主要修改点：

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

### **4. 测试优化后的策略**

@@ -0,0 +1,5 @@
+
+<<<<<<< HEAD
+=======
+freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20230101-20230401
+>>>>>>> Snippet

.gitignore

@@ -73,7 +73,9 @@ coverage.xml
 *.pot
 
 # Django stuff:
-*.log
+#*.log
+!outout_filted.log
+
 local_settings.py
 
 # Flask stuff:

03a54a5b0ae8efd49daf13c408a37a2d3c7816bf.diff

@@ -0,0 +1,264 @@
+diff --git "a/4. **\346\270\205\347\220\206\347\274\223\345\255\230**\357\274\232" "b/4. **\346\270\205\347\220\206\347\274\223\345\255\230**\357\274\232"
+new file mode 100644
+index 0000000..3bb7671
+--- /dev/null
++++ "b/4. **\346\270\205\347\220\206\347\274\223\345\255\230**\357\274\232"	
+@@ -0,0 +1,5 @@
++
++<<<<<<< HEAD
++=======
++   rm -rf /freqtrade/user_data/models/test62/
++>>>>>>> Snippet
+diff --git "a/5. **\351\207\215\346\226\260\350\256\255\347\273\203**\357\274\232" "b/5. **\351\207\215\346\226\260\350\256\255\347\273\203**\357\274\232"
+new file mode 100644
+index 0000000..8a18d3d
+--- /dev/null
++++ "b/5. **\351\207\215\346\226\260\350\256\255\347\273\203**\357\274\232"	
+@@ -0,0 +1,5 @@
++
++<<<<<<< HEAD
++=======
++   freqtrade trade --config config_examples/config_freqai.okx.json --strategy FreqaiExampleStrategy
++>>>>>>> Snippet
+diff --git a/config_examples/config_freqai.okx.json b/config_examples/config_freqai.okx.json
+index 1816983..4535010 100644
+--- a/config_examples/config_freqai.okx.json
++++ b/config_examples/config_freqai.okx.json
+@@ -67,45 +67,31 @@
+         "freqaimodel": "CatboostClassifier",
+         "purge_old_models": 2,
+         "train_period_days": 15,
+-        "identifier": "test62",
+-        "train_period_days": 30,
+-        "backtest_period_days": 10,
++        "train_period_days": 180,
++        "backtest_period_days": 60,
+         "live_retrain_hours": 0,
+         "feature_selection": {
+           "method": "recursive_elimination"
+         },
+         "feature_parameters": {
+-            "include_timeframes": [
+-                "3m",
+-                "15m",
+-                "1h"
+-            ],
+-            "include_corr_pairlist": [
+-                "BTC/USDT",
+-                "SOL/USDT"
+-            ],
+-            "label_period_candles": 20,
+-            "include_shifted_candles": 2,
+-            "DI_threshold": 0.9,
++            "include_timeframes": ["15m"],
++            "include_corr_pairlist": ["BTC/USDT"],
++            "label_period_candles": 10,
++            "include_shifted_candles": 1,
++            "DI_threshold": 0.7,
+             "weight_factor": 0.9,
+             "principal_component_analysis": false,
+             "use_SVM_to_remove_outliers": false,
+-            "indicator_periods_candles": [
+-                10,
+-                20,
+-                50
+-            ],
+-            "plot_feature_importances": 0
++            "indicator_periods_candles": [14],
+         },
+         "data_split_parameters": {
+             "test_size": 0.2
+         },
+-        "model_training_parameters": {
+-            "n_estimators": 100,
+-            "learning_rate": 0.05,
+-            "max_depth": 5,
+-            "num_leaves": 31
+-        }
++      "model_training_parameters": {
++          "n_estimators": 100,
++          "learning_rate": 0.05,
++          "max_depth": 5
++      }
+     },
+     "api_server": {
+         "enabled": true,
+diff --git a/docker-compose.yml b/docker-compose.yml
+index defe81e..aeb31e6 100644
+--- a/docker-compose.yml
++++ b/docker-compose.yml
+@@ -64,7 +64,7 @@ services:
+     command: >
+        backtesting
+         --logfile /freqtrade/user_data/logs/freqtrade.log
+-        --freqaimodel LightGBMRegressor
++        --freqaimodel XGBoostRegressor
+         --config /freqtrade/config_examples/config_freqai.okx.json
+         --config /freqtrade/templates/FreqaiExampleStrategy.json
+         --strategy-path /freqtrade/templates
+diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
+index 688e644..e27e17b 100644
+--- a/freqtrade/templates/FreqaiExampleStrategy.py
++++ b/freqtrade/templates/FreqaiExampleStrategy.py
+@@ -30,12 +30,12 @@ class FreqaiExampleStrategy(IStrategy):
+ 
+     # FreqAI 配置
+     freqai_info = {
+-        "model": "XGBoostRegressor",  # 改用XGBoost
++        "model": "CatboostClassifier",  # 与config保持一致
+         "feature_parameters": {
+-            "include_timeframes": ["5m", "15m", "1h"],
+-            "include_corr_pairlist": [],
+-            "label_period_candles": 12,
+-            "include_shifted_candles": 3,
++            "include_timeframes": ["3m", "15m", "1h"],  # 与config一致
++            "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"],  # 添加相关交易对
++            "label_period_candles": 20,  # 与config一致
++            "include_shifted_candles": 2,  # 与config一致
+         },
+         "data_split_parameters": {
+             "test_size": 0.2,
+@@ -72,54 +72,26 @@ class FreqaiExampleStrategy(IStrategy):
+     }
+ 
+     def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
+-        # 添加更多技术指标
++        # 保留关键的技术指标
+         dataframe["%-rsi"] = ta.RSI(dataframe, timeperiod=14)
+-        dataframe["%-mfi"] = ta.MFI(dataframe, timeperiod=14)
+-        dataframe["%-sma"] = ta.SMA(dataframe, timeperiod=20)
+-        dataframe["%-ema"] = ta.EMA(dataframe, timeperiod=20)
+-        dataframe["%-adx"] = ta.ADX(dataframe, timeperiod=14)
+-        dataframe["%-atr"] = ta.ATR(dataframe, timeperiod=14)
+-        dataframe["%-obv"] = ta.OBV(dataframe)
+-        dataframe["%-cci"] = ta.CCI(dataframe, timeperiod=20)
+-        dataframe["%-stoch"] = ta.STOCH(dataframe)['slowk']
+-        dataframe["%-macd"] = ta.MACD(dataframe)['macd']
+-        dataframe["%-macdsignal"] = ta.MACD(dataframe)['macdsignal']
+-        dataframe["%-macdhist"] = ta.MACD(dataframe)['macdhist']
+-        dataframe["%-willr"] = ta.WILLR(dataframe, timeperiod=14)
+-        dataframe["%-ultosc"] = ta.ULTOSC(dataframe)
+-        dataframe["%-trix"] = ta.TRIX(dataframe, timeperiod=14)
+-        dataframe["%-ad"] = ta.ADOSC(dataframe)
+-        dataframe["%-mom"] = ta.MOM(dataframe, timeperiod=10)
+-        dataframe["%-roc"] = ta.ROC(dataframe, timeperiod=10)
+-        
+-        # 添加布林带相关特征
++        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
++        dataframe["%-macd"] = macd["macd"]
++        dataframe["%-macdsignal"] = macd["macdsignal"]
++
++        # 保留布林带相关特征
+         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"]
+         dataframe["bb_pct"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+-        
+-        # 添加成交量相关特征
++
++        # 保留成交量相关特征
+         dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
+         dataframe["volume_roc"] = dataframe["volume"].pct_change(periods=10)
+-        dataframe["volume_obv"] = ta.OBV(dataframe)
+-        
+-        # 添加价格相关特征
+-        dataframe["close_ma"] = dataframe["close"].rolling(window=20).mean()
++
++        # 保留价格变化率
+         dataframe["close_roc"] = dataframe["close"].pct_change(periods=10)
+-        dataframe["close_log_ret"] = np.log(dataframe["close"]).diff()
+-        dataframe["close_zscore"] = (dataframe["close"] - dataframe["close"].rolling(window=20).mean()) / dataframe["close"].rolling(window=20).std()
+-        
+-        # 添加时间相关特征
+-        dataframe["hour"] = dataframe["date"].dt.hour
+-        dataframe["day_of_week"] = dataframe["date"].dt.dayofweek
+-        dataframe["is_weekend"] = dataframe["day_of_week"].isin([5, 6]).astype(int)
+-        
+-        # 添加波动率相关特征
+-        dataframe["volatility"] = dataframe["close"].pct_change().rolling(window=20).std()
+-        dataframe["volatility_ma"] = dataframe["volatility"].rolling(window=20).mean()
+-        dataframe["volatility_roc"] = dataframe["volatility"].pct_change(periods=10)
+         
+         # 改进数据清理
+         for col in dataframe.columns:
+@@ -170,16 +142,23 @@ class FreqaiExampleStrategy(IStrategy):
+ 
+         try:
+             label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
++
++            # 生成更复杂的目标变量 up_or_down
++            dataframe["up_or_down"] = np.where(
++                dataframe["close"].shift(-label_period) > dataframe["close"], 1, 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)
++
+             # 生成 %-volatility 特征
+             dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+ 
+-            # 单一回归目标
+-            # 移除对未来的数据依赖
+             # 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"]:
++            for col in ["&-buy_rsi", "up_or_down", "%-volatility"]:
+                 # 使用直接操作避免链式赋值
+                 dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
+                 dataframe[col] = dataframe[col].ffill()  # 替代 fillna(method='ffill')
+@@ -187,19 +166,13 @@ class FreqaiExampleStrategy(IStrategy):
+                 if dataframe[col].isna().any():
+                     logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值")
+ 
+-            # 数据清理
+-            for col in ["&-buy_rsi", "%-volatility"]:
+-                # 使用直接操作避免链式赋值
+-                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
+-                dataframe[col] = dataframe[col].ffill()  # 替代 fillna(method='ffill')
+-                dataframe[col] = dataframe[col].fillna(0)
+-                if dataframe[col].isna().any():
+-                    logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
+         except Exception as e:
+             logger.error(f"创建 FreqAI 目标失败：{str(e)}")
+             raise
+ 
+-        logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
++        # 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()}")
+         return dataframe
+ 
+     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+@@ -237,13 +210,13 @@ class FreqaiExampleStrategy(IStrategy):
+                     dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0)
+             
+             # 简化动态参数生成逻辑
+-# 简化 buy_rsi 和 sell_rsi 的生成逻辑
++        # 放松 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"].rolling(window=10).mean().clip(30, 50)
+             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["buy_rsi_pred"] + 40
+             dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].mean())
+ 
+             # 计算 stoploss_pred 并清理 NaN 值
+@@ -308,8 +281,9 @@ class FreqaiExampleStrategy(IStrategy):
+         # 改进买入信号条件
+         enter_long_conditions = [
+             (df["rsi"] < df["buy_rsi_pred"]),  # RSI 低于买入阈值
+-            (df["volume"] > df["volume"].rolling(window=10).mean()),  # 成交量高于近期均值
+-            (df["close"] > df["bb_middleband"])  # 价格高于布林带中轨
++            (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2),  # 成交量高于近期均值20%
++            (df["close"] > df["bb_middleband"]),  # 价格高于布林带中轨
++            (df["do_predict"] == 1)  # 确保模型预测为买入
+         ]
+         if enter_long_conditions:
+             df.loc[

4. **清理缓存**：

@@ -0,0 +1,5 @@
+
+<<<<<<< HEAD
+=======
+   rm -rf /freqtrade/user_data/models/test62/
+>>>>>>> Snippet

5. **重新训练**：

@@ -0,0 +1,5 @@
+
+<<<<<<< HEAD
+=======
+   freqtrade trade --config config_examples/config_freqai.okx.json --strategy FreqaiExampleStrategy
+>>>>>>> Snippet

config_examples/8f4c374e6b1b993b493d29202dbf28e6b59ccf61-4e7e5f23638ea53874c913e30bcc979e82d49b45.diff

@@ -0,0 +1,121 @@
+diff --git a/config_examples/config_freqai.okx.json b/config_examples/config_freqai.okx.json
+index 259459e..c2693fc 100644
+--- a/config_examples/config_freqai.okx.json
++++ b/config_examples/config_freqai.okx.json
+@@ -5,11 +5,10 @@
+     "max_open_trades": 4,
+     "stake_currency": "USDT",
+     "stake_amount": 150,
+-    "startup_candle_count": 30,
+     "tradable_balance_ratio": 1,
+     "fiat_display_currency": "USD",
+     "dry_run": true,
+-    "timeframe": "5m",
++    "timeframe": "3m",
+     "dry_run_wallet": 1000,
+     "cancel_open_orders_on_exit": true,
+     "stoploss": -0.05,
+@@ -24,21 +23,21 @@
+         "enable_ws": false,
+         "ccxt_config": {
+             "enableRateLimit": true,
+-            "rateLimit": 800,
++            "rateLimit": 500,
+             "options": {
+                 "defaultType": "spot"
+             }
+         },
+         "ccxt_async_config": {
+             "enableRateLimit": true,
+-	          "rateLimit": 800,
++	          "rateLimit": 500,
+             "timeout": 20000
+         },
+         "pair_whitelist": [
+-            "OKB/USDT",
+-            "DOT/USDT",
++            "BTC/USDT",
+             "SOL/USDT"
+-        ]
++        ],
++        "pair_blacklist": []
+     },
+     "entry_pricing": {
+         "price_side": "same",
+@@ -65,47 +64,37 @@
+         "data_kitchen": {
+             "fillna": "ffill"
+         },
+-        "freqaimodel": "CatboostClassifier",
+-        "purge_old_models": 2,
+-        "identifier": "test175",
+-        "train_period_days": 30,
+-        "backtest_period_days": 10,
++        "freqaimodel": "XGBoostRegressor",
++        "model_training_parameters": {
++            "n_estimators": 100,
++            "learning_rate": 0.05,
++            "max_depth": 5
++        },
++        "train_period_days": 180,
++        "backtest_period_days": 60,
+         "live_retrain_hours": 0,
+         "feature_selection": {
+           "method": "recursive_elimination"
+         },
+         "feature_parameters": {
+-            "include_timeframes": [
+-                "5m",
+-                "1h"
+-            ],
+-            "include_corr_pairlist": [
+-                "BTC/USDT",
+-                "ETH/USDT"
+-            ],
+-            "label_period_candles": 12,
+-            "include_shifted_candles": 3,
+-            "DI_threshold": 0.9,
++            "include_timeframes": ["15m"],
++            "include_corr_pairlist": ["BTC/USDT"],
++            "label_period_candles": 10,
++            "include_shifted_candles": 1,
++
+             "weight_factor": 0.9,
+             "principal_component_analysis": false,
+             "use_SVM_to_remove_outliers": false,
+-            "indicator_periods_candles": [
+-                10,
+-                20,
+-                50
+-            ],
+-            "plot_feature_importances": 0
++            "indicator_periods_candles": [14],
+         },
+         "data_split_parameters": {
+-            "test_size": 0.2,
+-            "shuffle": false,
++            "test_size": 0.2
+         },
+-        "model_training_parameters": {
+-            "n_estimators": 100,
+-            "learning_rate": 0.1,
+-            "num_leaves": 15,
+-            "verbose": -1
+-        }
++      "model_training_parameters": {
++          "n_estimators": 100,
++          "learning_rate": 0.05,
++          "max_depth": 5
++      }
+     },
+     "api_server": {
+         "enabled": true,
+@@ -123,7 +112,7 @@
+     "initial_state": "running",
+     "force_entry_enable": false,
+     "internals": {
+-        "process_throttle_secs": 10,
++        "process_throttle_secs": 5,
+         "heartbeat_interval": 20,
+         "loglevel": "DEBUG"
+     }

config_examples/config_freqai.okx.json

@@ -5,6 +5,7 @@
     "max_open_trades": 4,
     "stake_currency": "USDT",
     "stake_amount": 150,
+    "startup_candle_count": 30,
     "tradable_balance_ratio": 1,
     "fiat_display_currency": "USD",
     "dry_run": true,
@@ -62,49 +63,45 @@
     "freqai": {
         "enabled": true,
         "data_kitchen": {
-            "fillna": "ffill"
+            "fillna": "ffill",
+            "feature_parameters": {
+                "DI_threshold": 0.9,
+                "weight_factor": 0.9
+            }
         },
-        "freqaimodel": "CatboostClassifier",
+        "freqaimodel": "XGBoostRegressor",
         "purge_old_models": 2,
-        "train_period_days": 15,
-        "identifier": "test62",
+        "identifier": "test175",
         "train_period_days": 30,
         "backtest_period_days": 10,
         "live_retrain_hours": 0,
         "feature_selection": {
-          "method": "recursive_elimination"
+            "method": "recursive_elimination",
+            "threshold": 0.01
         },
         "feature_parameters": {
-            "include_timeframes": [
-                "3m",
-                "15m",
-                "1h"
-            ],
-            "include_corr_pairlist": [
-                "BTC/USDT",
-                "SOL/USDT"
-            ],
-            "label_period_candles": 20,
-            "include_shifted_candles": 2,
-            "DI_threshold": 0.9,
-            "weight_factor": 0.9,
-            "principal_component_analysis": false,
-            "use_SVM_to_remove_outliers": false,
-            "indicator_periods_candles": [
-                10,
-                20,
-                50
-            ],
-            "plot_feature_importances": 0
+            "include_timeframes": ["3m", "5m", "1h"],
+            "include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
+            "label_period_candles": 12,
+            "include_shifted_candles": 3,
+            "indicator_periods_candles": [10, 20, 50],
+            "plot_feature_importances": 1
         },
         "data_split_parameters": {
-            "test_size": 0.2
+            "test_size": 0.2,
+            "shuffle": true,
+            "random_state": 42
         },
         "model_training_parameters": {
-            "n_estimators": 100,
+            "n_estimators": 200,
             "learning_rate": 0.05,
             "max_depth": 5,
-            "num_leaves": 31
+            "subsample": 0.8,
+            "colsample_bytree": 0.8,
+            "objective": "reg:squarederror",
+            "eval_metric": "rmse",
+            "early_stopping_rounds": 50,
+            "verbose": 0
         }
     },
     "api_server": {

config_examples/xgboostregression-ok1-kandipai.diff

@@ -0,0 +1,122 @@
+diff --git a/config_examples/config_freqai.okx.json b/config_examples/config_freqai.okx.json
+index 259459e..c8f04af 100644
+--- a/config_examples/config_freqai.okx.json
++++ b/config_examples/config_freqai.okx.json
+@@ -5,11 +5,10 @@
+     "max_open_trades": 4,
+     "stake_currency": "USDT",
+     "stake_amount": 150,
+-    "startup_candle_count": 30,
+     "tradable_balance_ratio": 1,
+     "fiat_display_currency": "USD",
+     "dry_run": true,
+-    "timeframe": "5m",
++    "timeframe": "3m",
+     "dry_run_wallet": 1000,
+     "cancel_open_orders_on_exit": true,
+     "stoploss": -0.05,
+@@ -24,21 +23,21 @@
+         "enable_ws": false,
+         "ccxt_config": {
+             "enableRateLimit": true,
+-            "rateLimit": 800,
++            "rateLimit": 500,
+             "options": {
+                 "defaultType": "spot"
+             }
+         },
+         "ccxt_async_config": {
+             "enableRateLimit": true,
+-	          "rateLimit": 800,
++	          "rateLimit": 500,
+             "timeout": 20000
+         },
+         "pair_whitelist": [
+-            "OKB/USDT",
+-            "DOT/USDT",
++            "BTC/USDT",
+             "SOL/USDT"
+-        ]
++        ],
++        "pair_blacklist": []
+     },
+     "entry_pricing": {
+         "price_side": "same",
+@@ -65,47 +64,38 @@
+         "data_kitchen": {
+             "fillna": "ffill"
+         },
+-        "freqaimodel": "CatboostClassifier",
+-        "purge_old_models": 2,
+-        "identifier": "test175",
+-        "train_period_days": 30,
+-        "backtest_period_days": 10,
++        "freqaimodel": "XGBoostRegressor",
++        "model_training_parameters": {
++            "n_estimators": 100,
++            "learning_rate": 0.05,
++            "max_depth": 5
++        },
++        "train_period_days": 15,
++        "train_period_days": 180,
++        "backtest_period_days": 60,
+         "live_retrain_hours": 0,
+         "feature_selection": {
+           "method": "recursive_elimination"
+         },
+         "feature_parameters": {
+-            "include_timeframes": [
+-                "5m",
+-                "1h"
+-            ],
+-            "include_corr_pairlist": [
+-                "BTC/USDT",
+-                "ETH/USDT"
+-            ],
+-            "label_period_candles": 12,
+-            "include_shifted_candles": 3,
+-            "DI_threshold": 0.9,
++            "include_timeframes": ["15m"],
++            "include_corr_pairlist": ["BTC/USDT"],
++            "label_period_candles": 10,
++            "include_shifted_candles": 1,
++
+             "weight_factor": 0.9,
+             "principal_component_analysis": false,
+             "use_SVM_to_remove_outliers": false,
+-            "indicator_periods_candles": [
+-                10,
+-                20,
+-                50
+-            ],
+-            "plot_feature_importances": 0
++            "indicator_periods_candles": [14],
+         },
+         "data_split_parameters": {
+-            "test_size": 0.2,
+-            "shuffle": false,
++            "test_size": 0.2
+         },
+-        "model_training_parameters": {
+-            "n_estimators": 100,
+-            "learning_rate": 0.1,
+-            "num_leaves": 15,
+-            "verbose": -1
+-        }
++      "model_training_parameters": {
++          "n_estimators": 100,
++          "learning_rate": 0.05,
++          "max_depth": 5
++      }
+     },
+     "api_server": {
+         "enabled": true,
+@@ -123,7 +113,7 @@
+     "initial_state": "running",
+     "force_entry_enable": false,
+     "internals": {
+-        "process_throttle_secs": 10,
++        "process_throttle_secs": 5,
+         "heartbeat_interval": 20,
+         "loglevel": "DEBUG"
+     }

docker-compose.yml

@@ -64,9 +64,10 @@ services:
     command: >
        backtesting
         --logfile /freqtrade/user_data/logs/freqtrade.log
-        --freqaimodel LightGBMRegressor
+        --freqaimodel XGBoostRegressor
         --config /freqtrade/config_examples/config_freqai.okx.json
         --config /freqtrade/templates/FreqaiExampleStrategy.json
         --strategy-path /freqtrade/templates
         --strategy FreqaiExampleStrategy
-        --timerange 20240920-20250420
+        --timerange 20250401-20250420
+        --cache none

filter.py

@@ -0,0 +1,7 @@
+with open('output.log', 'r') as input_file, open('output_filted.log', 'w') as output_file:
+    for line in input_file:
+        if "validation_0" in line:
+            if "[99]" in line:
+                output_file.write(line)
+        else:
+            output_file.write(line)

freqtrade/templates/FreqaiExampleHybridStrategy.py

@@ -1,311 +0,0 @@
-import logging
-
-import numpy as np  # noqa
-import pandas as pd  # noqa
-import talib.abstract as ta
-from pandas import DataFrame
-from technical import qtpylib
-
-from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair 
-
-
-logger = logging.getLogger(__name__)
-
-
-class FreqaiExampleHybridStrategy(IStrategy):
-    """
-    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
-    FreqAI to bolster a typical Freqtrade strategy.
-
-    Launching this strategy would be:
-
-    freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
-    --freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
-
-    or the user simply adds this to their config:
-
-    "freqai": {
-        "enabled": true,
-        "purge_old_models": 2,
-        "train_period_days": 15,
-        "identifier": "unique-id",
-        "feature_parameters": {
-            "include_timeframes": [
-                "3m",
-                "15m",
-                "1h"
-            ],
-            "include_corr_pairlist": [
-                "BTC/USDT",
-                "ETH/USDT"
-            ],
-            "label_period_candles": 20,
-            "include_shifted_candles": 2,
-            "DI_threshold": 0.9,
-            "weight_factor": 0.9,
-            "principal_component_analysis": false,
-            "use_SVM_to_remove_outliers": true,
-            "indicator_periods_candles": [10, 20]
-        },
-        "data_split_parameters": {
-            "test_size": 0,
-            "random_state": 1
-        },
-        "model_training_parameters": {
-            "n_estimators": 800
-        }
-    },
-
-    Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
-    """
-
-    minimal_roi = {
-        # "120": 0.0,  # exit after 120 minutes at break even
-        "60": 0.01,
-        "30": 0.02,
-        "0": 0.04,
-    }
-
-    plot_config = {
-        "main_plot": {
-            "tema": {},
-        },
-        "subplots": {
-            "MACD": {
-                "macd": {"color": "blue"},
-                "macdsignal": {"color": "orange"},
-            },
-            "RSI": {
-                "rsi": {"color": "red"},
-            },
-            "Up_or_down": {
-                "&s-up_or_down": {"color": "green"},
-            },
-        },
-    }
-
-    process_only_new_candles = True
-    stoploss = -0.05
-    use_exit_signal = True
-    startup_candle_count: int = 30
-    can_short = False
-
-    # Hyperoptable parameters
-    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
-    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
-
-    def feature_engineering_expand_all(
-        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
-    ) -> DataFrame:
-        """
-        *Only functional with FreqAI enabled strategies*
-        This function will automatically expand the defined features on the config defined
-        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
-        `include_corr_pairs`. In other words, a single feature defined in this function
-        will automatically expand to a total of
-        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
-        `include_corr_pairs` numbers of features added to the model.
-
-        All features must be prepended with `%` to be recognized by FreqAI internals.
-
-        More details on how these config defined parameters accelerate feature engineering
-        in the documentation at:
-
-        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
-
-        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
-
-        :param dataframe: strategy dataframe which will receive the features
-        :param period: period of the indicator - usage example:
-        :param metadata: metadata of current pair
-        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
-        """
-
-        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
-        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
-        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
-        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
-        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
-
-        bollinger = qtpylib.bollinger_bands(
-            qtpylib.typical_price(dataframe), window=period, stds=2.2
-        )
-        dataframe["bb_lowerband-period"] = bollinger["lower"]
-        dataframe["bb_middleband-period"] = bollinger["mid"]
-        dataframe["bb_upperband-period"] = bollinger["upper"]
-
-        dataframe["%-bb_width-period"] = (
-            dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
-        ) / dataframe["bb_middleband-period"]
-        dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
-
-        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
-
-        dataframe["%-relative_volume-period"] = (
-            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
-        )
-
-        return dataframe
-
-    def feature_engineering_expand_basic(
-        self, dataframe: DataFrame, metadata: dict, **kwargs
-    ) -> DataFrame:
-        """
-        *Only functional with FreqAI enabled strategies*
-        This function will automatically expand the defined features on the config defined
-        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
-        In other words, a single feature defined in this function
-        will automatically expand to a total of
-        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
-        numbers of features added to the model.
-
-        Features defined here will *not* be automatically duplicated on user defined
-        `indicator_periods_candles`
-
-        All features must be prepended with `%` to be recognized by FreqAI internals.
-
-        More details on how these config defined parameters accelerate feature engineering
-        in the documentation at:
-
-        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
-
-        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
-
-        :param dataframe: strategy dataframe which will receive the features
-        :param metadata: metadata of current pair
-        dataframe["%-pct-change"] = dataframe["close"].pct_change()
-        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
-        """
-        dataframe["%-pct-change"] = dataframe["close"].pct_change()
-        dataframe["%-raw_volume"] = dataframe["volume"]
-        dataframe["%-raw_price"] = dataframe["close"]
-        return dataframe
-
-    def feature_engineering_standard(
-        self, dataframe: DataFrame, metadata: dict, **kwargs
-    ) -> DataFrame:
-        """
-        *Only functional with FreqAI enabled strategies*
-        This optional function will be called once with the dataframe of the base timeframe.
-        This is the final function to be called, which means that the dataframe entering this
-        function will contain all the features and columns created by all other
-        freqai_feature_engineering_* functions.
-
-        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
-        This function is a good place for any feature that should not be auto-expanded upon
-        (e.g. day of the week).
-
-        All features must be prepended with `%` to be recognized by FreqAI internals.
-
-        More details about feature engineering available:
-
-        https://www.freqtrade.io/en/latest/freqai-feature-engineering
-
-        :param dataframe: strategy dataframe which will receive the features
-        :param metadata: metadata of current pair
-        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
-        """
-        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
-        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
-        return dataframe
-    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
-        logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
-        logger.info(f"DataFrame shape: {dataframe.shape}")
-        logger.info(f"Available columns: {list(dataframe.columns)}")
-        logger.info(f"First few rows:\n{dataframe[['date', 'close']].head().to_string()}")
-        
-        if "close" not in dataframe.columns:
-            logger.error("Required 'close' column missing in dataframe")
-            raise ValueError("Required 'close' column missing in dataframe")
-        
-        if len(dataframe) < 50:
-            logger.error(f"Insufficient data: {len(dataframe)} rows, need at least 50 for shift(-50)")
-            raise ValueError("Insufficient data for target calculation")
-        
-        try:
-            # 生成数值型标签：1 表示上涨，0 表示下跌
-            dataframe["&-up_or_down"] = np.where(
-                dataframe["close"].shift(-50) > dataframe["close"],
-                1.0,  # 数值型标签
-                0.0
-            )
-        except Exception as e:
-            logger.error(f"Failed to create &-up_or_down column: {str(e)}")
-            raise
-        
-        logger.info(f"Target column head:\n{dataframe[['&-up_or_down']].head().to_string()}")
-        
-        if "&-up_or_down" not in dataframe.columns:
-            logger.error("FreqAI failed to generate the &-up_or_down column")
-            raise KeyError("FreqAI failed to generate the &-up_or_down column")
-        
-        logger.info("FreqAI targets set successfully")
-        return dataframe
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        logger.info(f"Processing pair: {metadata['pair']}")
-        logger.info(f"Input DataFrame shape: {dataframe.shape}")
-        logger.info(f"Input DataFrame columns: {list(dataframe.columns)}")
-        logger.info(f"Input DataFrame head:\n{dataframe[['date', 'close', 'volume']].head().to_string()}")
-        
-        # Ensure FreqAI processing
-        logger.info("Calling self.freqai.start")
-        try:
-            dataframe = self.freqai.start(dataframe, metadata, self)
-        except Exception as e:
-            logger.error(f"self.freqai.start failed: {str(e)}")
-            raise
-        logger.info("self.freqai.start completed")
-        
-        logger.info(f"Output DataFrame shape: {dataframe.shape}")
-        logger.info(f"Output DataFrame columns: {list(dataframe.columns)}")
-        # Safely log columns that exist
-        available_columns = [col for col in ['date', 'close', '&-up_or_down'] if col in dataframe.columns]
-        logger.info(f"Output DataFrame head:\n{dataframe[available_columns].head().to_string()}")
-        
-        if "&-up_or_down" not in dataframe.columns:
-            logger.error("FreqAI did not generate the required &-up_or_down column")
-            raise KeyError("FreqAI did not generate the required &-up_or_down column")
-
-        # RSI
-        dataframe["rsi"] = ta.RSI(dataframe)
-
-        # Bollinger Bands
-        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_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
-            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
-        )
-        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
-            "bb_middleband"
-        ]
-
-        # TEMA
-        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
-
-        return dataframe
-    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        df.loc[
-            (
-                (qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
-                & (df["tema"] <= df["bb_middleband"])
-                & (df["tema"] > df["tema"].shift(1))
-                & (df["volume"] > 0)
-            ),
-            "enter_long",
-        ] = 1
-        return df
-
-    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        df.loc[
-            (
-                (qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
-                & (df["tema"] > df["bb_middleband"])
-                & (df["tema"] < df["tema"].shift(1))
-                & (df["volume"] > 0)
-            ),
-            "exit_long",
-        ] = 1
-        return df 

freqtrade/templates/FreqaiExampleStrategy.json

@@ -1,32 +0,0 @@
-{
-  "strategy_name": "FreqaiExampleStrategy",
-  "params": {
-    "trailing": {
-      "trailing_stop": true,
-      "trailing_stop_positive": 0.01,
-      "trailing_stop_positive_offset": 0.02,
-      "trailing_only_offset_is_reached": false
-    },
-    "max_open_trades": {
-      "max_open_trades": 4
-    },
-    "buy": {
-      "buy_rsi": 39.92672300850069
-    },
-    "sell": {
-      "sell_rsi": 69.92672300850067
-    },
-    "protection": {},
-    "roi": {
-      "0": 0.132,
-      "8": 0.047,
-      "14": 0.007,
-      "60": 0
-    },
-    "stoploss": {
-      "stoploss": -0.322
-    }
-  },
-  "ft_stratparam_v": 1,
-  "export_time": "2025-04-23 12:30:05.550433+00:00"
-}

freqtrade/templates/FreqaiExampleStrategy.py

@@ -30,23 +30,34 @@ class FreqaiExampleStrategy(IStrategy):
 
     # FreqAI 配置
     freqai_info = {
-        "model": "LightGBMRegressor",
+        "model": "XGBoostRegressor",  # 与config保持一致
         "feature_parameters": {
-            "include_timeframes": ["5m", "15m", "1h"],
-            "include_corr_pairlist": [],
-            "label_period_candles": 12,
-            "include_shifted_candles": 3,
+            "include_timeframes": ["3m", "15m", "1h"],  # 与config一致
+            "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"],  # 添加相关交易对
+            "label_period_candles": 20,  # 与config一致
+            "include_shifted_candles": 2,  # 与config一致
         },
         "data_split_parameters": {
             "test_size": 0.2,
-            "shuffle": False,
+            "shuffle": True,  # 启用shuffle
         },
         "model_training_parameters": {
-            "n_estimators": 100,
-            "learning_rate": 0.1,
-            "num_leaves": 31,
-            "verbose": -1,
+            "n_estimators": 100,  # 减少树的数量
+            "learning_rate": 0.1,  # 提高学习率
+            "max_depth": 6,  # 限制树深度
+            "subsample": 0.8,  # 添加子采样
+            "colsample_bytree": 0.8,  # 添加特征采样
+            "objective": "reg:squarederror",
+            "eval_metric": "rmse",
+            "early_stopping_rounds": 20,
+            "verbose": 0,
         },
+        "data_kitchen": {
+            "feature_parameters": {
+                "DI_threshold": 1.5,  # 降低异常值过滤阈值
+                "use_DBSCAN_to_remove_outliers": False  # 禁用DBSCAN
+            }
+        }
     }
 
     plot_config = {
@@ -61,42 +72,77 @@ class FreqaiExampleStrategy(IStrategy):
     }
 
     def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
-        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
-        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
-        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
-        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
-        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
-        dataframe["bb_lowerband-period"] = bollinger["lower"]
-        dataframe["bb_middleband-period"] = bollinger["mid"]
-        dataframe["bb_upperband-period"] = bollinger["upper"]
-        dataframe["%-bb_width-period"] = (
-            dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
-        ) / dataframe["bb_middleband-period"]
-        dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
-        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
-        dataframe["%-relative_volume-period"] = (
-            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        # 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)
+        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)
+        
+        # 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"]
+        dataframe["bb_pct"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
+            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
         )
-        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
-        dataframe.ffill(inplace=True)
-        dataframe.fillna(0, inplace=True)
+        
+        # 成交量特征
+        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)
+        
+        # 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)
+        
+        # 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:
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-raw_volume"] = dataframe["volume"]
         dataframe["%-raw_price"] = dataframe["close"]
-        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
-        dataframe.fillna(method='ffill', inplace=True)
-        dataframe.fillna(0, inplace=True)
+# 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)
+                dataframe[col] = dataframe[col].ffill()
+                dataframe[col] = dataframe[col].fillna(0)
+                
+                # 检查是否仍有无效值
+                if dataframe[col].isna().any() or np.isinf(dataframe[col]).any():
+                    logger.warning(f"列 {col} 仍包含无效值，已填充为默认值")
+                    dataframe[col] = dataframe[col].fillna(0)
         return dataframe
 
     def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
         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
 
@@ -108,24 +154,48 @@ class FreqaiExampleStrategy(IStrategy):
 
         try:
             label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
-            # 生成 %-volatility 特征
+
+            # 定义目标变量为未来价格变化百分比（连续值）
+            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
+            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["target"].ndim == 1:
+                dataframe["target"] = dataframe["target"].values.reshape(-1, 1)
+
+            # 生成波动率特征
             dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
 
-            # 单一回归目标
-            dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14).shift(-label_period)
-            
             # 数据清理
-            for col in ["&-buy_rsi", "%-volatility"]:
-                dataframe[col].replace([np.inf, -np.inf], 0, inplace=True)
-                dataframe[col].fillna(method='ffill', inplace=True)
-                dataframe[col].fillna(0, inplace=True)
+            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()
+                dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
                 if dataframe[col].isna().any():
-                    logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
+                    logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值")
+
         except Exception as e:
             logger.error(f"创建 FreqAI 目标失败：{str(e)}")
             raise
 
-        logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
+        # Log the shape of the target variable for debugging
+        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:
@@ -142,6 +212,8 @@ class FreqaiExampleStrategy(IStrategy):
         
         # 生成 up_or_down 信号（非 FreqAI 目标）
         label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+        # 使用未来价格变化方向生成 up_or_down 信号
+        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
         dataframe["up_or_down"] = np.where(
             dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
         )
@@ -151,55 +223,64 @@ class FreqaiExampleStrategy(IStrategy):
             # 派生其他目标
             dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
             dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
-            dataframe["&-stoploss"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
-            dataframe["&-roi_0"] = (dataframe["close"].shift(-label_period) / dataframe["close"] - 1).clip(0, 0.2)
+            # Ensure proper calculation and handle potential NaN values
+            dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1)
+            dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0)
 
-            # 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")
+            # Additional check to ensure no NaN values remain
+            for col in ["&-stoploss", "&-roi_0"]:
+                if dataframe[col].isna().any():
+                    logger.warning(f"列 {col} 仍包含 NaN，填充为默认值")
+                    dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0)
+            
+            # 简化动态参数生成逻辑
+        # 放松 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["rsi"].rolling(window=10).mean().clip(30, 50)
+            dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median())
+
+            # 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"].median())
+
+            # 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"].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")
+            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())
             
-            # 动态追踪止盈
-            dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
-            dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.75).clip(0.02, 0.4)
+            # 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)
+            dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
             
             # 设置策略级参数
             self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
             self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
-            self.stoploss = float(self.stoploss_param.value)
+# 更保守的止损设置
+            self.stoploss = -0.15  # 固定止损 15%
             self.minimal_roi = {
                 0: float(self.roi_0.value),
                 15: float(self.roi_15.value),
                 30: float(self.roi_30.value),
                 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])
+# 更保守的追踪止损设置
+            self.trailing_stop_positive = 0.05  # 追踪止损触发点
+            self.trailing_stop_positive_offset = 0.1  # 追踪止损偏移量
             
             logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
                         f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
         
         dataframe.replace([np.inf, -np.inf], 0, inplace=True)
-        dataframe.fillna(method='ffill', inplace=True)
+        dataframe.ffill(inplace=True)
         dataframe.fillna(0, inplace=True)
         
         logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
@@ -207,28 +288,13 @@ class FreqaiExampleStrategy(IStrategy):
         
         return dataframe
 
-    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        enter_long_conditions = [
-            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
-            df["tema"] > df["tema"].shift(1),
-            df["volume"] > 0,
-            df["do_predict"] == 1,
-            df["up_or_down"] == 1
-        ]
-        if enter_long_conditions:
-            df.loc[
-                reduce(lambda x, y: x & y, enter_long_conditions),
-                ["enter_long", "enter_tag"]
-            ] = (1, "long")
-        return df
-
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        # 改进卖出信号条件
         exit_long_conditions = [
-            qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"]),
-            (df["close"] < df["close"].shift(1) * 0.97),
-            df["volume"] > 0,
-            df["do_predict"] == 1,
-            df["up_or_down"] == 0
+            (df["rsi"] > df["sell_rsi_pred"]),  # RSI 高于卖出阈值
+            (df["volume"] > df["volume"].rolling(window=10).mean()),  # 成交量高于近期均值
+            (df["close"] < df["bb_middleband"]),  # 价格低于布林带中轨
+            (df["sma_short"] < df["sma_long"])  # SMA 死叉（短期 SMA 下穿长期 SMA）
         ]
         if exit_long_conditions:
             df.loc[
@@ -236,7 +302,29 @@ class FreqaiExampleStrategy(IStrategy):
                 "exit_long"
             ] = 1
         return df
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        # 改进买入信号条件
+        # 计算短期和长期 SMA
+        df["sma_short"] = ta.SMA(df, timeperiod=12)
+        df["sma_long"] = ta.SMA(df, timeperiod=26)
 
+        enter_long_conditions = [
+            (df["rsi"] < df["buy_rsi_pred"]),  # RSI 低于买入阈值
+            (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2),  # 成交量高于近期均值20%
+            (df["close"] > df["bb_middleband"])  # 价格高于布林带中轨
+        ]
+
+        # 添加 SMA 金叉条件（短期 SMA 上穿长期 SMA）
+        enter_long_conditions.append((df["sma_short"] > df["sma_long"]))
+        
+        # 确保模型预测为买入
+        enter_long_conditions.append((df["do_predict"] == 1))
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions),
+                ["enter_long", "enter_tag"]
+            ] = (1, "long")
+        return df
     def confirm_trade_entry(
         self, pair: str, order_type: str, amount: float, rate: float,
         time_in_force: str, current_time, entry_tag, side: str, **kwargs

freqtrade/templates/aaa.md

@@ -0,0 +1,183 @@
+```python
+
+class FreqaiExampleStrategy(IStrategy):
+    minimal_roi = {}
+    stoploss = -0.1
+    trailing_stop = True
+    process_only_new_candles = True
+    use_exit_signal = True
+    startup_candle_count: int = 100  # 增加数据需求
+    can_short = False
+
+    # Hyperopt 参数
+    buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True)
+    sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True)
+    roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
+    roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
+    roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
+    stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
+
+    # 保护机制
+    protections = [
+        {"method": "StoplossGuard", "stop_duration": 60, "lookback_period": 120},
+        {"method": "MaxDrawdown", "lookback_period": 120, "max_allowed_drawdown": 0.05}
+    ]
+
+    # FreqAI 配置
+    freqai_info = {
+        "model": "LightGBMRegressor",
+        "feature_parameters": {
+            "include_timeframes": ["5m", "15m", "1h"],
+            "include_corr_pairlist": [],
+            "label_period_candles": 12,
+            "include_shifted_candles": 3,
+        },
+        "data_split_parameters": {
+            "test_size": 0.2,
+            "shuffle": False,
+            "n_splits": 5  # 添加交叉验证
+        },
+        "model_training_parameters": {
+            "n_estimators": 200,
+            "learning_rate": 0.05,
+            "num_leaves": 10,
+            "min_child_weight": 1,
+            "verbose": -1,
+        },
+    }
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
+        if "close" not in dataframe.columns:
+            logger.error("数据框缺少必要的 'close' 列")
+            raise ValueError("数据框缺少必要的 'close' 列")
+
+        try:
+            label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+            dataframe["&-buy_rsi"] = (dataframe["close"].shift(-label_period) / dataframe["close"] - 1) * 100  # 修改目标为收益率
+
+            for col in ["&-buy_rsi", "%-volatility"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
+                dataframe[col] = dataframe[col].ffill()
+                if dataframe[col].isna().any():
+                    logger.warning(f"目标列 {col} 包含 NaN，填充为 0")
+                    dataframe[col] = dataframe[col].fillna(0)
+                logger.info(f"目标列 {col} 统计：\n{dataframe[col].describe().to_string()}")
+        except Exception as e:
+            logger.error(f"创建 FreqAI 目标失败：{str(e)}")
+            raise
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        logger.info(f"处理交易对：{metadata['pair']}, 数据形状：{dataframe.shape}")
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        # 计算传统指标
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        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["tema"] = ta.TEMA(dataframe, timeperiod=9)
+
+        # 检查FreqAI预测列
+        if "&-buy_rsi_pred" in dataframe.columns:
+            logger.info(f"&-buy_rsi_pred 统计：均值={dataframe['&-buy_rsi_pred'].mean():.2f}, 标准差={dataframe['&-buy_rsi_pred'].std():.2f}")
+            dataframe["buy_rsi_trend"] = np.where(
+                dataframe["&-buy_rsi_pred"] > dataframe["&-buy_rsi_pred"].shift(1), 1, 0
+            )
+            dataframe["&-sell_rsi_pred"] = dataframe["&-buy_rsi_pred"] + 30
+            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+            dataframe["&-stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
+            dataframe["&-roi_0_pred"] = (dataframe["&-buy_rsi_pred"] / 1000).clip(0.01, 0.2)
+
+            for col in ["&-buy_rsi_pred", "&-sell_rsi_pred", "&-stoploss_pred", "&-roi_0_pred"]:
+                if dataframe[col].isna().any():
+                    logger.warning(f"列 {col} 包含 NaN，填充为默认值")
+                    mean_value = dataframe[col].mean()
+                    if pd.isna(mean_value):
+                        mean_value = {
+                            "&-buy_rsi_pred": 30,
+                            "&-sell_rsi_pred": 70,
+                            "&-stoploss_pred": -0.1,
+                            "&-roi_0_pred": 0.05
+                        }.get(col, 0)
+                    dataframe[col] = dataframe[col].fillna(mean_value)
+        else:
+            logger.warning(f"&-buy_rsi_pred 列缺失，使用默认值初始化")
+            dataframe["buy_rsi_trend"] = 0
+            dataframe["&-buy_rsi_pred"] = 30
+            dataframe["&-sell_rsi_pred"] = 70
+            dataframe["&-stoploss_pred"] = -0.1
+            dataframe["&-roi_0_pred"] = 0.05
+
+        # 动态参数设置
+        try:
+            last_valid_idx = dataframe["&-stoploss_pred"].last_valid_index()
+            if last_valid_idx is None:
+                raise ValueError("没有有效的预测数据")
+            self.stoploss = float(np.clip(dataframe["&-stoploss_pred"].iloc[last_valid_idx], -0.25, -0.05))
+            self.buy_rsi.value = int(np.clip(dataframe["&-buy_rsi_pred"].iloc[last_valid_idx], 10, 50))
+            self.sell_rsi.value = int(np.clip(dataframe["&-sell_rsi_pred"].iloc[last_valid_idx], 50, 90))
+            self.roi_0.value = float(np.clip(dataframe["&-roi_0_pred"].iloc[last_valid_idx], 0.01, 0.2))
+            self.minimal_roi = {
+                0: self.roi_0.value,
+                15: self.roi_15.value,
+                30: self.roi_30.value,
+                60: 0.0
+            }
+            logger.info(f"动态参数设置: buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, stoploss={self.stoploss:.2%}")
+        except Exception as e:
+            logger.error(f"动态参数设置失败，使用默认值: {str(e)}")
+            self.stoploss = -0.1
+            self.buy_rsi.value = 27
+            self.sell_rsi.value = 59
+            self.minimal_roi = {0: 0.038, 15: 0.027, 30: 0.009, 60: 0.0}
+
+        dataframe = dataframe.replace([np.inf, -np.inf], 0)
+        dataframe = dataframe.ffill()
+        dataframe = dataframe.fillna(0)
+
+        logger.info(f"do_predict 分布：\n{dataframe['do_predict'].value_counts().to_string()}")
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [
+            qtpylib.crossed_above(df["rsi"], df["&-buy_rsi_pred"] - 5),  # 放宽RSI条件
+            df["tema"] > df["tema"].shift(1),
+            df["volume"] > 0,
+            df["do_predict"] == 1
+        ]
+        if enter_long_conditions:
+            condition_met = reduce(lambda x, y: x & y, enter_long_conditions)
+            df.loc[condition_met, ["enter_long", "enter_tag"]] = (1, "long")
+            if condition_met.any():
+                logger.info(f"买入信号触发：{metadata['pair']}，时间={df.index[condition_met][-1]}")
+            else:
+                logger.debug(f"买入条件未满足：{metadata['pair']}，do_predict={df['do_predict'].iloc[-1]}, rsi={df['rsi'].iloc[-1]:.2f}, buy_rsi_pred={df['&-buy_rsi_pred'].iloc[-1]:.2f}")
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [
+            qtpylib.crossed_above(df["rsi"], df["&-sell_rsi_pred"]),
+            df["close"] < df["bb_lowerband"],
+            df["volume"] > 0,
+            df["do_predict"] == 1
+        ]
+        time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=2))
+        df.loc[
+            (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
+            "exit_long"
+        ] = 1
+        return df
+```
+```
+```
+```
+```
+```
+```
+```

output_filted.log

@@ -0,0 +1,387 @@
+Creating freqtrade_freqtrade_run ... 
+Creating freqtrade_freqtrade_run ... done
+2025-04-29 08:31:07,002 - freqtrade - INFO - freqtrade 2025.3
+2025-04-29 08:31:07,217 - numexpr.utils - INFO - NumExpr defaulting to 12 threads.
+2025-04-29 08:31:08,612 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ...
+2025-04-29 08:31:08,615 - freqtrade.loggers - INFO - Enabling colorized output.
+2025-04-29 08:31:08,615 - root - INFO - Logfile configured
+2025-04-29 08:31:08,615 - freqtrade.loggers - INFO - Verbosity set to 0
+2025-04-29 08:31:08,616 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates
+2025-04-29 08:31:08,616 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ...
+2025-04-29 08:31:08,617 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250401-20250420 ...
+2025-04-29 08:31:08,640 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ...
+2025-04-29 08:31:08,641 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ...
+2025-04-29 08:31:08,641 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ...
+2025-04-29 08:31:08,642 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250401-20250420
+2025-04-29 08:31:08,642 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor
+2025-04-29 08:31:08,643 - freqtrade.exchange.check_exchange - INFO - Checking exchange...
+2025-04-29 08:31:08,649 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team.
+2025-04-29 08:31:08,649 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration.
+2025-04-29 08:31:08,650 - freqtrade.configuration.config_validation - INFO - Validating configuration ...
+2025-04-29 08:31:08,652 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode
+2025-04-29 08:31:08,652 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled
+2025-04-29 08:31:08,653 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69
+2025-04-29 08:31:08,653 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}}
+2025-04-29 08:31:08,658 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000}
+2025-04-29 08:31:08,664 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX"
+2025-04-29 08:31:11,241 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'...
+2025-04-29 08:31:11,261 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'...
+2025-04-29 08:31:11,262 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json
+2025-04-29 08:31:11,263 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m.
+2025-04-29 08:31:11,263 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05.
+2025-04-29 08:31:11,264 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT.
+2025-04-29 08:31:11,264 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150.
+2025-04-29 08:31:11,264 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30.
+2025-04-29 08:31:11,265 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 
+'minutes'}.
+2025-04-29 08:31:11,265 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4.
+2025-04-29 08:31:11,266 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0}
+2025-04-29 08:31:11,266 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m
+2025-04-29 08:31:11,266 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05
+2025-04-29 08:31:11,267 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True
+2025-04-29 08:31:11,267 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01
+2025-04-29 08:31:11,267 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02
+2025-04-29 08:31:11,268 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False
+2025-04-29 08:31:11,268 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False
+2025-04-29 08:31:11,268 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True
+2025-04-29 08:31:11,269 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 
+'stoploss_on_exchange_interval': 60}
+2025-04-29 08:31:11,269 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'}
+2025-04-29 08:31:11,269 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT
+2025-04-29 08:31:11,270 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150
+2025-04-29 08:31:11,270 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30
+2025-04-29 08:31:11,270 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'}
+2025-04-29 08:31:11,271 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True
+2025-04-29 08:31:11,271 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False
+2025-04-29 08:31:11,272 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False
+2025-04-29 08:31:11,272 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0
+2025-04-29 08:31:11,272 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False
+2025-04-29 08:31:11,272 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0
+2025-04-29 08:31:11,273 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False
+2025-04-29 08:31:11,273 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1
+2025-04-29 08:31:11,273 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4
+2025-04-29 08:31:11,273 - freqtrade.configuration.config_validation - INFO - Validating configuration ...
+2025-04-29 08:31:11,277 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'...
+2025-04-29 08:31:11,283 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier).
+2025-04-29 08:31:11,284 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450
+2025-04-29 08:31:11,284 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ...
+2025-04-29 08:31:11,413 - freqtrade.optimize.backtesting - INFO - Loading data from 2025-03-01 21:30:00 up to 2025-04-20 00:00:00 (49 days).
+2025-04-29 08:31:11,414 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators
+2025-04-29 08:31:11,415 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy
+2025-04-29 08:31:12,973 - matplotlib.font_manager - INFO - generated new fontManager
+2025-04-29 08:31:13,178 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'...
+2025-04-29 08:31:13,178 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch
+2025-04-29 08:31:13,178 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch
+2025-04-29 08:31:13,179 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT']
+2025-04-29 08:31:13,179 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069
+2025-04-29 08:31:13,180 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067
+2025-04-29 08:31:13,180 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values.
+2025-04-29 08:31:13,183 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT
+2025-04-29 08:31:13,184 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges
+2025-04-29 08:31:13,186 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains
+2025-04-29 08:31:13,186 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at 
+/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather
+2025-04-29 08:31:13,221 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690
+2025-04-29 08:31:13,222 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:13,294 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770
+2025-04-29 08:31:13,295 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:13,354 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450
+2025-04-29 08:31:13,355 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:13,449 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690
+2025-04-29 08:31:13,449 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:13,521 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770
+2025-04-29 08:31:13,522 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:13,571 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
+2025-04-29 08:31:13,576 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,)
+2025-04-29 08:31:13,579 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.007116  50.010488
+1    0.005291  50.010488
+2    0.004416  50.010488
+3    0.002082  50.010488
+4    0.001904  50.010488
+2025-04-29 08:31:13,584 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
+2025-04-29 08:31:13,590 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
+2025-04-29 08:31:13,592 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.007116  49.846666
+1    0.005291  49.846666
+2    0.004416  49.846666
+3    0.002082  49.846666
+4    0.001904  49.846666
+2025-04-29 08:31:13,600 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600
+2025-04-29 08:31:13,600 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
+2025-04-29 08:31:13,622 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
+2025-04-29 08:31:13,623 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 --------------------
+2025-04-29 08:31:13,641 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
+2025-04-29 08:31:13,642 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning: [08:31:13] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+  warnings.warn(smsg, UserWarning)
+[99]	validation_0-rmse:0.13679	validation_1-rmse:0.11901
+2025-04-29 08:31:15,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (1.58 secs) --------------------
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:15] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+2025-04-29 08:31:15,422 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600--buy_rsi.html
+2025-04-29 08:31:15,423 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
+2025-04-29 08:31:15,442 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None
+2025-04-29 08:31:15,449 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains
+2025-04-29 08:31:15,449 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at 
+/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather
+2025-04-29 08:31:15,453 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
+2025-04-29 08:31:15,458 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
+2025-04-29 08:31:15,459 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.007116  49.846666
+1    0.005291  49.846666
+2    0.004416  49.846666
+3    0.002082  49.846666
+4    0.001904  49.846666
+2025-04-29 08:31:15,464 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
+2025-04-29 08:31:15,469 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,)
+2025-04-29 08:31:15,471 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.007116  50.074781
+1    0.005291  50.074781
+2    0.004416  50.074781
+3    0.002082  50.074781
+4    0.001904  50.074781
+2025-04-29 08:31:15,476 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600
+2025-04-29 08:31:15,476 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
+2025-04-29 08:31:15,494 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
+2025-04-29 08:31:15,495 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 --------------------
+2025-04-29 08:31:15,509 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
+2025-04-29 08:31:15,510 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:15] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+[99]	validation_0-rmse:0.13376	validation_1-rmse:0.11638
+2025-04-29 08:31:17,083 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (1.61 secs) --------------------
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:17] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+2025-04-29 08:31:17,121 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600--buy_rsi.html
+2025-04-29 08:31:17,122 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
+2025-04-29 08:31:17,144 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None
+2025-04-29 08:31:17,182 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05
+2025-04-29 08:31:17,189 - FreqaiExampleStrategy - INFO - up_or_down 值统计：
+up_or_down
+1    11845
+0    11726
+2025-04-29 08:31:17,190 - FreqaiExampleStrategy - INFO - do_predict 值统计：
+do_predict
+0.0    14451
+1.0     9120
+2025-04-29 08:31:17,192 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT
+2025-04-29 08:31:17,193 - freqtrade.freqai.freqai_interface - INFO - Training 2 timeranges
+2025-04-29 08:31:17,194 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 1/2 trains
+2025-04-29 08:31:17,195 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at 
+/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather
+2025-04-29 08:31:17,217 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690
+2025-04-29 08:31:17,217 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2025-03-01 19:50:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:17,281 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770
+2025-04-29 08:31:17,282 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2025-02-27 22:00:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:17,329 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450
+2025-04-29 08:31:17,329 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2025-03-01 21:30:00 to 2025-04-20 00:00:00
+2025-04-29 08:31:17,591 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
+2025-04-29 08:31:17,596 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,)
+2025-04-29 08:31:17,597 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.016595   49.72136
+1    0.012811   49.72136
+2    0.010135   49.72136
+3    0.008514   49.72136
+4    0.006242   49.72136
+2025-04-29 08:31:17,602 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
+2025-04-29 08:31:17,608 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
+2025-04-29 08:31:17,609 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.016595  49.562407
+1    0.012811  49.562407
+2    0.010135  49.562407
+3    0.008514  49.562407
+4    0.006242  49.562407
+2025-04-29 08:31:17,615 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600
+2025-04-29 08:31:17,616 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
+2025-04-29 08:31:17,644 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
+2025-04-29 08:31:17,645 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 --------------------
+2025-04-29 08:31:17,667 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
+2025-04-29 08:31:17,668 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:17] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+[99]	validation_0-rmse:0.11848	validation_1-rmse:0.10165
+2025-04-29 08:31:20,071 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (2.46 secs) --------------------
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:20] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+2025-04-29 08:31:20,104 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600--buy_rsi.html
+2025-04-29 08:31:20,105 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
+2025-04-29 08:31:20,136 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None
+2025-04-29 08:31:20,143 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 2/2 trains
+2025-04-29 08:31:20,144 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at 
+/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather
+2025-04-29 08:31:20,148 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
+2025-04-29 08:31:20,154 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
+2025-04-29 08:31:20,156 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.016595  49.562407
+1    0.012811  49.562407
+2    0.010135  49.562407
+3    0.008514  49.562407
+4    0.006242  49.562407
+2025-04-29 08:31:20,160 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
+2025-04-29 08:31:20,166 - FreqaiExampleStrategy - INFO - 目标列形状：(23570,)
+2025-04-29 08:31:20,167 - FreqaiExampleStrategy - INFO - 目标列预览：
+   up_or_down  &-buy_rsi
+0    0.016595  49.934347
+1    0.012811  49.934347
+2    0.010135  49.934347
+3    0.008514  49.934347
+4    0.006242  49.934347
+2025-04-29 08:31:20,172 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600
+2025-04-29 08:31:20,173 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
+2025-04-29 08:31:20,197 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
+2025-04-29 08:31:20,197 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 --------------------
+2025-04-29 08:31:20,219 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
+2025-04-29 08:31:20,220 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:20] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+[99]	validation_0-rmse:0.12679	validation_1-rmse:0.11178
+2025-04-29 08:31:22,488 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (2.31 secs) --------------------
+/home/ftuser/.local/lib/python3.12/site-packages/xgboost/core.py:158: UserWarning:
+
+[08:31:22] WARNING: /workspace/src/learner.cc:740: 
+Parameters: { "verbose" } are not used.
+
+
+2025-04-29 08:31:22,529 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600--buy_rsi.html
+2025-04-29 08:31:22,530 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
+2025-04-29 08:31:22,557 - datasieve.pipeline - WARNING - Could not find step di in pipeline, returning None
+2025-04-29 08:31:22,599 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05
+2025-04-29 08:31:22,606 - FreqaiExampleStrategy - INFO - up_or_down 值统计：
+up_or_down
+0    11865
+1    11706
+2025-04-29 08:31:22,607 - FreqaiExampleStrategy - INFO - do_predict 值统计：
+do_predict
+0.0    14451
+1.0     9120
+2025-04-29 08:31:22,610 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-04-01 00:00:00 up to 2025-04-20 00:00:00 (19 days).
+2025-04-29 08:31:23,193 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_08-31-23.meta.json"
+Result for strategy FreqaiExampleStrategy
+                                             BACKTESTING REPORT                                              
+┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃     Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ BTC/USDT │     15 │         0.04 │           0.801 │         0.08 │     12:58:00 │    2    13     0   100 │
+│ SOL/USDT │     13 │         -0.9 │         -17.573 │        -1.76 │      8:54:00 │    5     7     1  38.5 │
+│    TOTAL │     28 │         -0.4 │         -16.773 │        -1.68 │     11:05:00 │    7    20     1  25.0 │
+└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
+                                         LEFT OPEN TRADES REPORT                                          
+┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃  Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ TOTAL │      0 │          0.0 │           0.000 │          0.0 │         0:00 │    0     0     0     0 │
+└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
+                                                ENTER TAG STATS                                                
+┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│      long │      28 │         -0.4 │         -16.773 │        -1.68 │     11:05:00 │    7    20     1  25.0 │
+│     TOTAL │      28 │         -0.4 │         -16.773 │        -1.68 │     11:05:00 │    7    20     1  25.0 │
+└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
+                                                    EXIT REASON STATS                                                    
+┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│                roi │    27 │         0.14 │           5.757 │         0.58 │         9:48:00 │    7    20     0   100 │
+│ trailing_stop_loss │     1 │        -15.0 │         -22.529 │        -2.25 │ 1 day, 21:45:00 │    0     0     1     0 │
+│              TOTAL │    28 │         -0.4 │         -16.773 │        -1.68 │        11:05:00 │    7    20     1  25.0 │
+└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
+                                                                  MIXED TAG STATS                                                                   
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                      Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│                ('long', 'roi') │             │     27 │         0.14 │           5.757 │         0.58 │         9:48:00 │    7    20     0   100 │
+│ ('long', 'trailing_stop_loss') │             │      1 │        -15.0 │         -22.529 │        -2.25 │ 1 day, 21:45:00 │    0     0     1     0 │
+│                          TOTAL │             │     28 │         -0.4 │         -16.773 │        -1.68 │        11:05:00 │    7    20     1  25.0 │
+└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
+                   SUMMARY METRICS                   
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
+┃ Metric                      ┃ Value               ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Backtesting from            │ 2025-04-01 00:00:00 │
+│ Backtesting to              │ 2025-04-20 00:00:00 │
+│ Trading Mode                │ Spot                │
+│ Max open trades             │ 2                   │
+│                             │                     │
+│ Total/Daily Avg Trades      │ 28 / 1.47           │
+│ Starting balance            │ 1000 USDT           │
+│ Final balance               │ 983.227 USDT        │
+│ Absolute profit             │ -16.773 USDT        │
+│ Total profit %              │ -1.68%              │
+│ CAGR %                      │ -27.74%             │
+│ Sortino                     │ -100.00             │
+│ Sharpe                      │ -3.97               │
+│ Calmar                      │ -74.86              │
+│ SQN                         │ -0.73               │
+│ Profit factor               │ 0.26                │
+│ Expectancy (Ratio)          │ -0.60 (-0.74)       │
+│ Avg. daily profit %         │ -0.09%              │
+│ Avg. stake amount           │ 150 USDT            │
+│ Total trade volume          │ 8408.398 USDT       │
+│                             │                     │
+│ Best Pair                   │ BTC/USDT 0.08%      │
+│ Worst Pair                  │ SOL/USDT -1.76%     │
+│ Best trade                  │ SOL/USDT 1.41%      │
+│ Worst trade                 │ SOL/USDT -15.00%    │
+│ Best day                    │ 2.151 USDT          │
+│ Worst day                   │ -22.529 USDT        │
+│ Days win/draw/lose          │ 4 / 13 / 1          │
+│ Avg. Duration Winners       │ 0:51:00             │
+│ Avg. Duration Loser         │ 1 day, 21:45:00     │
+│ Max Consecutive Wins / Loss │ 3 / 10              │
+│ Rejected Entry signals      │ 0                   │
+│ Entry/Exit Timeouts         │ 0 / 0               │
+│                             │                     │
+│ Min balance                 │ 977.471 USDT        │
+│ Max balance                 │ 1000 USDT           │
+│ Max % of account underwater │ 2.25%               │
+│ Absolute Drawdown (Account) │ 2.25%               │
+│ Absolute Drawdown           │ 22.529 USDT         │
+│ Drawdown high               │ 0 USDT              │
+│ Drawdown low                │ -22.529 USDT        │
+│ Drawdown Start              │ 2025-04-02 08:24:00 │
+│ Drawdown End                │ 2025-04-06 23:15:00 │
+│ Market change               │ -0.80%              │
+└─────────────────────────────┴─────────────────────┘
+
+Backtested 2025-04-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2
+                                                               STRATEGY SUMMARY                                                                
+┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
+┃              Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃           Drawdown ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
+│ FreqaiExampleStrategy │     28 │        -0.40 │         -16.773 │        -1.68 │     11:05:00 │    7    20     1  25.0 │ 22.529 USDT  2.25% │
+└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴────────────────────┘

utf8.py

@@ -0,0 +1,18 @@
+import chardet
+import codecs
+
+def convert_encoding(input_file, output_file):
+    # 检测文件编码
+    with open(input_file, 'rb') as f:
+        result = chardet.detect(f.read())
+    encoding = result['encoding']
+
+    # 以检测到的编码读取文件内容，并以UTF-8编码写入新文件
+    with codecs.open(input_file, 'r', encoding=encoding) as infile, \
+         codecs.open(output_file, 'w', encoding='utf-8') as outfile:
+        outfile.write(infile.read())
+
+if __name__ == "__main__":
+    input_file = 'freqtrade.log'  # 替换为实际的输入文件名
+    output_file = 'freqtrade_utf8.log'  # 替换为你想要的输出文件名
+    convert_encoding(input_file, output_file)

请啊!

@@ -0,0 +1,233 @@
+import logging
+import numpy as np
+from functools import reduce
+import talib.abstract as ta
+from pandas import DataFrame
+from technical import qtpylib
+from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
+
+logger = logging.getLogger(__name__)
+
+class FreqaiExampleStrategy(IStrategy):
+    # 移除硬编码的 minimal_roi 和 stoploss，改为动态适配
+    minimal_roi = {}  # 将在 populate_indicators 中动态生成
+    stoploss = 0.0   # 将在 populate_indicators 中动态设置
+    trailing_stop = True
+    process_only_new_candles = True
+    use_exit_signal = True
+    startup_candle_count: int = 40
+    can_short = False
+
+    # 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化
+    buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True)
+    sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True)
+
+    # 为 Hyperopt 优化添加 ROI 和 stoploss 参数
+    roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
+    roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
+    roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
+    stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True)
+
+    # FreqAI 配置
+    freqai_info = {
+        "model": "LightGBMRegressor",
+        "feature_parameters": {
+            "include_timeframes": ["5m", "15m", "1h"],
+            "include_corr_pairlist": [],
+            "label_period_candles": 12,
+            "include_shifted_candles": 3,
+        },
+        "data_split_parameters": {
+            "test_size": 0.2,
+            "shuffle": False,
+        },
+        "model_training_parameters": {
+            "n_estimators": 100,
+            "learning_rate": 0.1,
+            "num_leaves": 31,
+            "verbose": -1,
+        },
+    }
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
+            "&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
+            "&-stoploss": {"&-stoploss": {"color": "purple"}},
+            "&-roi_0": {"&-roi_0": {"color": "orange"}},
+            "do_predict": {"do_predict": {"color": "brown"}},
+        },
+    }
+
+    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
+        dataframe.ffill(inplace=True)
+        dataframe.fillna(0, inplace=True)
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
+        dataframe.fillna(method='ffill', inplace=True)
+        dataframe.fillna(0, inplace=True)
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        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.fillna(0, inplace=True)
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
+        if "close" not in dataframe.columns:
+            logger.error("数据框缺少必要的 'close' 列")
+            raise ValueError("数据框缺少必要的 'close' 列")
+        
+        try:
+            label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+            # 生成 %-volatility 特征
+            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+            
+            # 单一回归目标
+            # 移除对未来的数据依赖
+            dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
+            
+            # 数据清理
+            for col in ["&-buy_rsi", "%-volatility"]:
+                dataframe[col].replace([np.inf, -np.inf], 0, inplace=True)
+                dataframe[col].fillna(method='ffill', inplace=True)
+                dataframe[col].fillna(0, inplace=True)
+                if dataframe[col].isna().any():
+                    logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
+        except Exception as e:
+            logger.error(f"创建 FreqAI 目标失败：{str(e)}")
+            raise
+
+        logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        logger.info(f"处理交易对：{metadata['pair']}")
+        dataframe = self.freqai.start(dataframe, metadata, self)
+        
+        # 计算传统指标
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        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["tema"] = ta.TEMA(dataframe, timeperiod=9)
+        
+        # 生成 up_or_down 信号（非 FreqAI 目标）
+        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+        # 使用历史数据生成 up_or_down 信号
+        dataframe["up_or_down"] = np.where(
+            dataframe["close"] > dataframe["close"].shift(1), 1, 0
+        )
+        
+        # 动态设置参数
+        if "&-buy_rsi" in dataframe.columns:
+            # 派生其他目标
+            dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
+            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+            dataframe["&-stoploss"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
+            dataframe["&-roi_0"] = (dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)
+            
+            # 简化动态参数生成逻辑
+            dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].clip(10, 50)
+            dataframe["sell_rsi_pred"] = dataframe["&-buy_rsi"] + 30
+            dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
+            dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
+                        
+            # 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)
+            
+            # 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)
+            dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
+            
+            # 设置策略级参数
+            self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
+            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),
+                15: float(self.roi_15.value),
+                30: float(self.roi_30.value),
+                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])
+            
+            logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
+                        f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
+        
+        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
+        dataframe.fillna(method='ffill', inplace=True)
+        dataframe.fillna(0, inplace=True)
+        
+        logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
+        logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}")
+        
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [
+            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
+            df["volume"] > 0
+        ]
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions),
+                ["enter_long", "enter_tag"]
+            ] = (1, "long")
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [
+            qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"]),
+            df["volume"] > 0
+        ]
+        if exit_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, exit_long_conditions),
+                "exit_long"
+            ] = 1
+        return df
+
+    def confirm_trade_entry(
+        self, pair: str, order_type: str, amount: float, rate: float,
+        time_in_force: str, current_time, entry_tag, side: str, **kwargs
+    ) -> bool:
+        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = df.iloc[-1].squeeze()
+        if side == "long":
+            if rate > (last_candle["close"] * (1 + 0.0025)):
+                return False
+        return True