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zhangkun9038@dingtalk.com 2025-05-07 23:31:55 +08:00
parent 971318fda3
commit 95a125f67f
1 changed files with 25 additions and 25 deletions
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freqtrade/templates/FreqaiExampleStrategy.py
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@ -84,39 +84,36 @@ class FreqaiExampleStrategy(IStrategy):
logger.info(f"FreqAI 模型路径：{freqai_model_path}")
def _normalize_column(self, series: pd.Series) -> pd.Series:
"""对单个列进行最小最大归一化"""
return (series - series.min()) / (series.max() - series.min()).fillna(0)
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
# 保留关键的技术指标
# 基础指标
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["macd"], dataframe["macdsignal"], dataframe["macdhist"] = ta.MACD(
dataframe, fastperiod=12, slowperiod=26, signalperiod=9
)
# 强制计算 MACD 并确保列存在
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe["macd"] = macd["macd"].fillna(0)
dataframe["macdsignal"] = macd["macdsignal"].fillna(0)
# 确保列存在
assert "macd" in dataframe.columns and "macdsignal" in dataframe.columns, \
"MACD 列缺失，无法继续执行"
# 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
# 布林带及其宽度
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["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
# 保留成交量相关特征
dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
# ATR (Average True Range)
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
# RSI 变化率
dataframe["rsi_gradient"] = dataframe["rsi"].diff().fillna(0)
# 数据清理与归一化
for col in dataframe.select_dtypes(include=[np.number]).columns:
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
dataframe[col] = dataframe[col].ffill().fillna(0)
dataframe[f"{col}_norm"] = self._normalize_column(dataframe[col])
# 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
@ -133,7 +130,10 @@ class FreqaiExampleStrategy(IStrategy):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
# 数据清理逻辑
dataframe["%-volume_change"] = dataframe["volume"].pct_change(periods=5)
dataframe["%-price_momentum"] = dataframe["close"] / dataframe["close"].shift(20) - 1
# 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)