diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
index 1d7ed33..245f771 100644
--- a/freqtrade/templates/FreqaiExampleStrategy.py
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
@@ -128,9 +128,9 @@ class FreqaiExampleStrategy(IStrategy):
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        logger.info(f"Processing pair: {metadata['pair']}")
-        logger.info(f"DataFrame rows: {len(dataframe)}")
-        logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
+        logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
+        logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
+        logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
 
         if "close" not in dataframe.columns or dataframe["close"].isna().all():
             logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
@@ -173,6 +173,19 @@ class FreqaiExampleStrategy(IStrategy):
                 logger.warning("bb_lowerband contains NaN, filling with close")
                 dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
             logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
+        # 生成 up_or_down
+        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+        if len(dataframe) < label_period + 1:
+            logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
+            dataframe["up_or_down"] = 0
+        else:
+            dataframe["up_or_down"] = np.where(
+                dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
+            )
+            if dataframe["up_or_down"].isna().any():
+                logger.warning("up_or_down contains NaN, filling with 0")
+                dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
+            logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
 
         if "date" in dataframe.columns:
             dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
@@ -197,17 +210,18 @@ class FreqaiExampleStrategy(IStrategy):
                 dataframe[col] = 50 if col == "buy_rsi_pred" else 80
             logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
 
-        if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
-            if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
-                dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
-                    dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
-                ) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
-            logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
+        # 调试特征分布
+        if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
+            if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
+                dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
+                    dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
+                ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
+            logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
 
         def get_expected_columns(freqai_config: dict) -> list:
             indicators = ["rsi", "bb_width", "pct-change"]
-            periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
-            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
+            periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
+            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
             timeframes = freqai_config.get("include_timeframes", ["5m"])
             shifts = [0]
             expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
@@ -242,11 +256,17 @@ 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"]),
+            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
             df["tema"] > df["tema"].shift(1),
             df["volume"] > 0,
-            df["do_predict"] == 1
+            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")
         df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
         df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
         df.loc[
@@ -259,14 +279,16 @@ 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"] - 5)) |
+            (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
             (df["close"] < df["close"].shift(1) * 0.98) |
             (df["close"] < df["bb_lowerband"]),
             df["volume"] > 0,
-            df["do_predict"] == 1
+            df["do_predict"] == 1,
+            df["up_or_down"] == 0
         ]
+        time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
         df.loc[
-            reduce(lambda x, y: x & y, exit_long_conditions),
+            (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
             "exit_long"
         ] = 1
         return df