stable1 11
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zhangkun9038@dingtalk.com
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@ -1,38 +1,222 @@
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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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minimal_roi = {}
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stoploss = 0.0
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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
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buy_rsi = IntParameter(low=10, high=50, default=30, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=70, space="sell", optimize=True, load=True)
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roi_0 = DecimalParameter(low=0.01, high=0.1, default=0.05, space="roi", optimize=True, load=True)
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roi_15 = DecimalParameter(low=0.005, high=0.05, default=0.03, space="roi", optimize=True, load=True)
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roi_30 = DecimalParameter(low=0.001, high=0.03, default=0.01, space="roi", optimize=True, load=True)
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stoploss_param = DecimalParameter(low=-0.2, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
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# FreqAI 配置
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freqai_info = {
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"model": "XGBoostRegressor",
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"return_type": "raw", # 确保使用原始回归输出
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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"],
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"label_period_candles": 20, # 增加预测周期
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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": {"nu": 0.1},
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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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"model_training_parameters": {
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"n_estimators": 300, # 增加迭代次数
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"learning_rate": 0.03, # 降低学习率
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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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"verbose": 0,
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},
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"data_kitchen": {
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"feature_parameters": {
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"DI_threshold": 0 # 禁用 DI 过滤
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}
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}
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}
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def calculate_macd(self, dataframe: DataFrame) -> DataFrame:
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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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dataframe["macdsignal"] = macd["macdsignal"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
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logger.info("MACD calculated successfully.")
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except Exception as e:
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logger.error(f"Error calculating MACD: {str(e)}")
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dataframe["macd"] = np.nan
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dataframe["macdsignal"] = np.nan
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 改进买入信号条件
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# 使用 centralized 方法计算 MACD
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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# 增加趋势性特征
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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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dataframe["ema50"] = ta.EMA(dataframe["close"], timeperiod=50)
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dataframe["atr"] = ta.ATR(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
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dataframe["obv"] = ta.OBV(dataframe["close"], dataframe["volume"])
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dataframe["adx"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
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dataframe["price_sma_diff"] = (dataframe["close"] - dataframe["sma20"]) / dataframe["sma20"]
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dataframe["momentum"] = ta.MOM(dataframe["close"], 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["bb_width"] = (bollinger["upper"] - bollinger["lower"]) / bollinger["mid"]
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# 添加相关交易对特征
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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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dataframe["btc_rsi"] = ta.RSI(btc_data, timeperiod=14)
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dataframe["btc_price_change"] = btc_data["close"].pct_change()
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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).ffill().fillna(dataframe[col].median())
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logger.info(f"Feature engineering completed, features: {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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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).ffill().fillna(dataframe[col].median())
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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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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).ffill().fillna(dataframe[col].median())
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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"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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raise ValueError("DataFrame missing 'close' column")
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try:
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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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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dataframe["&-up_or_down"] = dataframe["&-up_or_down"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
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for col in ["&-buy_rsi", "&-up_or_down", "%-volatility"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].median())
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except Exception as e:
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logger.error(f"Failed to create FreqAI targets: {str(e)}")
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raise
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logger.info(f"Target column shape: {dataframe['&-up_or_down'].shape}")
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logger.info(f"Target preview:\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"Processing pair: {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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dataframe["atr"] = ta.ATR(dataframe["high"], dataframe["low"], dataframe["close"], 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 = self.calculate_macd(dataframe)
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# 再次检查 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 信号列仍然缺失，无法生成买入信号。")
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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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# 动态参数设置
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if "&-buy_rsi" in dataframe.columns:
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dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 20
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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dataframe["&-stoploss"] = -2 * dataframe["atr"] / dataframe["close"]
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dataframe["&-roi_0"] = (dataframe["close"] / dataframe["close"].shift(10) - 1).clip(0, 0.1).fillna(0)
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enter_long_conditions = [
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(dataframe["rsi"] < dataframe["buy_rsi_pred"]), # RSI 低于买入阈值
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(
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(dataframe["volume"] > dataframe["volume"].rolling(window=10).mean() * 1.2) | # 成交量高于近期均值20%
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(dataframe["volume"] > dataframe["volume"].shift(1) * 1.1) # 当前成交量高于上一周期的10%
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),
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(
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(dataframe["close"] > dataframe["bb_middleband"]) | # 价格高于布林带中轨
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(dataframe["close"] > dataframe["bb_upperband"].shift(1)) # 当前价格高于上一周期的布林带上轨
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)
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]
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# 动态预测值
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dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(20, 50).fillna(dataframe["rsi"].median())
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dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20
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dataframe["stoploss_pred"] = -2 * dataframe["atr"] / dataframe["close"].clip(-0.2, -0.05)
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dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.1).fillna(dataframe["&-roi_0"].mean())
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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 dataframe.columns and "macdsignal" in dataframe.columns:
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enter_long_conditions.append((dataframe["macd"] > dataframe["macdsignal"]))
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# 确保模型预测为买入
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enter_long_conditions.append((dataframe["do_predict"] == 1))
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if enter_long_conditions:
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dataframe.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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for col in ["&-stoploss", "&-roi_0", "buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].mean())
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# 设置策略参数
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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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self.stoploss = float(dataframe["stoploss_pred"].iloc[-1])
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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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# 动态追踪止损
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self.trailing_stop_positive = float(2 * dataframe["atr"].iloc[-1] / dataframe["close"].iloc[-1])
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self.trailing_stop_positive_offset = float(3 * dataframe["atr"].iloc[-1] / dataframe["close"].iloc[-1])
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dataframe.ffill(inplace=True)
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dataframe.fillna(dataframe.mean(numeric_only=True), inplace=True)
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logger.info(f"&-up_or_down stats:\n{dataframe['&-up_or_down'].describe().to_string()}")
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> 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() * 1.05),
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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"] > 0.003) # 降低阈值
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]
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if enter_long_conditions:
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dataframe.loc[reduce(lambda x, y: x & y, enter_long_conditions), ["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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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"] < -0.003)
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]
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if exit_long_conditions:
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dataframe.loc[reduce(lambda x, y: x | y, exit_long_conditions), "exit_long"] = 1
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return dataframe
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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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@ -44,4 +228,3 @@
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return False
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return True
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