diff --git a/.gitignore b/.gitignore
index d70536b0..8938f0ec 100644
--- a/.gitignore
+++ b/.gitignore
@@ -125,5 +125,5 @@ data/
 !result/
 
 tools/.env
-result/
 output.log
+result/
diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 57dac6bd..35dbd6f0 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -4,47 +4,38 @@ from functools import reduce
 import talib.abstract as ta
 from pandas import DataFrame
 from technical import qtpylib
-from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
-from pandas.core.dtypes.common import is_scalar
-import pandas as pd
-
+from freqtrade.strategy import IStrategy
 
 logger = logging.getLogger(__name__)
 
 class FreqaiPrimer(IStrategy):
-    minimal_roi = {
-        0: 0.135,
-        9: 0.052,
-        15: 0.007,
-        60: 0
-    }
-    stoploss = -0.263
-    trailing_stop = True
-    trailing_stop_positive = 0.324
-    trailing_stop_positive_offset = 0.411
-    trailing_only_offset_is_reached = False
-    max_open_trades = 4
-    process_only_new_candles = True
-    use_exit_signal = True
-    startup_candle_count: int = 40
-    can_short = False
+    """
+    策略说明：
+        - 所有交易信号由 FreqAI 动态预测
+        - 不使用 Hyperopt 优化任何参数
+        - 使用 FreqAI 提供的趋势和波动率信号进行交易决策
+    """
 
-    buy_rsi = IntParameter(low=10, high=50, default=30, space="buy", optimize=False, load=True)
-    sell_rsi = IntParameter(low=50, high=90, default=70, space="sell", optimize=False, load=True)
-    roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.135, space="roi", optimize=True, load=True)
-    roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.052, space="roi", optimize=True, load=True)
-    roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.007, space="roi", optimize=True, load=True)
-    stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.263, space="stoploss", optimize=True, load=True)
-    trailing_stop_positive_param = DecimalParameter(low=0.1, high=0.5, default=0.324, space="trailing", optimize=True, load=True)
-    trailing_stop_positive_offset_param = DecimalParameter(low=0.2, high=0.6, default=0.411, space="trailing", optimize=True, load=True)
-    tema_period = IntParameter(low=5, high=20, default=9, space="buy", optimize=True, load=True)
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "Signals": {
+                "enter_long": {"color": "green"},
+                "exit_long": {"color": "red"}
+            },
+            "FreqAI Predictions": {
+                "&-buy_signal": {"color": "blue"},
+                "&-sell_signal": {"color": "orange"},
+                "&-volatility_forecast": {"color": "purple"}
+            }
+        }
+    }
 
     freqai_info = {
-        "model": "LightGBMRegressorMultiTarget",
+        "model": "LightGBMClassifier",
         "feature_parameters": {
             "include_timeframes": ["5m", "15m", "1h"],
-            "include_corr_pairlist": [],
-            "label_period_candles": 12,
+            "label_period_candles": 24,
             "include_shifted_candles": 3,
         },
         "data_split_parameters": {
@@ -52,357 +43,100 @@ class FreqaiPrimer(IStrategy):
             "shuffle": False,
         },
         "model_training_parameters": {
-            "n_estimators": 500,
-            "learning_rate": 0.01,
-            "num_leaves": 64,
-            "max_depth": 8,
+            "n_estimators": 200,
+            "learning_rate": 0.05,
+            "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 __init__(self, config: dict):
-        super().__init__(config)
-        self.cycle_trade_pair = None  # 跟踪当前周期的下单币对
-        self.last_cycle_time = None   # 记录上一个周期时间
-    def bot_loop_start(self, current_time, **kwargs) -> None:
-        """
-        Reset cycle_trade_pair at the start of each new cycle.
-        """
-        try:
-            # 检查是否进入新周期（基于 timeframe，如 5m）
-            if self.last_cycle_time is None or current_time >= self.last_cycle_time + pd.Timedelta(self.timeframe):
-                self.cycle_trade_pair = None
-                self.last_cycle_time = current_time
-                logger.debug(f"New cycle started at {current_time}, reset cycle_trade_pair")
-        except Exception as e:
-            logger.error(f"Error in bot_loop_start: {str(e)}")
-    def clean_dataframe(self, dataframe: DataFrame, columns: list = None) -> DataFrame:
-        try:
-            if columns:
-                valid_columns = [col for col in columns if col in dataframe.columns]
-                if not valid_columns:
-                    logger.warning(f"None of the specified columns {columns} exist in DataFrame. Skipping cleaning.")
-                    return dataframe
-                for col in valid_columns:
-                    logger.debug(f"Cleaning column {col}: NaN count before = {dataframe[col].isna().sum()}")
-                    dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
-                    logger.debug(f"NaN count after = {dataframe[col].isna().sum()}")
-            else:
-                logger.debug(f"Cleaning entire DataFrame: NaN count before = {dataframe.isna().sum().sum()}")
-                dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
-                logger.debug(f"NaN count after = {dataframe.isna().sum().sum()}")
-            return dataframe
-        except Exception as e:
-            logger.error(f"Data cleaning failed: {str(e)}")
-            return dataframe  # 返回原始 DataFrame，避免策略崩溃
-
     def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
-        try:
-            dataframe = self.clean_dataframe(dataframe, columns=["close", "volume", "open", "high", "low"])
-            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 = self.clean_dataframe(dataframe, columns=["%-rsi-period", "%-mfi-period", "%-sma-period",
-                                                                "%-ema-period", "%-adx-period", "bb_lowerband-period",
-                                                                "bb_middleband-period", "bb_upperband-period",
-                                                                "%-bb_width-period", "%-close-bb_lower-period",
-                                                                "%-roc-period", "%-relative_volume-period"])
-            return dataframe
-        except Exception as e:
-            logger.error(f"Error in feature_engineering_expand_all for {metadata['pair']}: {str(e)}")
-            return dataframe
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
-        try:
-            dataframe = self.clean_dataframe(dataframe, columns=["close", "volume", "open", "high", "low"])
-            dataframe["%-pct-change"] = dataframe["close"].pct_change()
-            dataframe["%-raw_volume"] = dataframe["volume"]
-            dataframe["%-raw_price"] = dataframe["close"]
-            dataframe = self.clean_dataframe(dataframe, columns=["%-pct-change", "%-raw_volume", "%-raw_price"])
-            return dataframe
-        except Exception as e:
-            logger.error(f"Error in feature_engineering_expand_basic for {metadata['pair']}: {str(e)}")
-            return dataframe
+        real = ta.TYPPRICE(dataframe)
+        upperband, middleband, lowerband = ta.BBANDS(real, timeperiod=period, nbdevup=2.0, nbdevdn=2.0)
+        dataframe["bb_lowerband-period"] = lowerband
+        dataframe["bb_upperband-period"] = upperband
+        dataframe["bb_middleband-period"] = middleband
+        dataframe["%-bb_width-period"] = (dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]) / dataframe["bb_middleband-period"]
 
-    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
-        try:
-            dataframe = self.clean_dataframe(dataframe, columns=["close", "volume", "open", "high", "low"])
-            if len(dataframe["close"]) < 20:
-                logger.warning(f"数据不足 {len(dataframe)} 根 K 线，%-volatility 可能不完整")
-            dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
-            dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
-            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
-            dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
-            dataframe = self.clean_dataframe(dataframe, columns=["%-day_of_week", "%-hour_of_day", "%-volatility"])
-            return dataframe
-        except Exception as e:
-            logger.error(f"Error in feature_engineering_standard for {metadata['pair']}: {str(e)}")
-            return dataframe
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+        return dataframe
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
-        try:
-            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")
-            label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
-            if "%-volatility" not in dataframe.columns:
-                logger.warning("缺少 %-volatility 列，强制重新生成")
-                dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
-                dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
-            dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14).rolling(window=label_period).mean().ffill().bfill()
-            for col in ["&-buy_rsi", "%-volatility"]:
-                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
-                if dataframe[col].isna().any():
-                    logger.warning(f"目标列 {col} 仍包含 NaN，数据预览：\n{dataframe[col].tail(10)}")
-            logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
-            return dataframe
-        except Exception as e:
-            logger.error(f"创建 FreqAI 目标失败：{str(e)}")
-            return dataframe
+        """
+        使用历史窗口预测未来趋势和波动率作为辅助信号，
+        避免使用任何未来数据（如 shift(-N)）
+        """
+
+        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+
+        # 1. 趋势强度：使用过去 N 根 K 线的收益率判断当前趋势
+        dataframe["&-trend_strength"] = dataframe["close"].pct_change(label_period)
+
+        # 2. 波动率预测：使用过去 N 根 K 线的价格变动绝对值
+        dataframe["&-volatility_forecast"] = dataframe["close"].pct_change(label_period).abs()
+
+        # 3. 动态 ROI 目标：基于趋势强度缩放
+        dataframe["&-roi_target"] = np.where(
+            dataframe["&-trend_strength"] > 0,
+            dataframe["&-trend_strength"] * 1.5,
+            0.01  # 最小 ROI
+        )
+
+        # 4. 动态止损目标：基于波动率缩放
+        dataframe["&-stoploss_target"] = -dataframe["&-volatility_forecast"] * 1.2
+
+        # 5. 市场状态识别：判断当前是震荡、趋势、超买还是超卖
+        if "bb_upperband-period" in dataframe.columns and "bb_lowerband-period" in dataframe.columns:
+            dataframe["&-market_condition"] = np.select([
+                (dataframe["close"] > dataframe["bb_upperband-period"]),
+                (dataframe["close"] < dataframe["bb_lowerband-period"]),
+                (dataframe["&-trend_strength"] > 0.03),
+            ], [
+                "overbought",
+                "oversold",
+                "strong_up",
+            ], default="sideways")
+
+        # 买入信号（必须存在）
+        dataframe["&-buy_signal"] = np.where(dataframe["&-trend_strength"] > 0.01, 1, 0)
+
+        # 卖出信号（必须存在）
+        dataframe["&-sell_signal"] = np.where(dataframe["&-trend_strength"] < -0.01, 1, 0)
+
+
+        return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        try:
-            logger.debug(f"Pre-cleaning input DataFrame for {metadata['pair']}: columns={list(dataframe.columns)}")
-            dataframe = self.clean_dataframe(dataframe, columns=["close", "volume", "open", "high", "low"])
-            logger.info(f"Processing pair: {metadata['pair']}")
-            logger.debug(f"Input columns before FreqAI: {list(dataframe.columns)}, shape={dataframe.shape}")
-            
-            # FreqAI 预测
-            dataframe = self.freqai.start(dataframe, metadata, self)
-            logger.debug(f"Columns after FreqAI: {list(dataframe.columns)}, shape={dataframe.shape}")
-            
-            # 技术指标
-            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=self.tema_period.value)
-            dataframe["atr"] = ta.ATR(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
-            
-            # 标签生成
-            label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
-            dataframe["up_or_down"] = np.where(
-                dataframe["close"].rolling(window=label_period).mean() > dataframe["close"], 1, 0
-            )
-            
-            # FreqAI 预测后处理
-            if "&-buy_rsi" in dataframe.columns:
-                if "%-volatility" not in dataframe.columns:
-                    logger.warning("Missing %-volatility column, regenerating")
-                    dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
-                    dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
-                dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
-                dataframe["&-stoploss"] = self.stoploss - (dataframe["%-volatility"] * 5).clip(-0.05, 0.05)
-                dataframe["&-roi_0"] = (dataframe["close"].rolling(window=label_period).mean() / dataframe["close"] - 1).clip(0, 0.2)
-                dataframe = self.clean_dataframe(dataframe, columns=["&-buy_rsi", "&-sell_rsi", "&-stoploss", "&-roi_0", "%-volatility"])
-                dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5).mean().clip(10, 50)
-                dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5).mean().clip(50, 90)
-                dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.35, -0.1)
-                dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
-                
-                # 处理缺失值
-                for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred"]:
-                    if dataframe[col].isna().any():
-                        logger.warning(f"Column {col} contains NaN values, filling with mean")
-                        dataframe[col] = dataframe[col].ffill().fillna(dataframe[col].mean())
-                
-                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)
-                
-                # 更新动态参数
-                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(self.trailing_stop_positive_param.value)
-                self.trailing_stop_positive_offset = float(self.trailing_stop_positive_offset_param.value)
-                logger.info(f"Dynamic parameters: buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
-                            f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
-            else:
-                logger.warning(f"&-buy_rsi column missing, skipping FreqAI prediction logic. Check model training or prediction files.")
-                dataframe["buy_rsi_pred"] = self.buy_rsi.default
-                dataframe["sell_rsi_pred"] = self.sell_rsi.default
-                dataframe["stoploss_pred"] = self.stoploss_param.default
-                dataframe["roi_0_pred"] = self.roi_0.default
-                dataframe["trailing_stop_positive"] = self.trailing_stop_positive_param.default
-                dataframe["trailing_stop_positive_offset"] = self.trailing_stop_positive_offset_param.default
-            
-            # 清理生成的列
-            generated_columns = [
-                "rsi", "bb_lowerband", "bb_middleband", "bb_upperband", "tema", "atr",
-                "up_or_down", "buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred",
-                "trailing_stop_positive", "trailing_stop_positive_offset"
-            ]
-            dataframe = self.clean_dataframe(dataframe, columns=generated_columns)
-            
-            logger.info(f"up_or_down value counts:\n{dataframe['up_or_down'].value_counts().to_string()}")
-            logger.info(f"do_predict value counts:\n{dataframe['do_predict'].value_counts().to_string()}")
-            logger.debug(f"Final columns: {list(dataframe.columns)}, shape={dataframe.shape}")
-            return dataframe
-        except Exception as e:
-            logger.error(f"Error processing indicators for {metadata['pair']}: {str(e)}")
-            return dataframe  # 返回未修改的 DataFrame，避免中断
+        logger.info(f"[{metadata['pair']}] 当前可用列: {list(dataframe.columns)}")
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        # FreqAI 提供的预测列
+        if "&-trend" in dataframe.columns:
+            dataframe["in_uptrend"] = dataframe["&-trend"] > 0.5
+
+        return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        try:
-            # 验证输入列
-            required_columns = ["rsi", "tema", "volume", "buy_rsi_pred", "do_predict", "bb_middleband", "close", "&-buy_rsi"]
-            missing_cols = [col for col in required_columns if col not in df.columns]
-            if missing_cols:
-                logger.error(f"Missing columns for {metadata['pair']}: {missing_cols}")
-                return df
+        conditions = []
 
-            # 处理 NaN
-            if df[required_columns].isna().any().any():
-                logger.warning(f"NaN values in {metadata['pair']}: {df[required_columns].isna().any()}")
-                df[required_columns] = df[required_columns].ffill().fillna(0)
+        if "&-buy_signal" in df.columns:
+            conditions.append(df["&-buy_signal"] > 0.5)
+        else:
+            logger.warning("⚠️ &-buy_signal 列缺失，跳过该条件")
 
-            # 计算 entry_score
-            df["entry_score"] = (
-                (50 - df["rsi"]) * 0.5 +  # RSI 超卖权重
-                (df["volume"] / df["volume"].rolling(20, min_periods=1).mean()) * 0.3 +  # 成交量活跃
-                df["&-buy_rsi"] * 0.2      # FreqAI 预测
-            )
-
-            # 确保 entry_score 是标量
-            if not df["entry_score"].apply(is_scalar).all():
-                logger.error(f"Non-scalar values in entry_score for {metadata['pair']}: {df['entry_score'].head()}")
-                df["entry_score"] = df["entry_score"].apply(lambda x: x[0] if isinstance(x, (list, np.ndarray)) else x).astype(float)
-
-            # 买入条件（优化为 5 个，触发率 0.35%-0.42%）
-            enter_long_conditions = [
-                qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),  # RSI 上穿 FreqAI 预测
-                df["tema"] > df["tema"].shift(1),                      # TEMA 上行（趋势确认）
-                df["volume"] > df["volume"].rolling(20, min_periods=1).mean() * 1.1,  # 成交量略高于均量
-                df["do_predict"] == 1,                                 # FreqAI 预测有效
-                df["rsi"] < 45                                         # RSI 放宽到 < 45
-            ]
-
-            # 合并条件
-            if enter_long_conditions:
-                condition = reduce(lambda x, y: x & y, enter_long_conditions)
-                df.loc[condition, "enter_long"] = 1
-                df.loc[condition, "enter_tag"] = "long"
-                df.loc[condition, "entry_score"] = df.loc[condition, "entry_score"]
-                df["enter_long"] = df["enter_long"].fillna(0).astype(int)
-                df["enter_tag"] = df["enter_tag"].fillna("")
-                df["entry_score"] = df["entry_score"].fillna(0.0).astype(float)
-            else:
-                logger.warning(f"No valid entry conditions for {metadata['pair']}")
-                df["enter_long"] = 0
-                df["enter_tag"] = ""
-                df["entry_score"] = 0.0
-
-            logger.debug(f"Entry signals for {metadata['pair']}: {df['enter_long'].sum()} signals")
-            return df
-        except Exception as e:
-            logger.error(f"Error in populate_entry_trend for {metadata['pair']}: {str(e)}")
+        if len(conditions) == 0:
             return df
+
+        df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
+        return df
+
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-        try:
-            exit_long_conditions = [
-                qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"]),
-                (df["close"] < df["close"].shift(1) * 0.97) | (df["rsi"] > 80),
-                df["volume"] > 0,
-                (df["do_predict"] == 1) | (df["do_predict"] == 0),
-                (df["up_or_down"] == 0) | (df["rsi"] > 70)
-            ]
-            if exit_long_conditions:
-                df.loc[
-                    reduce(lambda x, y: x & y, exit_long_conditions),
-                    "exit_long"
-                ] = 1
-            return df
-        except Exception as e:
-            logger.error(f"Error in populate_exit_trend for {metadata['pair']}: {str(e)}")
-            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:
-        try:
-            df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-            if df is None or df.empty:
-                logger.warning(f"无法获取 {pair} 的分析数据，拒绝交易")
-                return False
-
-            last_candle = df.iloc[-1].squeeze()
-            if "close" not in last_candle or np.isnan(last_candle["close"]):
-                logger.warning(f"{pair} 的最新 K 线缺少有效 close 价格，拒绝交易")
-                return False
-
-            if side == "long":
-                # 检查当前周期是否已有下单
-                if self.cycle_trade_pair is not None:
-                    logger.debug(f"周期内已有下单 {self.cycle_trade_pair}，拒绝 {pair} 的买入")
-                    return False
-
-                # 收集所有交易对的信号和分数
-                signals = []
-                for p in self.config["exchange"]["pair_whitelist"]:
-                    p_df, _ = self.dp.get_analyzed_dataframe(p, self.timeframe)
-                    if p_df is not None and not p_df.empty and "enter_long" in p_df.columns:
-                        if p_df["enter_long"].iloc[-1] == 1:
-                            score = p_df["entry_score"].iloc[-1] if "entry_score" in p_df.columns else -float('inf')
-                            signals.append((p, score))
-
-                # 如果有信号，选择得分最高的币对
-                if signals:
-                    signals = sorted(signals, key=lambda x: x[1], reverse=True)
-                    top_pair, top_score = signals[0]
-                    if pair != top_pair:
-                        logger.debug(f"{pair} 不是最高得分币对 {top_pair} (score={top_score:.2f})，拒绝买入")
-                        return False
-                else:
-                    logger.debug(f"无有效信号，拒绝 {pair} 的买入")
-                    return False
-
-                # 滑点检查
-                max_rate = last_candle["close"] * (1 + 0.0025)  # 0.25% 滑点阈值
-                if rate > max_rate:
-                    logger.debug(f"拒绝 {pair} 的买入，价格 {rate} 超过最大允许价格 {max_rate}")
-                    return False
-
-                # 记录下单币对
-                self.cycle_trade_pair = pair
-                logger.info(f"确认 {pair} 的买入，周期内最优信号 (score={last_candle['entry_score']:.2f})")
-                return True
-            elif side == "short":
-                logger.warning(f"{pair} 尝试做空，但策略不支持做空 (can_short={self.can_short})")
-                return False
-        except Exception as e:
-            logger.error(f"确认 {pair} 交易时出错：{str(e)}")
-            return False
+        conditions = [
+            df["&-sell_signal"] > 0.5,
+        ]
+        df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
+        return df