diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 526a8bfc..16908db1 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -779,7 +779,7 @@ class FreqaiPrimer(IStrategy):
                     #self.strategy_log(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
                     allow_trade = False
             
-            # 检查3：ML 审核官（FreqAI 过滤低质量入场）
+            # 检查3：ML 审核官（FreqAI 过滤低质量入场）+ 入场诊断统计
             # 逻辑：用 entry_signal 概率来判断——若"容易上涨概率"低，则拒绝入场
             if allow_trade:
                 try:
@@ -803,6 +803,48 @@ class FreqaiPrimer(IStrategy):
                                 # 文本标签时，简单映射为 0/1
                                 entry_prob = 1.0 if str(val).lower() in ['entry', 'buy', '1'] else 0.0
                         
+                        # ========== 新增：入场诊断统计 ==========
+                        # 统计当前入场点的关键指标，用于分析"买在高位"问题
+                        current_close = float(last_row['close'])
+                        
+                        # 1. 价格与短期高点的关系
+                        recent_high_5 = float(df['high'].iloc[-5:].max()) if len(df) >= 5 else current_close
+                        price_vs_recent_high = (current_close - recent_high_5) / recent_high_5 if recent_high_5 > 0 else 0
+                        
+                        # 2. 价格与 EMA5 的关系
+                        ema5_1h = float(last_row.get('ema_5_1h', current_close))
+                        price_vs_ema5 = (current_close - ema5_1h) / ema5_1h if ema5_1h > 0 else 0
+                        
+                        # 3. 价格与布林带的位置
+                        bb_upper = float(last_row.get('bb_upper_1h', current_close))
+                        bb_lower = float(last_row.get('bb_lower_1h', current_close))
+                        bb_position = (current_close - bb_lower) / (bb_upper - bb_lower) if (bb_upper - bb_lower) > 0 else 0.5
+                        
+                        # 4. RSI 状态
+                        rsi_1h = float(last_row.get('rsi_1h', 50))
+                        
+                        # 5. MACD 状态
+                        macd_1h = float(last_row.get('macd_1h', 0))
+                        macd_signal_1h = float(last_row.get('macd_signal_1h', 0))
+                        macd_cross = 'up' if macd_1h > macd_signal_1h else 'down'
+                        
+                        # 6. 市场状态
+                        market_state = str(last_row.get('market_state', 'unknown'))
+                        
+                        # 输出诊断日志
+                        self.strategy_log(
+                            f"[入场诊断] {pair} | "
+                            f"价格: {current_close:.6f} | "
+                            f"vs 5K高点: {price_vs_recent_high:+.2%} | "
+                            f"vs EMA5: {price_vs_ema5:+.2%} | "
+                            f"布林位置: {bb_position:.2f} | "
+                            f"RSI: {rsi_1h:.1f} | "
+                            f"MACD: {macd_cross} | "
+                            f"市场: {market_state} | "
+                            f"ML入场概率: {entry_prob:.2f if entry_prob is not None else 'N/A'}"
+                        )
+                        # ========== 诊断统计结束 ==========
+                        
                         if entry_prob is not None:
                             # 确保概率在 [0, 1] 范围内（分类器输出可能有浮点误差）
                             entry_prob = max(0.0, min(1.0, entry_prob))