sma

freqtrade/templates/FreqaiExampleStrategy.py

@@ -72,32 +72,38 @@ class FreqaiExampleStrategy(IStrategy):
     }
 
     def featcaure_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
-        # 保留关键的技术指标
+        # 计算关键指标
         dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
-
-        # 计算短期和长期 SMA
+        
+        # 计算短期和长期SMA
         dataframe["sma_short"] = ta.SMA(dataframe, timeperiod=12)
         dataframe["sma_long"] = ta.SMA(dataframe, timeperiod=26)
-
-        # 检查 SMA 列是否存在
-        if "sma_short" not in dataframe.columns or "sma_long" not in dataframe.columns:
-            logger.error("SMA 列缺失，无法生成买入信号")
-            raise ValueError("DataFrame 缺少必要的 SMA 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
+        
+        # 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
         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
+        
+        # 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
         dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
-
+        dataframe["volume_pct"] = dataframe["volume"] / dataframe["volume_ma"]
+        
+        # 价格变化特征
+        dataframe["pct_change"] = dataframe["close"].pct_change()
+        dataframe["pct_change_5"] = dataframe["close"].pct_change(5)
+        dataframe["pct_change_10"] = dataframe["close"].pct_change(10)
+        
         # 数据清理
         for col in dataframe.columns:
             if dataframe[col].dtype in ["float64", "int64"]:
@@ -142,34 +148,36 @@ class FreqaiExampleStrategy(IStrategy):
             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["target"] = (
                 dataframe["close"].shift(-label_period) - dataframe["close"]
             ) / dataframe["close"]
 
+            # 添加辅助目标变量
+            dataframe["target_5"] = (
+                dataframe["close"].shift(-5) - dataframe["close"]
+            ) / dataframe["close"]
+            
+            dataframe["target_10"] = (
+                dataframe["close"].shift(-10) - 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)
+            for col in ["target", "target_5", "target_10"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
+                dataframe[col] = dataframe[col].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)
+            if dataframe["target"].ndim == 1:
+                dataframe["target"] = dataframe["target"].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
+            # 生成波动率特征
             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
+            for col in ["target", "target_5", "target_10", "%-volatility"]:
                 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
+                dataframe[col] = dataframe[col].ffill()
+                dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
                 if dataframe[col].isna().any():
                     logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值")
 
@@ -178,8 +186,8 @@ class FreqaiExampleStrategy(IStrategy):
             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()}")
+        logger.info(f"目标列形状：{dataframe['target'].shape}")
+        logger.info(f"目标列预览：\n{dataframe[['target', 'target_5', 'target_10']].head().to_string()}")
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: