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freqtrade/templates/FreqaiExampleStrategy.py

@@ -145,7 +145,7 @@ class FreqaiExampleStrategy(IStrategy):
         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(label_period), 1, 0
+            dataframe["close"] > dataframe["close"].shift(1), 1, 0
         )
         
         # 动态设置参数
@@ -154,25 +154,13 @@ class FreqaiExampleStrategy(IStrategy):
             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)
+            dataframe["&-roi_0"] = (dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)
             
-            # 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")
+            # 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)
-            dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean(), inplace=True)
-            if dataframe["roi_0_pred"].isna().any():
-                logger.warning("roi_0_pred 列包含 NaN，已填充为默认值")
                         
             # 检查预测值
             for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
@@ -180,9 +168,9 @@ class FreqaiExampleStrategy(IStrategy):
                     logger.warning(f"列 {col} 包含 NaN，填充为默认值")
                     dataframe[col].fillna(dataframe[col].mean(), inplace=True)
             
-            # 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])
@@ -212,10 +200,7 @@ class FreqaiExampleStrategy(IStrategy):
     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
+            df["volume"] > 0
         ]
         if enter_long_conditions:
             df.loc[
@@ -227,10 +212,7 @@ class FreqaiExampleStrategy(IStrategy):
     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["close"].shift(1) * 0.97),
-            df["volume"] > 0,
-            df["do_predict"] == 1,
-            df["up_or_down"] == 0
+            df["volume"] > 0
         ]
         if exit_long_conditions:
             df.loc[

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