diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 80ff8615..158aed13 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -22,9 +22,23 @@ class FreqaiPrimer(IStrategy):
     
     stoploss = -0.01  # 固定止损 -1%
     trailing_stop = True
-    trailing_stop_positive = 0.005  # 价格上涨 0.5% 后开始追踪
     trailing_stop_positive_offset = 0.008  # 追踪止损偏移量 0.8%
     
+    # 用于跟踪市场状态的数据框缓存
+    _dataframe_cache = None
+    
+    @property
+    def trailing_stop_positive(self):
+        """根据市场状态动态调整跟踪止盈参数"""
+        # 获取当前市场状态
+        if self._dataframe_cache is not None and len(self._dataframe_cache) > 0:
+            current_state = self._dataframe_cache['market_state'].iloc[-1]
+            if current_state == 'strong_bull':
+                return 0.01  # 强劲牛市中放宽跟踪止盈
+            elif current_state == 'weak_bull':
+                return 0.007
+        return 0.005  # 默认值
+    
     timeframe = "3m"  # 主时间框架为 3 分钟
     can_short = False  # 禁用做空
 
@@ -203,14 +217,46 @@ class FreqaiPrimer(IStrategy):
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        # 做多退出（优化以减少亏损）
-        dataframe.loc[
-            (
-                (dataframe['close'] >= dataframe['bb_upper_3m']) |
-                (dataframe['rsi_3m'] > self.rsi_overbought) |
-                (dataframe['close'] > dataframe['open'] + dataframe['atr'] * 2)  # 获利2倍ATR
-            ),
-            'exit_long'] = 1
+        # 缓存数据框以便在trailing_stop_positive属性中使用
+        self._dataframe_cache = dataframe
+        
+        # 基础退出条件
+        basic_exit = (
+            (dataframe['close'] >= dataframe['bb_upper_3m']) |
+            (dataframe['rsi_3m'] > self.rsi_overbought)
+        )
+        
+        # 强劲趋势条件：1h趋势向上 + 熊牛得分>70
+        strong_trend = (dataframe['trend_1h_ema'] == 1) & (dataframe['market_score'] > 70)
+        
+        # 一般趋势条件：熊牛得分50-70
+        normal_trend = (dataframe['market_score'] >= 50) & (dataframe['market_score'] <= 70)
+        
+        # 获取15m数据进行趋势确认
+        df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
+        df_15m = df_15m.rename(columns={'date': 'date_15m'})
+        merged_data = dataframe.merge(df_15m[['date_15m', 'rsi_15m']], how='left', left_on='date', right_on='date_15m')
+        merged_data = merged_data.fillna(method='ffill')
+        
+        # 趋势反转信号：15m RSI超买
+        trend_reversal = merged_data['rsi_15m'] > 75
+        
+        # 动态调整退出条件
+        # 强劲趋势中：只有获利达到3倍ATR才退出，或出现明确的趋势反转信号
+        dataframe.loc[strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 3) | (basic_exit & trend_reversal)), 'exit_long'] = 1
+        
+        # 一般趋势中：保持原有2倍ATR退出
+        dataframe.loc[normal_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 2) | basic_exit), 'exit_long'] = 1
+        
+        # 非趋势或弱势：使用基础条件+1.5倍ATR
+        dataframe.loc[~strong_trend & ~normal_trend & (basic_exit | (dataframe['close'] > dataframe['open'] + dataframe['atr'] * 1.5)), 'exit_long'] = 1
+        
+        # 记录退出决策日志
+        if len(dataframe[dataframe['exit_long'] == 1]) > 0:
+            last_exit = dataframe[dataframe['exit_long'] == 1].iloc[-1]
+            current_state = dataframe['market_state'].iloc[-1]
+            logger.info(f"[{metadata['pair']}] 触发退出信号，市场状态: {current_state}, 价格: {last_exit['close']:.2f}")
+        
         return dataframe
 
     def detect_h1_rapid_rise(self, pair: str, dataframe: DataFrame, metadata: dict) -> tuple[bool, float]:
@@ -278,12 +324,54 @@ class FreqaiPrimer(IStrategy):
             logger.error(f"[{pair}] 剧烈拉升检测过程中发生错误: {str(e)}")
             return False, 0.0
             
-    def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float, 
                         current_profit: float, **kwargs) -> float:
         # 动态止损基于ATR
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1]
         atr = last_candle['atr']
+        
+        # 渐进式止损策略
+        if current_profit > 0.03:  # 利润超过3%时
+            return -1.5 * atr / current_rate  # 扩大止损范围，让利润奔跑
+        elif current_profit > 0.015:  # 利润超过1.5%时
+            return -1.2 * atr / current_rate  # 轻微扩大止损范围
+        
         if atr > 0:
-            return -1.0 * atr / current_rate  # 收紧到1倍ATR
+            return -1.0 * atr / current_rate  # 基础1倍ATR止损
         return self.stoploss
+    
+    def custom_exit(self, pair: str, trade: 'Trade', current_time, current_rate: float, 
+                    current_profit: float, **kwargs) -> float:
+        """渐进式止盈逻辑"""
+        
+        # 获取当前市场状态
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
+        
+        # 定义渐进式止盈水平
+        profit_levels = {
+            # 状态: [(止盈触发利润, 止盈比例)]
+            'strong_bull': [(0.02, 0.3), (0.04, 0.5), (0.06, 0.7), (0.08, 0.9)],  # 强劲牛市的渐进止盈
+            'weak_bull': [(0.015, 0.3), (0.03, 0.5), (0.05, 0.8)],  # 弱牛市的渐进止盈
+            'neutral': [(0.01, 0.4), (0.02, 0.7), (0.03, 0.9)],  # 中性市场的渐进止盈
+            'bear': [(0.008, 0.5), (0.015, 0.8), (0.025, 1.0)]  # 熊市的渐进止盈（更保守）
+        }
+        
+        # 默认使用中性市场的止盈设置
+        levels = profit_levels.get(current_state, profit_levels['neutral'])
+        
+        # 确定当前应该止盈的比例
+        exit_ratio = 0.0
+        for profit_target, ratio in levels:
+            if current_profit >= profit_target:
+                exit_ratio = ratio
+            else:
+                break
+        
+        # 记录渐进式止盈决策
+        if exit_ratio > 0:
+            logger.info(f"[{pair}] 渐进式止盈: 当前利润 {current_profit:.2%}, 市场状态 {current_state}, 止盈比例 {exit_ratio:.0%}")
+        
+        # 返回应退出的比例（0.0表示不退出，1.0表示全部退出）
+        return exit_ratio