backtesting 通过了, 继续优化

config_examples/config_freqai.okx.json

@@ -67,7 +67,7 @@
         "freqaimodel": "CatboostClassifier",
         "purge_old_models": 2,
         "train_period_days": 15,
-        "identifier": "test58",
+        "identifier": "test62",
         "train_period_days": 30,
         "backtest_period_days": 10,
         "live_retrain_hours": 0,

docker-compose.yml

@@ -49,23 +49,24 @@ services:
     #     --strategy FreqaiExampleStrategy
     #     --timerange 20250310-20250410
     #     --export trades
-    command: >
-      hyperopt
-        --logfile /freqtrade/user_data/logs/freqtrade.log
-        --freqaimodel LightGBMRegressor
-        --config /freqtrade/config_examples/config_freqai.okx.json
-        --strategy-path /freqtrade/templates
-        --strategy FreqaiExampleStrategy
-        --timerange 20250301-20250420
-        --hyperopt-loss SharpeHyperOptLoss
-        --spaces roi stoploss 
-        -e 200
-
     # command: >
-    #    backtesting
+    #   hyperopt
     #     --logfile /freqtrade/user_data/logs/freqtrade.log
     #     --freqaimodel LightGBMRegressor
     #     --config /freqtrade/config_examples/config_freqai.okx.json
     #     --strategy-path /freqtrade/templates
     #     --strategy FreqaiExampleStrategy
     #     --timerange 20250301-20250420
+    #     --hyperopt-loss SharpeHyperOptLoss
+    #     --spaces roi stoploss 
+    #     -e 200
+
+    command: >
+       backtesting
+        --logfile /freqtrade/user_data/logs/freqtrade.log
+        --freqaimodel LightGBMRegressor
+        --config /freqtrade/config_examples/config_freqai.okx.json
+        --config /freqtrade/templates/FreqaiExampleStrategy.json
+        --strategy-path /freqtrade/templates
+        --strategy FreqaiExampleStrategy
+        --timerange 20240920-20250420

freqtrade/templates/FreqaiExampleStrategy.py

@@ -79,7 +79,7 @@ class FreqaiExampleStrategy(IStrategy):
             dataframe["volume"] / dataframe["volume"].rolling(period).mean()
         )
         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
 
@@ -156,9 +156,21 @@ class FreqaiExampleStrategy(IStrategy):
             
             # 限制预测值，添加平滑
             dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5).mean().clip(10, 50)
+            dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].mean(), inplace=True)
+            if dataframe["buy_rsi_pred"].isna().any():
+                logger.warning("buy_rsi_pred 列包含 NaN，已填充为默认值")
             dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5).mean().clip(50, 90)
+            dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].mean(), inplace=True)
+            if dataframe["sell_rsi_pred"].isna().any():
+                logger.warning("sell_rsi_pred 列包含 NaN，已填充为默认值")
             dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.35, -0.1)
+            dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean(), inplace=True)
+            if dataframe["stoploss_pred"].isna().any():
+                logger.warning("stoploss_pred 列包含 NaN，已填充为默认值")
             dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
+            dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean(), inplace=True)
+            if dataframe["roi_0_pred"].isna().any():
+                logger.warning("roi_0_pred 列包含 NaN，已填充为默认值")
                         
             # 检查预测值
             for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
@@ -175,10 +187,10 @@ class FreqaiExampleStrategy(IStrategy):
             self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
             self.stoploss = float(self.stoploss_param.value)
             self.minimal_roi = {
-                "0": float(self.roi_0.value),
+                0: float(self.roi_0.value),
-                "15": float(self.roi_15.value),
+                15: float(self.roi_15.value),
-                "30": float(self.roi_30.value),
+                30: float(self.roi_30.value),
-                "60": 0
+                60: 0
             }
             self.trailing_stop_positive = float(dataframe["trailing_stop_positive"].iloc[-1])
             self.trailing_stop_positive_offset = float(dataframe["trailing_stop_positive_offset"].iloc[-1])