diff --git a/config_examples/config_freqai.okx.json b/config_examples/config_freqai.okx.json index e5bfe03f..4a25f045 100644 --- a/config_examples/config_freqai.okx.json +++ b/config_examples/config_freqai.okx.json @@ -62,13 +62,14 @@ ], "freqai": { "enabled": true, + "model_path": "/freqtrade/user_data/models", "data_kitchen": { "fillna": "ffill" }, "freqaimodel": "XGBoostRegressor", "model_training_parameters": { - "n_estimators": 200, - "learning_rate": 0.05, + "n_estimators": 300, + "learning_rate": 0.03, "max_depth": 6, "subsample": 0.8, "colsample_bytree": 0.8, @@ -77,7 +78,7 @@ "eval_metric": "rmse", "early_stopping_rounds": 20 }, - "train_period_days": 365, + "train_period_days": 500, "backtest_period_days": 90, "live_retrain_hours": 0, "feature_selection": { @@ -86,7 +87,7 @@ "feature_parameters": { "include_timeframes": ["15m", "1h", "4h"], "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], - "label_period_candles": 10, + "label_period_candles": 20, "include_shifted_candles": 2, "weight_factor": 0.9, "principal_component_analysis": false, @@ -123,4 +124,3 @@ } } - diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 0511c82f..c83c3ab3 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -1,326 +1,3 @@ -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 featcaure_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) - - def calculate_macd(self, dataframe: DataFrame) -> DataFrame: - """ - Centralized method to calculate MACD and ensure proper assignment to the dataframe. - """ - try: - macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) - dataframe["macd"] = macd["macd"] - dataframe["macdsignal"] = macd["macdsignal"] - - # Ensure no NaN or infinite values in MACD columns - dataframe["macd"] = dataframe["macd"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0) - dataframe["macdsignal"] = dataframe["macdsignal"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0) - - logger.info("MACD 列已成功计算并验证。") - except Exception as e: - logger.error(f"计算 MACD 列时出错：{str(e)}") - dataframe["macd"] = np.nan - dataframe["macdsignal"] = np.nan - - return dataframe - - # 使用 centralized 方法计算 MACD - dataframe = self.calculate_macd(dataframe) - - # 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。尝试重新计算 MACD 列。") - try: - macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) - dataframe["macd"] = macd["macd"] - dataframe["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。") - 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 - exit_long_conditions = [ - (dataframe["rsi"] > dataframe["sell_rsi_pred"]), # RSI 高于卖出阈值 - (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean()), # 成交量高于近期均值 - (dataframe["close"] < dataframe["bb_middleband"]), # 价格低于布林带中轨 - (dataframe["close"] < dataframe["bb_lowerband"].shift(1)), # 当前价格低于上一周期的布林带下轨 - (dataframe["volume"] < dataframe["volume"].shift(1) * 0.9) # 当前成交量低于上一周期的10% - ] - 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, dataframe: DataFrame, metadata: dict) -> DataFrame: -# 改进卖出信号条件 - exit_long_conditions = [ - (dataframe["rsi"] > dataframe["sell_rsi_pred"]), # RSI 高于卖出阈值 - ( - (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean()) | # 成交量高于近期均值 - (dataframe["volume"] < dataframe["volume"].shift(1) * 0.9) # 当前成交量低于上一周期的10% - ), - ( - (dataframe["close"] < dataframe["bb_middleband"]) | # 价格低于布林带中轨 - (dataframe["close"] < dataframe["bb_lowerband"].shift(1)) # 当前价格低于上一周期的布林带下轨 - ) - ] - if exit_long_conditions: - dataframe.loc[ - reduce(lambda x, y: x & y, exit_long_conditions), - "exit_long" - ] = 1 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 改进买入信号条件 @@ -367,3 +44,4 @@ class FreqaiExampleStrategy(IStrategy): return False return True +