up3

config_examples/config_freqai.okx.json

@@ -64,8 +64,12 @@
         "data_kitchen": {
             "fillna": "ffill"
         },
-        "freqaimodel": "CatboostClassifier",
-        "purge_old_models": 2,
+        "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,

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

freqtrade/templates/FreqaiExampleStrategy.py

@@ -71,40 +71,50 @@ class FreqaiExampleStrategy(IStrategy):
         },
     }
 
-    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
+def feature_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)
+    dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
     macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
-        dataframe["%-macd"] = macd["macd"]
-        dataframe["%-macdsignal"] = macd["macdsignal"]
+    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"])
 
     # 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)
 
-        # 保留价格变化率
-        dataframe["close_roc"] = dataframe["close"].pct_change(periods=10)
-        
-        # 改进数据清理
+    # 数据清理
     for col in dataframe.columns:
         if dataframe[col].dtype in ["float64", "int64"]:
-                # 使用更稳健的归一化方法
-                mean = dataframe[col].mean()
-                std = dataframe[col].std()
-                dataframe[col] = (dataframe[col] - mean) / std
-                dataframe[col] = dataframe[col].clip(-3, 3)  # 限制在3个标准差内
+            dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
+            dataframe[col] = dataframe[col].ffill().fillna(0)
     
-        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
-        dataframe.ffill(inplace=True)
-        dataframe.fillna(0, inplace=True)
+    logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}")
+    return dataframe
+        # 保留关键的技术指标
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        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"]
+
+        # 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
@@ -134,7 +144,42 @@ class FreqaiExampleStrategy(IStrategy):
         dataframe.fillna(0, inplace=True)
         return dataframe
 
-    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> 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"]
+
+        # 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()
+
+        # 确保 &-buy_rsi 列的值计算正确
+        dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
+
+        # 数据清理
+        for col in ["&-buy_rsi", "up_or_down", "%-volatility"]:
+            # 使用直接操作避免链式赋值
+            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，填充为默认值")
+
+    except Exception as e:
+        logger.error(f"创建 FreqAI 目标失败：{str(e)}")
+        raise
+
+    # Log the shape of the target variable for debugging
+    logger.info(f"目标列形状：{dataframe['up_or_down'].shape}")
+    logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}")
+    return 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")
@@ -185,9 +230,10 @@ class FreqaiExampleStrategy(IStrategy):
         
         # 生成 up_or_down 信号（非 FreqAI 目标）
         label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
-        # 使用历史数据生成 up_or_down 信号
+        # 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"] > dataframe["close"].shift(1), 1, 0
+            dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
         )
         
         # 动态设置参数
@@ -208,12 +254,12 @@ class FreqaiExampleStrategy(IStrategy):
             # 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["&-buy_rsi"].rolling(window=10).mean().clip(30, 50)
-            dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].mean())
+            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"] + 40
-            dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].mean())
+            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)
@@ -279,6 +325,7 @@ class FreqaiExampleStrategy(IStrategy):
             (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
+            (df["macd"] > df["macdsignal"]),  # MACD 金叉
             (df["do_predict"] == 1)  # 确保模型预测为买入
         ]
         if enter_long_conditions: