up

### **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

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/config_freqai.okx.json

@@ -2,16 +2,16 @@
     "$schema": "https://schema.freqtrade.io/schema.json",
     "trading_mode": "spot",
     "margin_mode": "isolated",
-    "max_open_trades": 4,
+    "max_open_trades": 3,
     "stake_currency": "USDT",
-    "stake_amount": 150,
+    "stake_amount": 100,
     "tradable_balance_ratio": 1,
     "fiat_display_currency": "USD",
     "dry_run": true,
-    "timeframe": "3m",
+    "timeframe": "15m",
     "dry_run_wallet": 1000,
     "cancel_open_orders_on_exit": true,
-    "stoploss": -0.05,
+    "stoploss": -0.1,
     "unfilledtimeout": {
         "entry": 5,
         "exit": 15
@@ -35,7 +35,8 @@
         },
         "pair_whitelist": [
             "BTC/USDT",
-            "SOL/USDT"
+            "SOL/USDT",
+            "ETH/USDT"
         ],
         "pair_blacklist": []
     },
@@ -64,47 +65,40 @@
         "data_kitchen": {
             "fillna": "ffill"
         },
-        "freqaimodel": "CatboostClassifier",
-        "purge_old_models": 2,
-        "train_period_days": 15,
-        "identifier": "test62",
-        "train_period_days": 30,
-        "backtest_period_days": 10,
+        "freqaimodel": "XGBoostRegressor",
+        "model_training_parameters": {
+            "n_estimators": 200,
+            "learning_rate": 0.05,
+            "max_depth": 6,
+            "subsample": 0.8,
+            "colsample_bytree": 0.8,
+            "reg_lambda": 1.0,
+            "objective": "reg:squarederror",
+            "eval_metric": "rmse",
+            "early_stopping_rounds": 20
+        },
+        "train_period_days": 365,
+        "backtest_period_days": 90,
         "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_timeframes": ["15m", "1h", "4h"],
+            "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"],
+            "label_period_candles": 10,
             "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
+            "use_SVM_to_remove_outliers": true,
+            "SVM_parameters": {
+                "nu": 0.1
+            },
+            "indicator_periods_candles": [14, 20]
         },
         "data_split_parameters": {
-            "test_size": 0.2
-        },
-        "model_training_parameters": {
-            "n_estimators": 100,
-            "learning_rate": 0.05,
-            "max_depth": 5,
-            "num_leaves": 31
+            "test_size": 0.2,
+            "shuffle": true
         }
     },
     "api_server": {
@@ -128,3 +122,5 @@
         "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 20250320-20250420
+        --cache none

freqtrade/templates/FreqaiExampleHybridStrategy.py

@@ -52,7 +52,9 @@ class FreqaiExampleHybridStrategy(IStrategy):
             "random_state": 1
         },
         "model_training_parameters": {
-            "n_estimators": 800
+            "n_estimators": 200,
+            "max_depth": 5,
+            "learning_rate": 0.05
         }
     },
 
@@ -122,13 +124,11 @@ class FreqaiExampleHybridStrategy(IStrategy):
         """
 
         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
+            qtpylib.typical_price(dataframe), window=period, stds=2
         )
         dataframe["bb_lowerband-period"] = bollinger["lower"]
         dataframe["bb_middleband-period"] = bollinger["mid"]
@@ -137,13 +137,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
         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
 
@@ -177,8 +170,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
         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(
@@ -209,10 +201,10 @@ class FreqaiExampleHybridStrategy(IStrategy):
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
         return dataframe
     def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        """
+        Redefined target variable to predict whether the price will increase or decrease in the future.
+        """
         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")
@@ -223,22 +215,16 @@ class FreqaiExampleHybridStrategy(IStrategy):
             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
+            # Define target variable: 1 for price increase, 0 for price decrease
             dataframe["&-up_or_down"] = np.where(
-                dataframe["close"].shift(-50) > dataframe["close"],
-                1.0,  # 数值型标签
-                0.0
+                dataframe["close"].shift(-50) > dataframe["close"], 1, 0
             )
+            # Ensure target variable is a 2D array
+            dataframe["&-up_or_down"] = dataframe["&-up_or_down"].values.reshape(-1, 1)
         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
 

freqtrade/templates/FreqaiExampleStrategy.py

@@ -30,23 +30,34 @@ class FreqaiExampleStrategy(IStrategy):
 
     # FreqAI 配置
     freqai_info = {
-        "model": "LightGBMRegressor",
+        "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,
-            "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 = {
@@ -60,43 +71,93 @@ 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()
-        )
-        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
-        dataframe.ffill(inplace=True)
-        dataframe.fillna(0, inplace=True)
+    def featcaure_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
+        # 保留关键的技术指标
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+
+    def calculate_macd(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Centralized method to calculate MACD and ensure proper assignment to the dataframe.
+        """
+        try:
+            macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+            dataframe["macd"] = macd["macd"]
+            dataframe["macdsignal"] = macd["macdsignal"]
+
+            # Ensure no NaN or infinite values in MACD columns
+            dataframe["macd"] = dataframe["macd"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
+            dataframe["macdsignal"] = dataframe["macdsignal"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
+
+            logger.info("MACD 列已成功计算并验证。")
+        except Exception as e:
+            logger.error(f"计算 MACD 列时出错：{str(e)}")
+            dataframe["macd"] = np.nan
+            dataframe["macdsignal"] = np.nan
+
+        return dataframe
+
+        # 使用 centralized 方法计算 MACD
+        dataframe = self.calculate_macd(dataframe)
+
+        # 检查 MACD 列是否存在
+        if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
+            logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。")
+            try:
+                macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+                dataframe["macd"] = macd["macd"]
+                dataframe["macdsignal"] = macd["macdsignal"]
+                logger.info("MACD 列已成功重新计算。")
+            except Exception as e:
+                logger.error(f"重新计算 MACD 列时出错：{str(e)}")
+                raise ValueError("dataframe 缺少必要的 MACD 列且无法重新计算。")
+            logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号")
+            raise ValueError("DataFrame 缺少必要的 MACD 列")
+
+        # 确保 MACD 列存在
+        if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
+            logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号")
+            raise ValueError("DataFrame 缺少必要的 MACD 列")
+
+        # 保留布林带相关特征
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
+
+        # 保留成交量相关特征
+        dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
+
+        # 数据清理
+        for col in dataframe.columns:
+            if dataframe[col].dtype in ["float64", "int64"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
+                dataframe[col] = dataframe[col].ffill().fillna(0)
+        
+        logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}")
         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 +169,46 @@ class FreqaiExampleStrategy(IStrategy):
 
         try:
             label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+
+            # 定义目标变量为未来价格变化百分比（连续值）
+            dataframe["up_or_down"] = (
+                dataframe["close"].shift(-label_period) - dataframe["close"]
+            ) / dataframe["close"]
+
+            # 数据清理：处理 NaN 和 Inf 值
+            dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan)
+            dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0)
+
+            # 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)
+
+            # 检查并处理 NaN 或无限值
+            dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan)
+            dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0)
+
             # 生成 %-volatility 特征
             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)
+            # 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", "%-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 ["&-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')
+                dataframe[col] = dataframe[col].fillna(dataframe[col].mean())  # 使用均值填充 NaN 值
                 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['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:
@@ -142,6 +225,8 @@ 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 +236,66 @@ 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])
-            
-            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)
+# 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
+        exit_long_conditions = [
+            (dataframe["rsi"] > dataframe["sell_rsi_pred"]),  # RSI 高于卖出阈值
+            (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean()),  # 成交量高于近期均值
+            (dataframe["close"] < dataframe["bb_middleband"]),  # 价格低于布林带中轨
+            (dataframe["close"] < dataframe["bb_lowerband"].shift(1)),  # 当前价格低于上一周期的布林带下轨
+            (dataframe["volume"] < dataframe["volume"].shift(1) * 0.9)  # 当前成交量低于上一周期的10%
+        ]
+        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()}")
@@ -207,36 +303,54 @@ 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:
+    def populate_exit_trend(self, dataframe: 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
+            (dataframe["rsi"] > dataframe["sell_rsi_pred"]),  # RSI 高于卖出阈值
+            (
+                (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean()) |  # 成交量高于近期均值
+                (dataframe["volume"] < dataframe["volume"].shift(1) * 0.9)  # 当前成交量低于上一周期的10%
+            ),
+            (
+                (dataframe["close"] < dataframe["bb_middleband"]) |  # 价格低于布林带中轨
+                (dataframe["close"] < dataframe["bb_lowerband"].shift(1))  # 当前价格低于上一周期的布林带下轨
+            )
         ]
         if exit_long_conditions:
-            df.loc[
+            dataframe.loc[
                 reduce(lambda x, y: x & y, exit_long_conditions),
                 "exit_long"
             ] = 1
-        return df
+        return dataframe
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 改进买入信号条件
+        # 确保使用 centralized 方法计算 MACD
+        dataframe = self.calculate_macd(dataframe)
 
+        enter_long_conditions = [
+            (dataframe["rsi"] < dataframe["buy_rsi_pred"]),  # RSI 低于买入阈值
+            (
+                (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean() * 1.2) |  # 成交量高于近期均值20%
+                (dataframe["volume"] > dataframe["volume"].shift(1) * 1.1)  # 当前成交量高于上一周期的10%
+            ),
+            (
+                (dataframe["close"] > dataframe["bb_middleband"]) |  # 价格高于布林带中轨
+                (dataframe["close"] > dataframe["bb_upperband"].shift(1))  # 当前价格高于上一周期的布林带上轨
+            )
+        ]
+
+        # 如果 MACD 列存在，则添加 MACD 金叉条件
+        if "macd" in dataframe.columns and "macdsignal" in dataframe.columns:
+            enter_long_conditions.append((dataframe["macd"] > dataframe["macdsignal"]))
+        
+        # 确保模型预测为买入
+        enter_long_conditions.append((dataframe["do_predict"] == 1))
+        if enter_long_conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions),
+                ["enter_long", "enter_tag"]
+            ] = (1, "long")
+        return dataframe
     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
@@ -247,3 +361,4 @@ class FreqaiExampleStrategy(IStrategy):
             if rate > (last_candle["close"] * (1 + 0.0025)):
                 return False
         return True
+

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