diff --git a/doc/strategy_improvements.md b/doc/strategy_improvements.md
index f1963437..e77b95a7 100644
--- a/doc/strategy_improvements.md
+++ b/doc/strategy_improvements.md
@@ -89,6 +89,25 @@
 - 使用高级市场状态信息进行决策
 - 实现了动态止损调整
 
+#### 4.6 量价关系指标添加
+- **成交量趋势指标**：
+  - `volume_change`: 成交量变化率
+  - `volume_ma_ratio`: 成交量移动平均比值
+  - `volume_std`: 成交量标准差（衡量成交量波动性）
+  - `volume_cv`: 成交量变异系数
+  - `volume_zscore`: 成交量Z分数（检测异常成交量）
+  - `volume_trend`: 成交量趋势（成交量是否在上升通道）
+  - `volume_ema_fast/slow`: 成交量EMA趋势
+
+- **量价关系指标**：
+  - `price_change`: 价格变化率
+  - `volume_price_confirmation`: 量价确认度（价格和成交量同向变动）
+  - `volume_price_ratio`: 量价比率（成交量/价格）
+  - `bearish_divergence`: 熊市背离（价格创新高但成交量未创新高）
+  - `bullish_divergence`: 牛市背离（价格创新低但成交量未创新低）
+  - `obv`: OBV平衡成交量
+  - `volume_price_corr_short/medium`: 量价相关系数（短期/中期窗口）
+
 ### 5. 移除的逻辑
 - 移除了冷启动保护逻辑，确保回测和实盘的一致性
 
diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 211b16b9..04dfa3c1 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -706,6 +706,59 @@ class FreqaiPrimer(IStrategy):
         # 为第一行设置默认值
         dataframe['prev_market_state'] = dataframe['prev_market_state'].fillna('neutral')
         
+        # ========== 新增：成交量趋势指标 ==========
+        # 成交量变化率
+        dataframe['volume_change'] = dataframe['volume'].pct_change()
+        
+        # 成交量移动平均比值
+        dataframe['volume_ma_ratio'] = dataframe['volume'] / dataframe['volume_ma']
+        
+        # 成交量标准差（衡量成交量波动性）
+        dataframe['volume_std'] = dataframe['volume'].rolling(window=20).std()
+        dataframe['volume_cv'] = dataframe['volume_std'] / dataframe['volume_ma']  # 变异系数
+        
+        # 成交量Z分数（检测异常成交量）
+        dataframe['volume_zscore'] = (dataframe['volume'] - dataframe['volume_ma']) / dataframe['volume_std']
+        
+        # 成交量趋势（成交量是否在上升通道）
+        dataframe['volume_trend'] = np.where(dataframe['volume'] > dataframe['volume_ma'], 1, 0)
+        
+        # 成交量EMA趋势
+        dataframe['volume_ema_fast'] = dataframe['volume'].ewm(span=5, adjust=False).mean()
+        dataframe['volume_ema_slow'] = dataframe['volume'].ewm(span=20, adjust=False).mean()
+        dataframe['volume_ema_trend'] = np.where(dataframe['volume_ema_fast'] > dataframe['volume_ema_slow'], 1, 0)
+        
+        # ========== 新增：量价关系指标 ==========
+        # 价格变化率
+        dataframe['price_change'] = dataframe['close'].pct_change()
+        
+        # 量价确认度（价格和成交量同向变动）
+        dataframe['volume_price_confirmation'] = np.sign(dataframe['price_change']) * np.sign(dataframe['volume_change'])
+        
+        # 量价比率（成交量/价格）
+        dataframe['volume_price_ratio'] = dataframe['volume'] / dataframe['close']
+        
+        # 量价背离检测
+        # 价格创新高但成交量未创新高（顶背离）
+        dataframe['price_new_high'] = (dataframe['close'] == dataframe['close'].rolling(20).max()).astype(int)
+        dataframe['volume_new_high'] = (dataframe['volume'] == dataframe['volume'].rolling(20).max()).astype(int)
+        dataframe['bearish_divergence'] = (dataframe['price_new_high'] == 1) & (dataframe['volume_new_high'] == 0)
+        
+        # 价格创新低但成交量未创新低（底背离）
+        dataframe['price_new_low'] = (dataframe['close'] == dataframe['close'].rolling(20).min()).astype(int)
+        dataframe['volume_new_low'] = (dataframe['volume'] == dataframe['volume'].rolling(20).min()).astype(int)
+        dataframe['bullish_divergence'] = (dataframe['price_new_low'] == 1) & (dataframe['volume_new_low'] == 0)
+        
+        # OBV (On Balance Volume) - 平衡成交量
+        dataframe['obv'] = dataframe['volume'] * np.sign(dataframe['close'].diff())
+        dataframe['obv'] = dataframe['obv'].cumsum()
+        
+        # 量价相关系数（短期窗口）
+        dataframe['volume_price_corr_short'] = dataframe['close'].pct_change().rolling(window=10).corr(dataframe['volume'].pct_change())
+        
+        # 量价相关系数（中期窗口）
+        dataframe['volume_price_corr_medium'] = dataframe['close'].pct_change().rolling(window=20).corr(dataframe['volume'].pct_change())
+        
         # 记录当前的市场状态
         if len(dataframe) > 0:
             current_score = dataframe['market_score'].iloc[-1]