stable1 14
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zhangkun9038@dingtalk.com
parent
eceb04d22d
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9bb14377ed
@ -60,47 +60,49 @@
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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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"model_path": "/freqtrade/user_data/models",
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"data_kitchen": {
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"fillna": "ffill"
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},
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"freqaimodel": "XGBoostRegressor",
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"model_training_parameters": {
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"n_estimators": 300,
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"learning_rate": 0.03,
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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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"reg_lambda": 1.0,
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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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},
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"train_period_days": 730, // 增加到 2 年
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"backtest_period_days": 90,
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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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},
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"feature_parameters": {
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"include_timeframes": ["15m", "1h", "4h"],
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"include_corr_pairlist": ["BTC/USDT", "SOL/USDT", "ETH/USDT"],
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"label_period_candles": 40, // 延长预测周期
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"include_shifted_candles": 2,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"SVM_parameters": {
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"nu": 0.1
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{
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"freqai": {
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"enabled": true,
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"model_path": "/freqtrade/user_data/models",
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"data_kitchen": {
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"fillna": "ffill"
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},
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"DI_threshold": 0, // 禁用 DI 过滤
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"indicator_periods_candles": [14, 20]
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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
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"freqaimodel": "XGBoostRegressor",
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"model_training_parameters": {
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"n_estimators": 300,
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"learning_rate": 0.03,
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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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"reg_lambda": 1.0,
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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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},
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"train_period_days": 730,
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"backtest_period_days": 90,
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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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},
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"feature_parameters": {
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"include_timeframes": ["15m", "1h", "4h"],
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"include_corr_pairlist": ["BTC/USDT", "SOL/USDT", "ETH/USDT"],
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"label_period_candles": 60,
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"include_shifted_candles": 2,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"SVM_parameters": {
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"nu": 0.1
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},
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"DI_threshold": 0,
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"indicator_periods_candles": [14, 20]
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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
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}
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}
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},
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"api_server": {
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@ -1,5 +1,6 @@
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import logging
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import numpy as np
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import pandas as pd
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from functools import reduce
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import talib.abstract as ta
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from pandas import DataFrame
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@ -13,10 +14,10 @@ class FreqaiExampleStrategy(IStrategy):
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FreqAI-based trading strategy using XGBoostRegressor for regression-based price movement prediction.
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Optimized for short-term trading on spot markets (BTC/USDT, ETH/USDT, SOL/USDT).
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Key improvements:
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- Fixed KeyError in populate_entry_trend by using pd.concat for conditions
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- Dynamic ATR-based stop-loss and ROI
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- Relaxed entry conditions to increase trading frequency
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- Enhanced feature engineering with cross-timeframe indicators
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- Standardized target values to amplify prediction signals
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- Standardized and transformed target values to amplify signals
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- Disabled DI filtering to resolve datasieve warnings
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"""
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@ -45,7 +46,7 @@ class FreqaiExampleStrategy(IStrategy):
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"feature_parameters": {
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"include_timeframes": ["15m", "1h", "4h"],
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"include_corr_pairlist": ["BTC/USDT", "SOL/USDT", "ETH/USDT"],
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"label_period_candles": 40, # Extended prediction horizon
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"label_period_candles": 60, # Extended prediction horizon
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"include_shifted_candles": 2,
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"weight_factor": 0.9,
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"principal_component_analysis": False,
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@ -78,9 +79,7 @@ class FreqaiExampleStrategy(IStrategy):
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}
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def calculate_macd(self, dataframe: DataFrame) -> DataFrame:
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"""
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Calculate MACD indicators and handle exceptions.
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"""
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"""Calculate MACD indicators and handle exceptions."""
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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"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
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@ -93,9 +92,7 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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"""
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Enhanced feature engineering with cross-timeframe and momentum indicators.
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"""
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"""Enhanced feature engineering with cross-timeframe and momentum indicators."""
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# Standard technical indicators
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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dataframe["sma20"] = ta.SMA(dataframe["close"], timeperiod=20)
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@ -121,7 +118,7 @@ class FreqaiExampleStrategy(IStrategy):
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bollinger_tf = qtpylib.bollinger_bands(qtpylib.typical_price(tf_data), window=20, stds=2)
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dataframe[f"bb_width_{tf}"] = (bollinger_tf["upper"] - bollinger_tf["lower"]) / bollinger_tf["mid"]
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# Correlated pair features (e.g., BTC influence on SOL)
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# Correlated pair features
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if metadata["pair"] == "SOL/USDT":
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btc_data = self.dp.get_pair_dataframe(pair="BTC/USDT", timeframe=self.timeframe)
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if not btc_data.empty:
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@ -137,9 +134,7 @@ class FreqaiExampleStrategy(IStrategy):
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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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"""
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Basic feature engineering for FreqAI.
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"""
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"""Basic feature engineering for FreqAI."""
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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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@ -149,9 +144,7 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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"""
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Standard feature engineering for temporal features.
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"""
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"""Standard feature engineering for temporal features."""
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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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for col in dataframe.columns:
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@ -160,9 +153,7 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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"""
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Set FreqAI prediction targets with standardized up_or_down values.
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"""
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"""Set FreqAI prediction targets with standardized and transformed values."""
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logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
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if "close" not in dataframe.columns:
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logger.error("DataFrame missing 'close' column")
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@ -173,10 +164,12 @@ class FreqaiExampleStrategy(IStrategy):
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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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# Standardize target values to amplify signal strength
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# Standardize target values
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dataframe["&-up_or_down"] = (
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dataframe["&-up_or_down"] - dataframe["&-up_or_down"].mean()
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) / dataframe["&-up_or_down"].std()
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# Apply logarithmic transformation to amplify signals
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dataframe["&-up_or_down"] = np.log1p(dataframe["&-up_or_down"].abs()) * np.sign(dataframe["&-up_or_down"])
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dataframe["&-up_or_down"] = dataframe["&-up_or_down"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
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dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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@ -191,9 +184,7 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Populate indicators and dynamic strategy parameters.
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"""
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"""Populate indicators and dynamic strategy parameters."""
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logger.info(f"Processing pair: {metadata['pair']}")
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dataframe = self.freqai.start(dataframe, metadata, self)
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@ -243,35 +234,44 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Generate entry signals with relaxed conditions to increase trading frequency.
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"""
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"""Generate entry signals with relaxed conditions."""
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# Validate required columns
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required_cols = ["rsi", "buy_rsi_pred", "volume", "bb_middleband", "macd", "macdsignal", "&-up_or_down"]
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if not all(col in dataframe.columns for col in required_cols):
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logger.error(f"Missing required columns: {set(required_cols) - set(dataframe.columns)}")
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return dataframe
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dataframe = self.calculate_macd(dataframe)
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enter_long_conditions = [
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(dataframe["rsi"] < dataframe["buy_rsi_pred"]),
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(dataframe["volume"] > dataframe["volume"].rolling(window=10).mean() * 1.0), # Relaxed volume condition
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(dataframe["close"] > dataframe["bb_middleband"]),
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(dataframe["macd"] > dataframe["macdsignal"]),
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(dataframe["&-up_or_down"] > 0.0015) # Lowered threshold for more signals
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dataframe["rsi"] < dataframe["buy_rsi_pred"],
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dataframe["volume"] > dataframe["volume"].rolling(window=10).mean() * 1.0,
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dataframe["close"] > dataframe["bb_middleband"],
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dataframe["macd"] > dataframe["macdsignal"],
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dataframe["&-up_or_down"] > (0.001 if metadata["pair"] == "BTC/USDT" else 0.0015) # Lower threshold for BTC
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]
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# Require at least 3 conditions to be met
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dataframe.loc[dataframe[enter_long_conditions].sum(axis=1) >= 3, ["enter_long", "enter_tag"]] = (1, "long")
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# Combine conditions into a DataFrame
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conditions_df = pd.concat(enter_long_conditions, axis=1)
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dataframe.loc[conditions_df.sum(axis=1) >= 3, ["enter_long", "enter_tag"]] = (1, "long")
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Generate exit signals with confirmation to reduce premature exits.
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"""
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"""Generate exit signals with confirmation."""
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# Validate required columns
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required_cols = ["rsi", "sell_rsi_pred", "bb_middleband", "macd", "macdsignal", "&-up_or_down"]
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if not all(col in dataframe.columns for col in required_cols):
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logger.error(f"Missing required columns: {set(required_cols) - set(dataframe.columns)}")
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return dataframe
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exit_long_conditions = [
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(dataframe["rsi"] > dataframe["sell_rsi_pred"]),
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(dataframe["close"] < dataframe["bb_middleband"]),
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(dataframe["macd"] < dataframe["macdsignal"]),
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(dataframe["&-up_or_down"] < -0.005) # Higher threshold for stronger exit signals
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dataframe["rsi"] > dataframe["sell_rsi_pred"],
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dataframe["close"] < dataframe["bb_middleband"],
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dataframe["macd"] < dataframe["macdsignal"],
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dataframe["&-up_or_down"] < -0.005
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]
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# Require conditions to be met for two consecutive candles
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exit_signal = (dataframe[exit_long_conditions].sum(axis=1) >= 3) & (
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dataframe[exit_long_conditions].shift(1).sum(axis=1) >= 3
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)
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# Combine conditions into a DataFrame
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conditions_df = pd.concat(exit_long_conditions, axis=1)
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# Require confirmation: at least 3 conditions met for two consecutive candles
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exit_signal = (conditions_df.sum(axis=1) >= 3) & (conditions_df.shift(1).sum(axis=1) >= 3)
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dataframe.loc[exit_signal, "exit_long"] = 1
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return dataframe
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@ -279,9 +279,7 @@ class FreqaiExampleStrategy(IStrategy):
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self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time, entry_tag, side: str, **kwargs
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) -> bool:
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"""
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Confirm trade entry to avoid slippage.
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"""
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"""Confirm trade entry to avoid slippage."""
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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