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337 lines
16 KiB
Python
337 lines
16 KiB
Python
import logging
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import numpy as np
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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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from technical import qtpylib
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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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trailing_stop = True
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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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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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# 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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"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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},
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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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},
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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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"objective": "reg:squarederror",
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"eval_metric": "rmse",
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"early_stopping_rounds": 20,
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"verbose": 0,
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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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}
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}
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}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
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"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
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"&-stoploss": {"&-stoploss": {"color": "purple"}},
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"&-roi_0": {"&-roi_0": {"color": "orange"}},
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"do_predict": {"do_predict": {"color": "brown"}},
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},
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}
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def featcaure_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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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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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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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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# 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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# 保留布林带相关特征
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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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dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
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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], np.nan)
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dataframe[col] = dataframe[col].ffill().fillna(0)
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logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}")
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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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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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# 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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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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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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try:
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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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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# 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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# 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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# 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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# 生成 %-volatility 特征
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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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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# 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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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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# 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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# 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)
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# 生成 up_or_down 信号（非 FreqAI 目标）
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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# 使用未来价格变化方向生成 up_or_down 信号
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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dataframe["up_or_down"] = np.where(
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dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
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)
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# 动态设置参数
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if "&-buy_rsi" in dataframe.columns:
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# 派生其他目标
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dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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# Ensure proper calculation and handle potential NaN values
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dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1)
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dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0)
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# Additional check to ensure no NaN values remain
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for col in ["&-stoploss", "&-roi_0"]:
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if dataframe[col].isna().any():
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logger.warning(f"列 {col} 仍包含 NaN，填充为默认值")
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dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0)
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# 简化动态参数生成逻辑
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# 放松 buy_rsi 和 sell_rsi 的生成逻辑
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# 计算 buy_rsi_pred 并清理 NaN 值
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dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50)
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dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median())
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# 计算 sell_rsi_pred 并清理 NaN 值
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dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20
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dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median())
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# 计算 stoploss_pred 并清理 NaN 值
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dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
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dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean())
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# 计算 roi_0_pred 并清理 NaN 值
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dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
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dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean())
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# 检查预测值
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for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
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if dataframe[col].isna().any():
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logger.warning(f"列 {col} 包含 NaN，填充为默认值")
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dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
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# 更保守的止损和止盈设置
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dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2)
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dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
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# 设置策略级参数
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self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
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self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
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# 更保守的止损设置
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self.stoploss = -0.15 # 固定止损 15%
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self.minimal_roi = {
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0: float(self.roi_0.value),
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15: float(self.roi_15.value),
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30: float(self.roi_30.value),
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60: 0
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}
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# 更保守的追踪止损设置
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self.trailing_stop_positive = 0.05 # 追踪止损触发点
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self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量
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logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
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f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
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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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logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
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logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}")
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return dataframe
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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# 改进卖出信号条件
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exit_long_conditions = [
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(df["rsi"] > df["sell_rsi_pred"]), # RSI 高于卖出阈值
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(df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值
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(df["close"] < df["bb_middleband"]) # 价格低于布林带中轨
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]
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if exit_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, exit_long_conditions),
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"exit_long"
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] = 1
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return df
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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# 改进买入信号条件
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# 检查 MACD 列是否存在
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if "macd" not in df.columns or "macdsignal" not in df.columns:
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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。")
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try:
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macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9)
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df["macd"] = macd["macd"]
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df["macdsignal"] = macd["macdsignal"]
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logger.info("MACD 列已成功重新计算。")
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except Exception as e:
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logger.error(f"重新计算 MACD 列时出错：{str(e)}")
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raise ValueError("DataFrame 缺少必要的 MACD 列且无法重新计算。")
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enter_long_conditions = [
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(df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值
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(df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20%
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(df["close"] > df["bb_middleband"]) # 价格高于布林带中轨
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]
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# 如果 MACD 列存在，则添加 MACD 金叉条件
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if "macd" in df.columns and "macdsignal" in df.columns:
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enter_long_conditions.append((df["macd"] > df["macdsignal"]))
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# 确保模型预测为买入
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enter_long_conditions.append((df["do_predict"] == 1))
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions),
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["enter_long", "enter_tag"]
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] = (1, "long")
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return df
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def confirm_trade_entry(
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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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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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if rate > (last_candle["close"] * (1 + 0.0025)):
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return False
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return True
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