phyer/myTestFreqAI
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主要修改点：

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1. 修复了方法名拼写错误（featcaure -> feature）
2. 增加了基础数据检查
3. 添加了更多技术指标（动量、ROC、波动率等）
4. 增加了特征数量检查
5. 添加了更详细的日志记录
This commit is contained in:
zhangkun9038@dingtalk.com 2025-04-29 17:38:59 +08:00
parent 5df9f23baf
commit 5027018fe0
1 changed files with 22 additions and 14 deletions
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36
freqtrade/templates/FreqaiExampleStrategy.py
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@ -71,31 +71,27 @@ class FreqaiExampleStrategy(IStrategy):
},
}
def featcaure_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
# 计算关键指标
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
# 确保基础价格数据存在
if "close" not in dataframe.columns:
raise ValueError("Dataframe must contain 'close' column")
# 计算技术指标
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
# 计算短期和长期SMA
dataframe["sma_short"] = ta.SMA(dataframe, timeperiod=12)
dataframe["sma_long"] = ta.SMA(dataframe, timeperiod=26)
dataframe["sma_cross"] = np.where(dataframe["sma_short"] > dataframe["sma_long"], 1, -1)
# 计算SMA交叉信号
dataframe["sma_cross"] = np.where(
dataframe["sma_short"] > dataframe["sma_long"], 1, -1
)
# 计算布林带
# 布林带
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"]
# 计算价格与布林带的关系
dataframe["bb_pct"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
)
# 成交量相关特征
# 成交量特征
dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
dataframe["volume_pct"] = dataframe["volume"] / dataframe["volume_ma"]
@ -104,13 +100,25 @@ class FreqaiExampleStrategy(IStrategy):
dataframe["pct_change_5"] = dataframe["close"].pct_change(5)
dataframe["pct_change_10"] = dataframe["close"].pct_change(10)
# 动量指标
dataframe["momentum"] = dataframe["close"] / dataframe["close"].shift(4) - 1
dataframe["roc"] = ta.ROC(dataframe, timeperiod=10)
# 波动率
dataframe["volatility"] = dataframe["close"].pct_change().rolling(window=20).std()
# 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)}")
# 确保至少有一个特征
if len(dataframe.columns) == 0:
raise ValueError("No features generated in feature engineering")
logger.info(f"特征工程完成，生成特征数量：{len(dataframe.columns)}")
logger.debug(f"特征列表：{list(dataframe.columns)}")
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: