与config一致
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- "include_shifted_candles": 2, # 与config一致
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+ "include_timeframes": ["3m", "15m", "1h"],
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+ "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"],
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+ "label_period_candles": 20,
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+ "include_shifted_candles": 2,
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},
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"data_split_parameters": {
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"test_size": 0.2,
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- "shuffle": True, # 启用shuffle
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+ "shuffle": True,
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},
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"model_training_parameters": {
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- "n_estimators": 100, # 减少树的数量
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- "learning_rate": 0.1, # 提高学习率
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- "max_depth": 6, # 限制树深度
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- "subsample": 0.8, # 添加子采样
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- "colsample_bytree": 0.8, # 添加特征采样
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+ "n_estimators": 100,
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+ "learning_rate": 0.1,
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+ "max_depth": 6,
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+ "subsample": 0.8,
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+ "colsample_bytree": 0.8,
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"objective": "reg:squarederror",
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"eval_metric": "rmse",
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"early_stopping_rounds": 20,
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@@ -54,8 +57,8 @@ class FreqaiExampleStrategy(IStrategy):
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},
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"data_kitchen": {
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"feature_parameters": {
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- "DI_threshold": 1.5, # 降低异常值过滤阈值
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- "use_DBSCAN_to_remove_outliers": False # 禁用DBSCAN
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+ "DI_threshold": 1.5,
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+ "use_DBSCAN_to_remove_outliers": False
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}
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}
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}
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@@ -72,265 +75,113 @@ class FreqaiExampleStrategy(IStrategy):
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}
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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- # 保留关键的技术指标
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+ # RSI 计算
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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- # 确保 MACD 列被正确计算并保留
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+ # MACD 计算并容错
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try:
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macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
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dataframe["macd"] = macd["macd"]
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dataframe["macdsignal"] = macd["macdsignal"]
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except Exception as e:
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- logger.error(f"计算 MACD 列时出错：{str(e)}")
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+ logger.error(f"MACD 计算失败: {e}")
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dataframe["macd"] = np.nan
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dataframe["macdsignal"] = np.nan
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- # 检查 MACD 列是否存在
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- if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
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- logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号")
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- raise ValueError("DataFrame 缺少必要的 MACD 列")
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-
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- # 确保 MACD 列存在
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- if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
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- logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号")
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- raise ValueError("DataFrame 缺少必要的 MACD 列")
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-
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- # 保留布林带相关特征
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+ # 布林带
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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- # 保留成交量相关特征
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+ # 成交量均线
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dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
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- # 数据清理
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+ # 清理无穷大值
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for col in dataframe.columns:
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if dataframe[col].dtype in ["float64", "int64"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
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- dataframe[col] = dataframe[col].ffill().fillna(0)
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-
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- logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}")
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+ dataframe[col] = dataframe[col].fillna(0)
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+
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return dataframe
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def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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-# 数据清理逻辑
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+
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+ # 数据清理
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for col in dataframe.columns:
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if dataframe[col].dtype in ["float64", "int64"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
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- dataframe[col] = dataframe[col].ffill()
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- dataframe[col] = dataframe[col].fillna(0)
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-
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- # 检查是否仍有无效值
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- if dataframe[col].isna().any() or np.isinf(dataframe[col]).any():
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- logger.warning(f"列 {col} 仍包含无效值，已填充为默认值")
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- dataframe[col] = dataframe[col].fillna(0)
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- return dataframe
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-
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- def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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- dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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- dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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- dataframe.replace([np.inf, -np.inf], 0, inplace=True)
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- dataframe.ffill(inplace=True)
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- dataframe.fillna(0, inplace=True)
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- return dataframe
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-
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- def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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- logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
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- if "close" not in dataframe.columns:
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- logger.error("数据框缺少必要的 'close' 列")
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- raise ValueError("数据框缺少必要的 'close' 列")
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-
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- try:
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- label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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-
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- # 定义目标变量为未来价格变化百分比（连续值）
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- dataframe["up_or_down"] = (
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- dataframe["close"].shift(-label_period) - dataframe["close"]
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- ) / dataframe["close"]
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-
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- # 数据清理：处理 NaN 和 Inf 值
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- dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan)
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- dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0)
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-
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- # 确保目标变量是二维数组
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- if dataframe["up_or_down"].ndim == 1:
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- dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1)
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-
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- # 检查并处理 NaN 或无限值
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- dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan)
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- dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0)
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-
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- # 生成 %-volatility 特征
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- dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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-
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- # 确保 &-buy_rsi 列的值计算正确
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- dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
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-
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- # 数据清理
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- for col in ["&-buy_rsi", "up_or_down", "%-volatility"]:
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- # 使用直接操作避免链式赋值
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- dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
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- dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill')
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- dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值
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- if dataframe[col].isna().any():
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- logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值")
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-
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- except Exception as e:
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- logger.error(f"创建 FreqAI 目标失败：{str(e)}")
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- raise
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+ dataframe[col] = dataframe[col].ffill().fillna(0)
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- # Log the shape of the target variable for debugging
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- logger.info(f"目标列形状：{dataframe['up_or_down'].shape}")
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- logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}")
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return dataframe
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- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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- logger.info(f"处理交易对：{metadata['pair']}")
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- dataframe = self.freqai.start(dataframe, metadata, self)
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-
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- # 计算传统指标
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- dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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- bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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- dataframe["bb_lowerband"] = bollinger["lower"]
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- dataframe["bb_middleband"] = bollinger["mid"]
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- dataframe["bb_upperband"] = bollinger["upper"]
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- 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
|
||||
888
filtered_output.log
Normal file
888
filtered_output.log
Normal file
@ -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% │
|
||||
└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴──────────────────────┘
|
||||
3066
freqtrade.log
Normal file
3066
freqtrade.log
Normal file
File diff suppressed because it is too large
Load Diff
336
freqtrade/abc.py
Normal file
336
freqtrade/abc.py
Normal file
@ -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
|
||||
295
freqtrade/new.py
Normal file
295
freqtrade/new.py
Normal file
@ -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
|
||||
336
freqtrade/old.py
Normal file
336
freqtrade/old.py
Normal file
@ -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
|
||||
185
freqtrade/templates/MyDynamicStrategy.py
Normal file
185
freqtrade/templates/MyDynamicStrategy.py
Normal file
@ -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
|
||||
|
||||
69
freqtrade/templates/optima.py
Normal file
69
freqtrade/templates/optima.py
Normal file
@ -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
|
||||
|
||||
3066
freqtrade_utf8.log
Normal file
3066
freqtrade_utf8.log
Normal file
File diff suppressed because it is too large
Load Diff
1232
output.log
Normal file
1232
output.log
Normal file
File diff suppressed because it is too large
Load Diff
@ -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[90m - [0m[95mroot[0m[90m - [0m[34mINFO[0m[90m - [0mLogfile configured
|
||||
2025-04-29 07:44:19,657[90m - [0m[95mfreqtrade.loggers[0m[90m - [0m[34mINFO[0m[90m - [0mVerbosity set to 0
|
||||
2025-04-29 07:44:19,657[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mUsing additional Strategy lookup path: /freqtrade/templates
|
||||
2025-04-29 07:44:19,658[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mUsing max_open_trades: 4 ...
|
||||
2025-04-29 07:44:19,658[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mParameter --timerange detected: 20250401-20250420 ...
|
||||
2025-04-29 07:44:19,698[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mUsing user-data directory: /freqtrade/user_data ...
|
||||
2025-04-29 07:44:19,699[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mUsing data directory: /freqtrade/user_data/data/okx ...
|
||||
2025-04-29 07:44:19,699[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mParameter --cache=none detected ...
|
||||
2025-04-29 07:44:19,700[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mFilter trades by timerange: 20250401-20250420
|
||||
2025-04-29 07:44:19,700[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mUsing freqaimodel class name: XGBoostRegressor
|
||||
2025-04-29 07:44:19,701[90m - [0m[95mfreqtrade.exchange.check_exchange[0m[90m - [0m[34mINFO[0m[90m - [0mChecking exchange...
|
||||
2025-04-29 07:44:19,708[90m - [0m[95mfreqtrade.exchange.check_exchange[0m[90m - [0m[34mINFO[0m[90m - [0mExchange "okx" is officially supported by the Freqtrade development team.
|
||||
2025-04-29 07:44:19,708[90m - [0m[95mfreqtrade.configuration.configuration[0m[90m - [0m[34mINFO[0m[90m - [0mUsing pairlist from configuration.
|
||||
2025-04-29 07:44:19,708[90m - [0m[95mfreqtrade.configuration.config_validation[0m[90m - [0m[34mINFO[0m[90m - [0mValidating configuration ...
|
||||
2025-04-29 07:44:19,710[90m - [0m[95mfreqtrade.commands.optimize_commands[0m[90m - [0m[34mINFO[0m[90m - [0mStarting freqtrade in Backtesting mode
|
||||
2025-04-29 07:44:19,711[90m - [0m[95mfreqtrade.exchange.exchange[0m[90m - [0m[34mINFO[0m[90m - [0mInstance is running with dry_run enabled
|
||||
2025-04-29 07:44:19,711[90m - [0m[95mfreqtrade.exchange.exchange[0m[90m - [0m[34mINFO[0m[90m - [0mUsing CCXT 4.4.69
|
||||
2025-04-29 07:44:19,712[90m - [0m[95mfreqtrade.exchange.exchange[0m[90m - [0m[34mINFO[0m[90m - [0mApplying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}}
|
||||
2025-04-29 07:44:19,717[90m - [0m[95mfreqtrade.exchange.exchange[0m[90m - [0m[34mINFO[0m[90m - [0mApplying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000}
|
||||
2025-04-29 07:44:19,722[90m - [0m[95mfreqtrade.exchange.exchange[0m[90m - [0m[34mINFO[0m[90m - [0mUsing Exchange "OKX"
|
||||
2025-04-29 07:44:22,575[90m - [0m[95mfreqtrade.resolvers.exchange_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mUsing resolved exchange 'Okx'...
|
||||
2025-04-29 07:44:22,595[90m - [0m[95mfreqtrade.resolvers.iresolver[0m[90m - [0m[34mINFO[0m[90m - [0mUsing resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'...
|
||||
2025-04-29 07:44:22,596[90m - [0m[95mfreqtrade.strategy.hyper[0m[90m - [0m[34mINFO[0m[90m - [0mLoading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json
|
||||
2025-04-29 07:44:22,597[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride strategy 'timeframe' with value in config file: 3m.
|
||||
2025-04-29 07:44:22,597[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride strategy 'stoploss' with value in config file: -0.05.
|
||||
2025-04-29 07:44:22,598[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride strategy 'stake_currency' with value in config file: USDT.
|
||||
2025-04-29 07:44:22,598[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride strategy 'stake_amount' with value in config file: 150.
|
||||
2025-04-29 07:44:22,598[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride strategy 'startup_candle_count' with value in config file: 30.
|
||||
2025-04-29 07:44:22,599[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride 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[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mOverride strategy 'max_open_trades' with value in config file: 4.
|
||||
2025-04-29 07:44:22,599[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0}
|
||||
2025-04-29 07:44:22,600[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using timeframe: 3m
|
||||
2025-04-29 07:44:22,600[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using stoploss: -0.05
|
||||
2025-04-29 07:44:22,600[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using trailing_stop: True
|
||||
2025-04-29 07:44:22,601[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using trailing_stop_positive: 0.01
|
||||
2025-04-29 07:44:22,601[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using trailing_stop_positive_offset: 0.02
|
||||
2025-04-29 07:44:22,602[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using trailing_only_offset_is_reached: False
|
||||
2025-04-29 07:44:22,602[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using use_custom_stoploss: False
|
||||
2025-04-29 07:44:22,602[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using process_only_new_candles: True
|
||||
2025-04-29 07:44:22,603[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy 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[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'}
|
||||
2025-04-29 07:44:22,603[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using stake_currency: USDT
|
||||
2025-04-29 07:44:22,604[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using stake_amount: 150
|
||||
2025-04-29 07:44:22,604[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using startup_candle_count: 30
|
||||
2025-04-29 07:44:22,604[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'}
|
||||
2025-04-29 07:44:22,605[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using use_exit_signal: True
|
||||
2025-04-29 07:44:22,605[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using exit_profit_only: False
|
||||
2025-04-29 07:44:22,605[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using ignore_roi_if_entry_signal: False
|
||||
2025-04-29 07:44:22,606[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using exit_profit_offset: 0.0
|
||||
2025-04-29 07:44:22,606[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using disable_dataframe_checks: False
|
||||
2025-04-29 07:44:22,606[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using ignore_buying_expired_candle_after: 0
|
||||
2025-04-29 07:44:22,607[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using position_adjustment_enable: False
|
||||
2025-04-29 07:44:22,607[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using max_entry_position_adjustment: -1
|
||||
2025-04-29 07:44:22,607[90m - [0m[95mfreqtrade.resolvers.strategy_resolver[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy using max_open_trades: 4
|
||||
2025-04-29 07:44:22,608[90m - [0m[95mfreqtrade.configuration.config_validation[0m[90m - [0m[34mINFO[0m[90m - [0mValidating configuration ...
|
||||
2025-04-29 07:44:22,611[90m - [0m[95mfreqtrade.resolvers.iresolver[0m[90m - [0m[34mINFO[0m[90m - [0mUsing resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'...
|
||||
2025-04-29 07:44:22,618[90m - [0m[95mfreqtrade.optimize.backtesting[0m[90m - [0m[34mINFO[0m[90m - [0mUsing fee 0.1500% - worst case fee from exchange (lowest tier).
|
||||
2025-04-29 07:44:22,619[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 3m to 14450
|
||||
2025-04-29 07:44:22,619[90m - [0m[95mfreqtrade.data.history.history_utils[0m[90m - [0m[34mINFO[0m[90m - [0mUsing indicator startup period: 14450 ...
|
||||
2025-04-29 07:44:22,751[90m - [0m[95mfreqtrade.optimize.backtesting[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.optimize.backtesting[0m[90m - [0m[34mINFO[0m[90m - [0mDataload complete. Calculating indicators
|
||||
2025-04-29 07:44:22,752[90m - [0m[95mfreqtrade.optimize.backtesting[0m[90m - [0m[34mINFO[0m[90m - [0mRunning backtesting for Strategy FreqaiExampleStrategy
|
||||
2025-04-29 07:44:24,335[90m - [0m[95mmatplotlib.font_manager[0m[90m - [0m[34mINFO[0m[90m - [0mgenerated new fontManager
|
||||
2025-04-29 07:44:24,532[90m - [0m[95mfreqtrade.resolvers.iresolver[0m[90m - [0m[34mINFO[0m[90m - [0mUsing resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'...
|
||||
2025-04-29 07:44:24,532[90m - [0m[95mfreqtrade.freqai.data_drawer[0m[90m - [0m[34mINFO[0m[90m - [0mCould not find existing datadrawer, starting from scratch
|
||||
2025-04-29 07:44:24,533[90m - [0m[95mfreqtrade.freqai.data_drawer[0m[90m - [0m[34mINFO[0m[90m - [0mCould not find existing historic_predictions, starting from scratch
|
||||
2025-04-29 07:44:24,533[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mSet fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT']
|
||||
2025-04-29 07:44:24,534[90m - [0m[95mfreqtrade.strategy.hyper[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy Parameter: buy_rsi = 39.92672300850069
|
||||
2025-04-29 07:44:24,535[90m - [0m[95mfreqtrade.strategy.hyper[0m[90m - [0m[34mINFO[0m[90m - [0mStrategy Parameter: sell_rsi = 69.92672300850067
|
||||
2025-04-29 07:44:24,535[90m - [0m[95mfreqtrade.strategy.hyper[0m[90m - [0m[34mINFO[0m[90m - [0mNo params for protection found, using default values.
|
||||
2025-04-29 07:44:24,537[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m处理交易对：BTC/USDT
|
||||
2025-04-29 07:44:24,539[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 2 timeranges
|
||||
2025-04-29 07:44:24,540[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mCould 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 5m to 8690
|
||||
2025-04-29 07:44:24,577[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 1h to 770
|
||||
2025-04-29 07:44:24,647[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 3m to 14450
|
||||
2025-04-29 07:44:24,698[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 5m to 8690
|
||||
2025-04-29 07:44:24,787[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 1h to 770
|
||||
2025-04-29 07:44:24,859[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 07:44:24,912[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(14450,)
|
||||
2025-04-29 07:44:24,914[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 07:44:24,922[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(19250,)
|
||||
2025-04-29 07:44:24,924[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mCould not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600
|
||||
2025-04-29 07:44:24,929[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 07:44:24,947[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mBTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 07:44:24,948[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Training on data from 2025-03-02 to 2025-03-31 --------------------
|
||||
2025-04-29 07:44:24,963[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining model on 75 features
|
||||
2025-04-29 07:44:24,963[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- 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[90m - [0m[95mfreqtrade.plot.plotting[0m[90m - [0m[34mINFO[0m[90m - [0mStored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600--buy_rsi.html
|
||||
2025-04-29 07:44:25,962[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mSaving metadata to disk.
|
||||
2025-04-29 07:44:25,985[90m - [0m[95mdatasieve.pipeline[0m[90m - [0m[33mWARNING[0m[90m - [0mCould not find step di in pipeline, returning None
|
||||
2025-04-29 07:44:25,993[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mCould 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 07:44:26,001[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(19250,)
|
||||
2025-04-29 07:44:26,003[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 07:44:26,013[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(23570,)
|
||||
2025-04-29 07:44:26,015[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mCould not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600
|
||||
2025-04-29 07:44:26,020[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 07:44:26,040[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mBTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 07:44:26,040[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Training on data from 2025-03-12 to 2025-04-10 --------------------
|
||||
2025-04-29 07:44:26,055[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining model on 75 features
|
||||
2025-04-29 07:44:26,056[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Done training BTC/USDT (0.81 secs) --------------------
|
||||
2025-04-29 07:44:26,867[90m - [0m[95mfreqtrade.plot.plotting[0m[90m - [0m[34mINFO[0m[90m - [0mStored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600--buy_rsi.html
|
||||
2025-04-29 07:44:26,868[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mSaving metadata to disk.
|
||||
2025-04-29 07:44:26,886[90m - [0m[95mdatasieve.pipeline[0m[90m - [0m[33mWARNING[0m[90m - [0mCould not find step di in pipeline, returning None
|
||||
2025-04-29 07:44:26,923[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0mup_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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0mdo_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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m处理交易对：SOL/USDT
|
||||
2025-04-29 07:44:26,934[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 2 timeranges
|
||||
2025-04-29 07:44:26,935[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mCould 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 5m to 8690
|
||||
2025-04-29 07:44:26,959[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 1h to 770
|
||||
2025-04-29 07:44:27,020[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mIncreasing startup_candle_count for freqai on 3m to 14450
|
||||
2025-04-29 07:44:27,068[90m - [0m[95mfreqtrade.data.dataprovider[0m[90m - [0m[34mINFO[0m[90m - [0mLoading 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 07:44:27,337[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(14450,)
|
||||
2025-04-29 07:44:27,339[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 07:44:27,347[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(19250,)
|
||||
2025-04-29 07:44:27,349[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mCould not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600
|
||||
2025-04-29 07:44:27,354[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 07:44:27,378[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mSOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 07:44:27,379[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Training on data from 2025-03-02 to 2025-03-31 --------------------
|
||||
2025-04-29 07:44:27,402[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining model on 111 features
|
||||
2025-04-29 07:44:27,402[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- 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[90m - [0m[95mfreqtrade.plot.plotting[0m[90m - [0m[34mINFO[0m[90m - [0mStored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600--buy_rsi.html
|
||||
2025-04-29 07:44:28,643[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mSaving metadata to disk.
|
||||
2025-04-29 07:44:28,668[90m - [0m[95mdatasieve.pipeline[0m[90m - [0m[33mWARNING[0m[90m - [0mCould not find step di in pipeline, returning None
|
||||
2025-04-29 07:44:28,675[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mCould 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 07:44:28,684[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(19250,)
|
||||
2025-04-29 07:44:28,685[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 07:44:28,696[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m目标列形状：(23570,)
|
||||
2025-04-29 07:44:28,697[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mCould not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600
|
||||
2025-04-29 07:44:28,703[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 07:44:28,727[90m - [0m[95mfreqtrade.freqai.data_kitchen[0m[90m - [0m[34mINFO[0m[90m - [0mSOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 07:44:28,728[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- Training on data from 2025-03-12 to 2025-04-10 --------------------
|
||||
2025-04-29 07:44:28,750[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining model on 111 features
|
||||
2025-04-29 07:44:28,751[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0mTraining 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[90m - [0m[95mfreqtrade.freqai.base_models.BaseRegressionModel[0m[90m - [0m[34mINFO[0m[90m - [0m-------------------- 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[90m - [0m[95mfreqtrade.plot.plotting[0m[90m - [0m[34mINFO[0m[90m - [0mStored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600--buy_rsi.html
|
||||
2025-04-29 07:44:29,964[90m - [0m[95mfreqtrade.freqai.freqai_interface[0m[90m - [0m[34mINFO[0m[90m - [0mSaving metadata to disk.
|
||||
2025-04-29 07:44:29,989[90m - [0m[95mdatasieve.pipeline[0m[90m - [0m[33mWARNING[0m[90m - [0mCould not find step di in pipeline, returning None
|
||||
2025-04-29 07:44:30,034[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0m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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0mup_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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0mdo_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[90m - [0m[95mfreqtrade.optimize.backtesting[0m[90m - [0m[34mINFO[0m[90m - [0mBacktesting 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[1;31mERROR[0m[90m - [0mMACD 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0mMACD 列已成功重新计算。
|
||||
2025-04-29 07:44:30,066[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[1;31mERROR[0m[90m - [0mMACD 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[90m - [0m[95mFreqaiExampleStrategy[0m[90m - [0m[34mINFO[0m[90m - [0mMACD 列已成功重新计算。
|
||||
2025-04-29 07:44:30,639[90m - [0m[95mfreqtrade.misc[0m[90m - [0m[34mINFO[0m[90m - [0mdumping 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
|
||||
[3m BACKTESTING REPORT [0m
|
||||
BACKTESTING REPORT
|
||||
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1m Pair[0m[1m [0m┃[1m [0m[1mTrades[0m[1m [0m┃[1m [0m[1mAvg Profit %[0m[1m [0m┃[1m [0m[1mTot Profit USDT[0m[1m [0m┃[1m [0m[1mTot Profit %[0m[1m [0m┃[1m [0m[1mAvg Duration[0m[1m [0m┃[1m [0m[1m Win Draw Loss Win%[0m[1m [0m┃
|
||||
┃ 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 │
|
||||
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
[3m LEFT OPEN TRADES REPORT [0m
|
||||
LEFT OPEN TRADES REPORT
|
||||
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1m Pair[0m[1m [0m┃[1m [0m[1mTrades[0m[1m [0m┃[1m [0m[1mAvg Profit %[0m[1m [0m┃[1m [0m[1mTot Profit USDT[0m[1m [0m┃[1m [0m[1mTot Profit %[0m[1m [0m┃[1m [0m[1mAvg Duration[0m[1m [0m┃[1m [0m[1m Win Draw Loss Win%[0m[1m [0m┃
|
||||
┃ 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 │
|
||||
└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
[3m ENTER TAG STATS [0m
|
||||
ENTER TAG STATS
|
||||
┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1mEnter Tag[0m[1m [0m┃[1m [0m[1mEntries[0m[1m [0m┃[1m [0m[1mAvg Profit %[0m[1m [0m┃[1m [0m[1mTot Profit USDT[0m[1m [0m┃[1m [0m[1mTot Profit %[0m[1m [0m┃[1m [0m[1mAvg Duration[0m[1m [0m┃[1m [0m[1m Win Draw Loss Win%[0m[1m [0m┃
|
||||
┃ 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 │
|
||||
└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
[3m EXIT REASON STATS [0m
|
||||
EXIT REASON STATS
|
||||
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1m Exit Reason[0m[1m [0m┃[1m [0m[1mExits[0m[1m [0m┃[1m [0m[1mAvg Profit %[0m[1m [0m┃[1m [0m[1mTot Profit USDT[0m[1m [0m┃[1m [0m[1mTot Profit %[0m[1m [0m┃[1m [0m[1m Avg Duration[0m[1m [0m┃[1m [0m[1m Win Draw Loss Win%[0m[1m [0m┃
|
||||
┃ 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 │
|
||||
└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
|
||||
[3m MIXED TAG STATS [0m
|
||||
MIXED TAG STATS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1m Enter Tag[0m[1m [0m┃[1m [0m[1mExit Reason[0m[1m [0m┃[1m [0m[1mTrades[0m[1m [0m┃[1m [0m[1mAvg Profit %[0m[1m [0m┃[1m [0m[1mTot Profit USDT[0m[1m [0m┃[1m [0m[1mTot Profit %[0m[1m [0m┃[1m [0m[1m Avg Duration[0m[1m [0m┃[1m [0m[1m Win Draw Loss Win%[0m[1m [0m┃
|
||||
┃ 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 │
|
||||
└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
|
||||
[3m SUMMARY METRICS [0m
|
||||
SUMMARY METRICS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1mMetric [0m[1m [0m┃[1m [0m[1mValue [0m[1m [0m┃
|
||||
┃ 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
|
||||
[3m STRATEGY SUMMARY [0m
|
||||
STRATEGY SUMMARY
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃[1m [0m[1m Strategy[0m[1m [0m┃[1m [0m[1mTrades[0m[1m [0m┃[1m [0m[1mAvg Profit %[0m[1m [0m┃[1m [0m[1mTot Profit USDT[0m[1m [0m┃[1m [0m[1mTot Profit %[0m[1m [0m┃[1m [0m[1mAvg Duration[0m[1m [0m┃[1m [0m[1m Win Draw Loss Win%[0m[1m [0m┃[1m [0m[1m Drawdown[0m[1m [0m┃
|
||||
┃ 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% │
|
||||
└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴────────────────────┘
|
||||
|
||||
1
result/backtest-result-2025-05-01_04-27-37.json
Normal file
1
result/backtest-result-2025-05-01_04-27-37.json
Normal file
File diff suppressed because one or more lines are too long
1
result/backtest-result-2025-05-01_04-27-37.meta.json
Normal file
1
result/backtest-result-2025-05-01_04-27-37.meta.json
Normal file
@ -0,0 +1 @@
|
||||
{"FreqaiExampleStrategy":{"run_id":"503e73c282d0ca15563ac9a0a92479d87ab89671","backtest_start_time":1746073641,"timeframe":"3m","timeframe_detail":null,"backtest_start_ts":1743465600,"backtest_end_ts":1745107200}}
|
||||
BIN
result/backtest-result-2025-05-01_04-27-37.zip
Normal file
BIN
result/backtest-result-2025-05-01_04-27-37.zip
Normal file
Binary file not shown.
@ -0,0 +1,32 @@
|
||||
{
|
||||
"strategy_name": "FreqaiExampleStrategy",
|
||||
"params": {
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.01,
|
||||
"trailing_stop_positive_offset": 0.02,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 4
|
||||
},
|
||||
"buy": {
|
||||
"buy_rsi": 39.92672300850069
|
||||
},
|
||||
"sell": {
|
||||
"sell_rsi": 69.92672300850067
|
||||
},
|
||||
"protection": {},
|
||||
"roi": {
|
||||
"0": 0.132,
|
||||
"8": 0.047,
|
||||
"14": 0.007,
|
||||
"60": 0
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -0.322
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-04-23 12:30:05.550433+00:00"
|
||||
}
|
||||
@ -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
|
||||
1
result/backtest-result-2025-05-01_04-27-37_config.json
Normal file
1
result/backtest-result-2025-05-01_04-27-37_config.json
Normal file
@ -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"]}
|
||||
BIN
result/backtest-result-2025-05-01_04-27-37_market_change.feather
Normal file
BIN
result/backtest-result-2025-05-01_04-27-37_market_change.feather
Normal file
Binary file not shown.
23
run.sh
Executable file
23
run.sh
Executable file
@ -0,0 +1,23 @@
|
||||
#!/bin/bash
|
||||
|
||||
rm -rf user_data/models/*
|
||||
rm -rf ./freqtrade/user_data/data/backtest_results/*
|
||||
|
||||
docker-compose run --rm freqtrade >output.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
|
||||
2202
temp_validation.log
Normal file
2202
temp_validation.log
Normal file
File diff suppressed because it is too large
Load Diff
3066
your_output_file.log
Normal file
3066
your_output_file.log
Normal file
File diff suppressed because it is too large
Load Diff
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Reference in New Issue
Block a user