diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 0f93d3dc..23da82b2 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -991,6 +991,12 @@ class FreqaiPrimer(IStrategy):
         dataframe['stochrsi_k_3m'] = stochrsi_3m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
         dataframe['stochrsi_d_3m'] = stochrsi_3m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
         
+        # 新增 KDJ 指标（3m）
+        kdj_3m = ta.stoch(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], k=9, d=3, smooth_k=3)
+        dataframe['kdj_k_3m'] = kdj_3m['STOCHk_9_3_3']
+        dataframe['kdj_d_3m'] = kdj_3m['STOCHd_9_3_3']
+        dataframe['kdj_j_3m'] = 3 * dataframe['kdj_k_3m'] - 2 * dataframe['kdj_d_3m']
+        
         # 新增 MACD 指标
         macd_3m = ta.macd(dataframe['close'], fast=12, slow=26, signal=9)
         dataframe['macd_3m'] = macd_3m['MACD_12_26_9']
@@ -1085,6 +1091,12 @@ class FreqaiPrimer(IStrategy):
         df_1h['stochrsi_k_1h'] = stochrsi_1h[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
         df_1h['stochrsi_d_1h'] = stochrsi_1h[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
         
+        # 新增 KDJ 指标（1h）
+        kdj_1h = ta.stoch(high=df_1h['high'], low=df_1h['low'], close=df_1h['close'], k=9, d=3, smooth_k=3)
+        df_1h['kdj_k_1h'] = kdj_1h['STOCHk_9_3_3']
+        df_1h['kdj_d_1h'] = kdj_1h['STOCHd_9_3_3']
+        df_1h['kdj_j_1h'] = 3 * df_1h['kdj_k_1h'] - 2 * df_1h['kdj_d_1h']
+        
         # 新增 MACD 指标
         macd_1h = ta.macd(df_1h['close'], fast=12, slow=26, signal=9)
         df_1h['macd_1h'] = macd_1h['MACD_12_26_9']
@@ -1193,6 +1205,7 @@ class FreqaiPrimer(IStrategy):
         df_1h = df_1h[[
             'date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 
             'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 
+            'kdj_k_1h', 'kdj_d_1h', 'kdj_j_1h',  # KDJ 指标
             'macd_1h', 'macd_signal_1h', 'ema_5_1h', 'ema_20_1h', 'ema5_cross_above_ema20',
             # === 1h 趋势特征 ===
             'adx_1h', 'plus_di_1h', 'minus_di_1h',  # ADX 趋势强度
@@ -1317,6 +1330,12 @@ class FreqaiPrimer(IStrategy):
         # 条件4: RSI 进入超买区域（使用可优化的超买阈值）
         rsi_overbought = dataframe['rsi_1h'] > self.rsi_overbought.value
         
+        # 条件5: KDJ 高位死叉（3m KDJ 辅助出场）
+        kdj_3m_exit = pd.Series(False, index=dataframe.index)
+        if 'kdj_k_3m' in dataframe.columns and 'kdj_d_3m' in dataframe.columns:
+            kdj_3m_death_cross = (dataframe['kdj_k_3m'] > 80) & (dataframe['kdj_k_3m'] < dataframe['kdj_d_3m']) & (dataframe['kdj_k_3m'].shift(1) >= dataframe['kdj_d_3m'].shift(1))
+            kdj_3m_exit = kdj_3m_death_cross
+        
         # === 新增：1h 趋势反转检测 ===
         # 计算趋势反转得分（0-100，得分越高越可能反转）
         if 'ema_bear_align_1h' in dataframe.columns and 'adx_1h' in dataframe.columns:
@@ -1381,12 +1400,13 @@ class FreqaiPrimer(IStrategy):
             self.strategy_log(f"[{metadata['pair']}] 警告：缺少趋势特征，趋势出场禁用")
         
         # === 最终出场条件 ===
-        # 方案1：传统条件 OR 趋势反转
+        # 方案1：传统条件 OR 趋势反转 OR KDJ 高位死叉
         final_condition = (
             breakout_condition | 
             volume_spike | 
             macd_downward | 
             rsi_overbought | 
+            kdj_3m_exit |
             trend_protected_exit
         )
         
@@ -1471,6 +1491,23 @@ class FreqaiPrimer(IStrategy):
             tech_score += vol_score
             tech_components += 1
             
+        # 1.6 KDJ 得分（3m + 1h 组合）
+        # 3m KDJ：低位金叉加分
+        if 'kdj_k_3m' in dataframe.columns and 'kdj_d_3m' in dataframe.columns:
+            kdj_3m_golden = (dataframe['kdj_k_3m'] < 40) & (dataframe['kdj_k_3m'] > dataframe['kdj_d_3m']) & (dataframe['kdj_k_3m'].shift(1) <= dataframe['kdj_d_3m'].shift(1))
+            kdj_3m_j_recovery = (dataframe['kdj_j_3m'] > 0) & (dataframe['kdj_j_3m'].shift(1) <= 0)
+            kdj_3m_score = ((kdj_3m_golden | kdj_3m_j_recovery).astype(float) * 100)
+            tech_score += kdj_3m_score
+            tech_components += 1
+            
+        # 1h KDJ：禁止极端高位（扣分机制）
+        if 'kdj_k_1h' in dataframe.columns and 'kdj_j_1h' in dataframe.columns:
+            kdj_1h_block = (dataframe['kdj_k_1h'] > 85) & (dataframe['kdj_j_1h'] < dataframe['kdj_j_1h'].shift(1))
+            # 高位见顶时扣 50 分（通过乘以 -0.5 实现）
+            kdj_1h_score = (~kdj_1h_block).astype(float) * 50 + 50  # 正常状态 100 分，见顶状态 50 分
+            tech_score += kdj_1h_score
+            tech_components += 1
+            
         # 计算技术指标平均得分
         if tech_components > 0:
             tech_score = tech_score / tech_components