--- original_strategy.py
+++ modified_strategy.py
@@ -28,8 +28,8 @@ class FreqaiPrimer(IStrategy):
     TRAILING_STOP_DISTANCE = DecimalParameter(0.005, 0.02, default=0.01, space='sell', optimize=True)

-    BUY_THRESHOLD_MIN = DecimalParameter(-0.1, -0.01, default=-0.05, space='buy', optimize=True)
-    BUY_THRESHOLD_MAX = DecimalParameter(-0.02, -0.001, default=-0.005, space='buy', optimize=True)
+    BUY_THRESHOLD_MIN = DecimalParameter(-0.15, -0.03, default=-0.07, space='buy', optimize=True)
+    BUY_THRESHOLD_MAX = DecimalParameter(-0.04, -0.005, default=-0.01, space='buy', optimize=True)
     SELL_THRESHOLD_MIN = DecimalParameter(0.001, 0.02, default=0.005, space='sell', optimize=True)
     SELL_THRESHOLD_MAX = DecimalParameter(0.02, 0.1, default=0.05, space='sell', optimize=True)

@@ -356,10 +356,10 @@ class FreqaiPrimer(IStrategy):
         volume_z_score_min = 0.5
         volume_z_score_max = 1.5
         volume_z_score_threshold = self.linear_map(trend_score, 0, 100, volume_z_score_max, volume_z_score_min)

-        rsi_min = 40
-        rsi_max = 60
+        rsi_min = 35
+        rsi_max = 55
         rsi_threshold = self.linear_map(trend_score, 0, 100, rsi_max, rsi_min)

-        stochrsi_min = 30
-        stochrsi_max = 50
+        stochrsi_min = 25
+        stochrsi_max = 45
         stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)

         if "&-price_value_divergence" in dataframe.columns:
             # 计算 STOCHRSI
             stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
             dataframe["stochrsi_k"] = stochrsi["fastk"]
+            # 添加 15m 时间框架的 EMA200
+            dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
+            dataframe_15m["ema200_15m"] = ta.EMA(dataframe_15m, timeperiod=200)
+            dataframe["ema200_15m"] = dataframe_15m["ema200_15m"].reindex(dataframe.index, method="ffill")

             cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
             cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold)
             cond3 = (dataframe["rsi"] < rsi_threshold)
             cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
-            cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
-            buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
+            cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
+            cond6 = (dataframe["close"] < dataframe["ema200_15m"])  # 价格低于 15m EMA200
+            buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6
             conditions.append(buy_condition)

@@ -534,7 +534,7 @@ class FreqaiPrimer(IStrategy):
     def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
         try:
             timeframes = ["3m", "15m", "1h"]
-            weights = {"3m": 0.5, "15m": 0.3, "1h": 0.2}
+            weights = {"3m": 0.3, "15m": 0.35, "1h": 0.35}
             trend_scores = {}
             pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'

@@ -565,7 +565,7 @@ class FreqaiPrimer(IStrategy):
                 pair_df["ema_short_slope"] = (pair_df["ema_short"] - pair_df["ema_short"].shift(10)) / pair_df["ema_short"].shift(10)

             # 动态调整权重
-            if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20:
-                weights = {"3m": 0.3, "15m": 0.3, "1h": 0.4}
+            if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20 or trend_scores.get("15m", 50) - trend_scores.get("3m", 50) > 20:
+                weights = {"3m": 0.2, "15m": 0.35, "1h": 0.45}
                 logger.debug(f"[{pair}] 1h 趋势得分（{trend_scores.get('1h', 50)}）显著高于 3m（{trend_scores.get('3m', 50)}），调整权重为 {weights}")