计算 buy_rsi_pred 并清理 NaN 值

freqtrade/templates/FreqaiExampleStrategy.py

@@ -104,7 +104,7 @@ class FreqaiExampleStrategy(IStrategy):
         dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
         dataframe.replace([np.inf, -np.inf], 0, inplace=True)
-        dataframe.fillna(method='ffill', inplace=True)
+        dataframe.ffill(inplace=True)
         dataframe.fillna(0, inplace=True)
         return dataframe
 
@@ -167,16 +167,27 @@ class FreqaiExampleStrategy(IStrategy):
             
             # 简化动态参数生成逻辑
 # 简化 buy_rsi 和 sell_rsi 的生成逻辑
+            # 计算 buy_rsi_pred 并清理 NaN 值
             dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(window=10).mean().clip(20, 40)
+            dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].mean())
+
+            # 计算 sell_rsi_pred 并清理 NaN 值
             dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20
+            dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].mean())
+
+            # 计算 stoploss_pred 并清理 NaN 值
             dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
+            dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean())
+
+            # 计算 roi_0_pred 并清理 NaN 值
             dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
+            dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean())
                         
             # 检查预测值
             for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
                 if dataframe[col].isna().any():
                     logger.warning(f"列 {col} 包含 NaN，填充为默认值")
-                    dataframe[col].fillna(dataframe[col].mean(), inplace=True)
+            dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
             
             # 更保守的止损和止盈设置
             dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2)