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zhangkun9038@dingtalk.com 2025-05-04 08:11:46 +08:00
parent b222485199
commit 22f3e52cbd
13 changed files with 5796 additions and 112882 deletions
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48
config_examples/config_freqai.okx.json
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@ -35,12 +35,8 @@
"timeout": 20000
},
"pair_whitelist": [
"BTC/USDT",
"DOGE/USDT",
"OKB/USDT",
"TON/USDT",
"XRP/USDT",
"SOL/USDT"
"TON/USDT"
],
"pair_blacklist": []
},
@ -66,46 +62,46 @@
],
"freqai": {
"enabled": true,
"identifier": "test175",
"freqaimodel": "XGBoostRegressor",
"purge_old_models": 2,
"train_period_days": 90,
"backtest_period_days": 10,
"live_retrain_hours": 0,
"data_kitchen": {
"fillna": "ffill",
"feature_parameters": {
"DI_threshold": 0.9,
"DI_threshold": 0.5,
"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_timeframes": ["3m"],
"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
"plot_feature_importances": 1,
"feature_selection": {
"method": "none"
}
},
"data_split_parameters": {
"test_size": 0.2,
"shuffle": true,
"shuffle": false,
"random_state": 42
},
"model_training_parameters": {
"n_estimators": 100,
"learning_rate": 0.05,
"max_depth": 5,
"subsample": 0.8,
"colsample_bytree": 0.8,
"n_estimators": 500,
"learning_rate": 0.015,
"max_depth": 6,
"subsample": 0.9,
"colsample_bytree": 0.9,
"objective": "reg:squarederror",
"eval_metric": "rmse",
"early_stopping_rounds": 50,
"verbose": 0
"early_stopping_rounds": 150,
"reg_alpha": 0.1,
"reg_lambda": 1.0
}
},
"api_server": {

5
docker-compose.yml
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@ -1,7 +1,7 @@
---
services:
freqtrade:
image: freqtradeorg/freqtrade:stable_freqaitorch
image: freqtradeorg/freqtrade:develop_freqaitorch
# # Enable GPU Image and GPU Resources
# # Make sure to uncomment the whole deploy section
# deploy:
@ -70,5 +70,6 @@ services:
--config /freqtrade/config_examples/config_freqai.okx.json
--strategy-path /freqtrade/templates
--strategy OKXRegressionStrategy
--timerange 20240415-20250415
--timerange 20250315-20250415
--fee 0.0008
--cache none

141
freqtrade/templates/OKXRegressionStrategy.py
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@ -11,6 +11,8 @@ from freqtrade.strategy import CategoricalParameter, DecimalParameter
from xgboost import XGBRegressor
import ccxt
logger = logging.getLogger(__name__)
class OKXRegressionStrategy(IStrategy):
@ -51,6 +53,7 @@ class OKXRegressionStrategy(IStrategy):
},
"data_split_parameters": {
"test_size": 0.2,
"random_state": 42,
"shuffle": False
},
"train_period_days": 90,
@ -88,15 +91,18 @@ class OKXRegressionStrategy(IStrategy):
# 成交量均线
dataframe[f"%-%-volume_ma-{period}"] = ta.SMA(dataframe["volume"], timeperiod=period)
# OKX 订单簿特征（通过 CCXT）
try:
order_book = self.fetch_okx_order_book(metadata["pair"])
dataframe[f"%-%-order_book_imbalance-{period}"] = (
order_book["bids"] - order_book["asks"]
) / (order_book["bids"] + order_book["asks"] + 1e-10)
except Exception as e:
logger.warning(f"获取 {metadata['pair']} 订单簿失败：{str(e)}")
dataframe[f"%-%-order_book_imbalance-{period}"] = 0.0
# 仅为 BTC/USDT 和 ETH/USDT 生成 order_book_imbalance
#
pair = metadata.get('pair', 'unknown')
if pair in ["BTC/USDT", "ETH/USDT"]:
try:
order_book = self.fetch_okx_order_book(pair)
dataframe[f"%-%-order_book_imbalance"] = (
order_book["bids"] - order_book["asks"]
) / (order_book["bids"] + order_book["asks"] + 1e-10)
except Exception as e:
logger.warning(f"Failed to fetch order book for {pair}: {str(e)}")
dataframe[f"%-%-order_book_imbalance"] = 0.0
# 数据清洗
dataframe = dataframe.replace([np.inf, -np.inf], np.nan)
@ -131,28 +137,108 @@ class OKXRegressionStrategy(IStrategy):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
"""
设置回归模型的目标变量。
设置回归模型的目标变量，为不同币对设置动态止损和ROI阈值。
输入：dataframe（K线数据：close, high, low），metadata（交易对信息），config（FreqAI配置）
输出：更新后的dataframe，包含目标标签
"""
label_period = self.freqai_config["feature_parameters"]["label_period_candles"]
# 目标：未来价格变化
dataframe["&-s_close"] = (
dataframe["close"].shift(-label_period) - dataframe["close"]
) / dataframe["close"]
# 获取配置参数
label_period = self.freqai_config["feature_parameters"]["label_period_candles"] # 标签预测周期（如5分钟K线的N根）
pair = metadata["pair"] # 当前交易对（如DOGE/USDT）
# ROI 目标
# 计算未来价格变化率（现有逻辑）
dataframe["&-s_close"] = (dataframe["close"].shift(-label_period) - dataframe["close"]) / dataframe["close"]
# 计算不同时间窗口的ROI（现有逻辑）
for minutes in [0, 15, 30]:
candles = int(minutes / 5)
candles = int(minutes / 5) # 假设5分钟K线
if candles > 0:
dataframe[f"&-roi_{minutes}"] = (
dataframe["close"].shift(-candles) - dataframe["close"]
) / dataframe["close"]
dataframe[f"&-roi_{minutes}"] = (dataframe["close"].shift(-candles) - dataframe["close"]) / dataframe["close"]
else:
dataframe[f"&-roi_{minutes}"] = 0.0
# 动态阈值
# 计算市场状态指标：ADX（14周期，与label_period_candles对齐）
dataframe["adx"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
# 定义币对特定的ADX阈值和止损/ROI范围
pair_thresholds = {
"DOGE/USDT": {
"adx_trend": 20, # 趋势市场ADX阈值
"adx_oscillation": 15, # 震荡市场ADX阈值
"stoploss_trend": -0.08, # 趋势市场止损：-8%
"stoploss_oscillation": -0.04, # 震荡市场止损：-4%
"stoploss_mid": -0.06, # 中间状态止损：-6%
"roi_trend": 0.06, # 趋势市场ROI：6%
"roi_oscillation": 0.025, # 震荡市场ROI：2.5%
"roi_mid": 0.04 # 中间状态ROI：4%
},
"BTC/USDT": {
"adx_trend": 25,
"adx_oscillation": 20,
"stoploss_trend": -0.03,
"stoploss_oscillation": -0.015,
"stoploss_mid": -0.02,
"roi_trend": 0.03,
"roi_oscillation": 0.015,
"roi_mid": 0.02
},
"SOL/USDT": {
"adx_trend": 22,
"adx_oscillation": 18,
"stoploss_trend": -0.06,
"stoploss_oscillation": -0.03,
"stoploss_mid": -0.045,
"roi_trend": 0.045,
"roi_oscillation": 0.02,
"roi_mid": 0.03
},
"XRP/USDT": {
"adx_trend": 22,
"adx_oscillation": 18,
"stoploss_trend": -0.06,
"stoploss_oscillation": -0.03,
"stoploss_mid": -0.045,
"roi_trend": 0.045,
"roi_oscillation": 0.02,
"roi_mid": 0.03
}
}
# 动态化 &-stoploss_pred（基于市场状态和币对）
dataframe["&-stoploss_pred"] = 0.0
for index, row in dataframe.iterrows():
thresholds = pair_thresholds.get(pair, {})
if not thresholds:
continue
adx_value = row["adx"]
if adx_value > thresholds["adx_trend"]: # 趋势市场
dataframe.at[index, "&-stoploss_pred"] = thresholds["stoploss_trend"] # 宽松止损
elif adx_value < thresholds["adx_oscillation"]: # 震荡市场
dataframe.at[index, "&-stoploss_pred"] = thresholds["stoploss_oscillation"] # 严格止损
else: # 中间状态
dataframe.at[index, "&-stoploss_pred"] = thresholds["stoploss_mid"] # 中等止损
# 风险控制：设置止损下限
if dataframe.at[index, "&-stoploss_pred"] < -0.10:
dataframe.at[index, "&-stoploss_pred"] = -0.10
# 动态化 &-roi_0_pred（基于市场趋势和币对）
dataframe["&-roi_0_pred"] = 0.0
for index, row in dataframe.iterrows():
thresholds = pair_thresholds.get(pair, {})
if not thresholds:
continue
adx_value = row["adx"]
if adx_value > thresholds["adx_trend"]: # 强趋势市场
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_trend"] # 高ROI
elif adx_value < thresholds["adx_oscillation"]: # 震荡市场
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_oscillation"] # 低ROI
else: # 中间状态
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_mid"] # 中等ROI
# 风险控制：设置ROI上限
if dataframe.at[index, "&-roi_0_pred"] > 0.10:
dataframe.at[index, "&-roi_0_pred"] = 0.10
# 计算RSI预测（现有逻辑）
dataframe["&-buy_rsi_pred"] = ta.RSI(dataframe["close"], timeperiod=14).rolling(20).mean()
dataframe["&-stoploss_pred"] = -0.05
dataframe["&-roi_0_pred"] = 0.03
# 数据清洗
dataframe = dataframe.replace([np.inf, -np.inf], np.nan)
@ -241,10 +327,17 @@ class OKXRegressionStrategy(IStrategy):
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
dataframe['atr'] = dataframe['ATR_14']
# 确保 ATR 列存在
if 'ATR_14' not in dataframe.columns:
dataframe['ATR_14'] = 0.0
# 计算动态止损线
dataframe['stop_loss_line'] = dataframe['entry_price'] - (dataframe['ATR_14'] * 2)
# 发送止损信息
self.dp.send_msg(f"ATR: {dataframe['ATR_14'].iloc[-1]:.5f}, Stop Loss Line: {dataframe['stop_loss_line'].iloc[-1]:.5f}")
# 应用动态止损逻辑
return self._dynamic_stop_loss(dataframe, metadata)
def custom_stake_amount(self, pair: str, current_time: 'datetime', current_rate: float,

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output.log
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output_filted.log
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result/backtest-result-2025-05-02_13-12-48.json
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1
result/backtest-result-2025-05-02_13-12-48.meta.json
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@ -1 +0,0 @@
{"OKXRegressionStrategy":{"run_id":"ab18fb2868557acc58d74ca02f236930d0ae691f","backtest_start_time":1746187066,"timeframe":"3m","timeframe_detail":null,"backtest_start_ts":1713139200,"backtest_end_ts":1744675200}}

1
result/backtest-result-2025-05-02_13-12-48_config.json
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@ -1 +0,0 @@
{"$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","DOGE/USDT","OKB/USDT","TON/USDT","XRP/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":100,"learning_rate":0.05,"max_depth":5,"subsample":0.8,"colsample_bytree":0.8,"objective":"reg:squarederror","eval_metric":"rmse","early_stopping_rounds":50,"verbose":0}},"api_server":{"enabled":true,"listen_ip_address":"0.0.0.0","listen_port":8080,"verbosity":"error","enable_openapi":false,"jwt_secret_key":"6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca","ws_token":"6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ","CORS_origins":[],"username":"freqAdmin","password":"REDACTED"},"bot_name":"freqtrade","initial_state":"running","force_entry_enable":false,"internals":{"process_throttle_secs":5,"heartbeat_interval":20,"loglevel":"DEBUG"},"config_files":["/freqtrade/config_examples/config_freqai.okx.json"]}

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result/backtest-result-2025-05-03_23-26-58.json Normal file
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result/backtest-result-2025-05-03_23-26-58.meta.json Normal file
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@ -0,0 +1 @@
{"OKXRegressionStrategy":{"run_id":"1b652eca8bab161933a4d0dbeb6c5aabe3aa0f25","backtest_start_time":1746309788,"timeframe":"3m","timeframe_detail":null,"backtest_start_ts":1741996800,"backtest_end_ts":1744675200}}

141
result/backtest-result-2025-05-02_13-12-48_OKXRegressionStrategy.py → result/backtest-result-2025-05-03_23-26-58_OKXRegressionStrategy.py
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@ -11,6 +11,8 @@ from freqtrade.strategy import CategoricalParameter, DecimalParameter
from xgboost import XGBRegressor
import ccxt
logger = logging.getLogger(__name__)
class OKXRegressionStrategy(IStrategy):
@ -51,6 +53,7 @@ class OKXRegressionStrategy(IStrategy):
},
"data_split_parameters": {
"test_size": 0.2,
"random_state": 42,
"shuffle": False
},
"train_period_days": 90,
@ -88,15 +91,18 @@ class OKXRegressionStrategy(IStrategy):
# 成交量均线
dataframe[f"%-%-volume_ma-{period}"] = ta.SMA(dataframe["volume"], timeperiod=period)
# OKX 订单簿特征（通过 CCXT）
try:
order_book = self.fetch_okx_order_book(metadata["pair"])
dataframe[f"%-%-order_book_imbalance-{period}"] = (
order_book["bids"] - order_book["asks"]
) / (order_book["bids"] + order_book["asks"] + 1e-10)
except Exception as e:
logger.warning(f"获取 {metadata['pair']} 订单簿失败：{str(e)}")
dataframe[f"%-%-order_book_imbalance-{period}"] = 0.0
# 仅为 BTC/USDT 和 ETH/USDT 生成 order_book_imbalance
#
pair = metadata.get('pair', 'unknown')
if pair in ["BTC/USDT", "ETH/USDT"]:
try:
order_book = self.fetch_okx_order_book(pair)
dataframe[f"%-%-order_book_imbalance"] = (
order_book["bids"] - order_book["asks"]
) / (order_book["bids"] + order_book["asks"] + 1e-10)
except Exception as e:
logger.warning(f"Failed to fetch order book for {pair}: {str(e)}")
dataframe[f"%-%-order_book_imbalance"] = 0.0
# 数据清洗
dataframe = dataframe.replace([np.inf, -np.inf], np.nan)
@ -131,28 +137,108 @@ class OKXRegressionStrategy(IStrategy):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
"""
设置回归模型的目标变量。
设置回归模型的目标变量，为不同币对设置动态止损和ROI阈值。
输入：dataframe（K线数据：close, high, low），metadata（交易对信息），config（FreqAI配置）
输出：更新后的dataframe，包含目标标签
"""
label_period = self.freqai_config["feature_parameters"]["label_period_candles"]
# 目标：未来价格变化
dataframe["&-s_close"] = (
dataframe["close"].shift(-label_period) - dataframe["close"]
) / dataframe["close"]
# 获取配置参数
label_period = self.freqai_config["feature_parameters"]["label_period_candles"] # 标签预测周期（如5分钟K线的N根）
pair = metadata["pair"] # 当前交易对（如DOGE/USDT）
# ROI 目标
# 计算未来价格变化率（现有逻辑）
dataframe["&-s_close"] = (dataframe["close"].shift(-label_period) - dataframe["close"]) / dataframe["close"]
# 计算不同时间窗口的ROI（现有逻辑）
for minutes in [0, 15, 30]:
candles = int(minutes / 5)
candles = int(minutes / 5) # 假设5分钟K线
if candles > 0:
dataframe[f"&-roi_{minutes}"] = (
dataframe["close"].shift(-candles) - dataframe["close"]
) / dataframe["close"]
dataframe[f"&-roi_{minutes}"] = (dataframe["close"].shift(-candles) - dataframe["close"]) / dataframe["close"]
else:
dataframe[f"&-roi_{minutes}"] = 0.0
# 动态阈值
# 计算市场状态指标：ADX（14周期，与label_period_candles对齐）
dataframe["adx"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
# 定义币对特定的ADX阈值和止损/ROI范围
pair_thresholds = {
"DOGE/USDT": {
"adx_trend": 20, # 趋势市场ADX阈值
"adx_oscillation": 15, # 震荡市场ADX阈值
"stoploss_trend": -0.08, # 趋势市场止损：-8%
"stoploss_oscillation": -0.04, # 震荡市场止损：-4%
"stoploss_mid": -0.06, # 中间状态止损：-6%
"roi_trend": 0.06, # 趋势市场ROI：6%
"roi_oscillation": 0.025, # 震荡市场ROI：2.5%
"roi_mid": 0.04 # 中间状态ROI：4%
},
"BTC/USDT": {
"adx_trend": 25,
"adx_oscillation": 20,
"stoploss_trend": -0.03,
"stoploss_oscillation": -0.015,
"stoploss_mid": -0.02,
"roi_trend": 0.03,
"roi_oscillation": 0.015,
"roi_mid": 0.02
},
"SOL/USDT": {
"adx_trend": 22,
"adx_oscillation": 18,
"stoploss_trend": -0.06,
"stoploss_oscillation": -0.03,
"stoploss_mid": -0.045,
"roi_trend": 0.045,
"roi_oscillation": 0.02,
"roi_mid": 0.03
},
"XRP/USDT": {
"adx_trend": 22,
"adx_oscillation": 18,
"stoploss_trend": -0.06,
"stoploss_oscillation": -0.03,
"stoploss_mid": -0.045,
"roi_trend": 0.045,
"roi_oscillation": 0.02,
"roi_mid": 0.03
}
}
# 动态化 &-stoploss_pred（基于市场状态和币对）
dataframe["&-stoploss_pred"] = 0.0
for index, row in dataframe.iterrows():
thresholds = pair_thresholds.get(pair, {})
if not thresholds:
continue
adx_value = row["adx"]
if adx_value > thresholds["adx_trend"]: # 趋势市场
dataframe.at[index, "&-stoploss_pred"] = thresholds["stoploss_trend"] # 宽松止损
elif adx_value < thresholds["adx_oscillation"]: # 震荡市场
dataframe.at[index, "&-stoploss_pred"] = thresholds["stoploss_oscillation"] # 严格止损
else: # 中间状态
dataframe.at[index, "&-stoploss_pred"] = thresholds["stoploss_mid"] # 中等止损
# 风险控制：设置止损下限
if dataframe.at[index, "&-stoploss_pred"] < -0.10:
dataframe.at[index, "&-stoploss_pred"] = -0.10
# 动态化 &-roi_0_pred（基于市场趋势和币对）
dataframe["&-roi_0_pred"] = 0.0
for index, row in dataframe.iterrows():
thresholds = pair_thresholds.get(pair, {})
if not thresholds:
continue
adx_value = row["adx"]
if adx_value > thresholds["adx_trend"]: # 强趋势市场
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_trend"] # 高ROI
elif adx_value < thresholds["adx_oscillation"]: # 震荡市场
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_oscillation"] # 低ROI
else: # 中间状态
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_mid"] # 中等ROI
# 风险控制：设置ROI上限
if dataframe.at[index, "&-roi_0_pred"] > 0.10:
dataframe.at[index, "&-roi_0_pred"] = 0.10
# 计算RSI预测（现有逻辑）
dataframe["&-buy_rsi_pred"] = ta.RSI(dataframe["close"], timeperiod=14).rolling(20).mean()
dataframe["&-stoploss_pred"] = -0.05
dataframe["&-roi_0_pred"] = 0.03
# 数据清洗
dataframe = dataframe.replace([np.inf, -np.inf], np.nan)
@ -241,10 +327,17 @@ class OKXRegressionStrategy(IStrategy):
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
dataframe['atr'] = dataframe['ATR_14']
# 确保 ATR 列存在
if 'ATR_14' not in dataframe.columns:
dataframe['ATR_14'] = 0.0
# 计算动态止损线
dataframe['stop_loss_line'] = dataframe['entry_price'] - (dataframe['ATR_14'] * 2)
# 发送止损信息
self.dp.send_msg(f"ATR: {dataframe['ATR_14'].iloc[-1]:.5f}, Stop Loss Line: {dataframe['stop_loss_line'].iloc[-1]:.5f}")
# 应用动态止损逻辑
return self._dynamic_stop_loss(dataframe, metadata)
def custom_stake_amount(self, pair: str, current_time: 'datetime', current_rate: float,

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@ -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":["OKB/USDT","TON/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,"identifier":"test175","freqaimodel":"XGBoostRegressor","purge_old_models":2,"train_period_days":90,"backtest_period_days":10,"live_retrain_hours":0,"data_kitchen":{"fillna":"ffill","feature_parameters":{"DI_threshold":0.5,"weight_factor":0.9}},"feature_parameters":{"include_timeframes":["3m"],"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,"feature_selection":{"method":"none"}},"data_split_parameters":{"test_size":0.2,"shuffle":false,"random_state":42},"model_training_parameters":{"n_estimators":500,"learning_rate":0.015,"max_depth":6,"subsample":0.9,"colsample_bytree":0.9,"objective":"reg:squarederror","eval_metric":"rmse","early_stopping_rounds":150,"reg_alpha":0.1,"reg_lambda":1.0}},"api_server":{"enabled":true,"listen_ip_address":"0.0.0.0","listen_port":8080,"verbosity":"error","enable_openapi":false,"jwt_secret_key":"6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca","ws_token":"6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ","CORS_origins":[],"username":"freqAdmin","password":"REDACTED"},"bot_name":"freqtrade","initial_state":"running","force_entry_enable":false,"internals":{"process_throttle_secs":5,"heartbeat_interval":20,"loglevel":"DEBUG"},"config_files":["/freqtrade/config_examples/config_freqai.okx.json"]}

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