--- original_strategy.py
+++ modified_strategy.py
@@ -297,6 +297,9 @@ class FreqaiPrimer(IStrategy):
         dataframe["bb_lowerband"] = lowerband
         dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
         dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
         dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
         dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
+        # 计算15分钟ATR和3分钟EMA10、EMA20
+        dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
+ cuyuan
+        dataframe_15m['atr15m'] = ta.ATR(dataframe_15m, timeperiod=14)
+        dataframe['atr15m'] = dataframe_15m['atr15m'].reindex(dataframe.index, method="ffill")
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)

@@ -390,8 +390,11 @@ class FreqaiPrimer(IStrategy):
         conditions = []

         if "&-price_value_divergence" in dataframe.columns:
             cond1 = (dataframe["&-price_value_divergence"] > self.sell_threshold * 1.025)
-            cond2 = (dataframe["rsi"] > 75)
+            rsi_threshold = 70 if trend_score > 50 else 65  # 动态RSI阈值
+            cond2 = (dataframe["rsi"] > rsi_threshold)
+            cond3 = (dataframe['ema10'] < dataframe['ema20'])  # 短期均线交叉
             sell_condition = cond1 | cond2
             conditions.append(sell_condition)

@@ -442,6 +442,13 @@ class FreqaiPrimer(IStrategy):
         initial_stake_amount = trade.stake_amount / 3
         logger.debug(f"{pair} 首次入场金额: {initial_stake_amount:.2f}, 当前持仓金额: {trade.stake_amount:.2f}, "
                     f"加仓次数: {trade.nr_of_successful_entries - 1}")

+        # 分批出场：短期涨幅触发
+        short_term_return = (current_rate - dataframe['close'].shift(3).iloc[-1]) / dataframe['close'].shift(3).iloc[-1]
+        if short_term_return >= 0.03:  # 3%涨幅
+            reduce_amount = -0.5 * trade.stake_amount  # 卖出50%仓位
+            logger.info(f"{pair} 短期涨幅 {short_term_return*100:.2f}%，卖出50%仓位 {abs(reduce_amount):.2f}")
+            return (reduce_amount, f"Short-term gain {short_term_return*100:.2f}%")
+
         # 加仓逻辑
         max_entry_adjustments = self.MAX_ENTRY_POSITION_ADJUSTMENT.value
@@ -484,12 +484,11 @@ class FreqaiPrimer(IStrategy):
         # 追踪止损逻辑
         trailing_stop_start = self.TRAILING_STOP_START.value
         trailing_stop_distance = self.TRAILING_STOP_DISTANCE.value
-        # 使用 Sigmoid 映射调整追踪止损参数
-        sigmoid = 1 / (1 + np.exp(-0.1 * (trend_score - 50)))
-        trailing_factor = 0.8 + (1.2 - 0.8) * sigmoid  # 牛市(100) -> 1.2, 熊市(0) -> 0.8
-        distance_factor = 0.7 + (1.5 - 0.7) * sigmoid  # 牛市(100) -> 1.5, 熊市(0) -> 0.7
-        trailing_stop_start *= trailing_factor
-        trailing_stop_distance *= distance_factor
+        # 使用15分钟ATR动态调整止损距离
+        atr15m = dataframe['atr15m'].iloc[-1]
+        trailing_stop_distance = min(max(2 * atr15m / current_rate, 0.01), 0.05)  # 2倍ATR，限制在0.01-0.05
+        if trend_score > 50:  # 强趋势时放宽止损
+            trailing_stop_distance *= 1.2
+        trailing_stop_price = max_rate * (1 - trailing_stop_distance)

         if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
             self.trailing_stop_enabled = True
             trade.adjust_min_max_rates(current_rate, current_rate)
             logger.info(f"{pair} 价格上涨超过 {trailing_stop_start*100:.1f}%，启动追踪止损")
             return None