log增强

freqtrade/templates/freqaiprimer.py

@@ -494,13 +494,40 @@ class FreqaiPrimer(IStrategy):
         if conditions:
             combined_condition = reduce(lambda x, y: x & y, conditions)
             dataframe.loc[combined_condition, 'enter_long'] = 1
+
+            # 创建条件总结列表
+            conditions_summary = [
+                ("&-price_value_divergence", divergence_value, "<", self.buy_threshold, cond1.iloc[-1]),
+                ("volume_z_score", volume_z_score_value, ">", volume_z_score_threshold, cond2.iloc[-1]),
+                ("rsi", rsi_value, "<", rsi_threshold, cond3.iloc[-1]),
+                ("close <= bb_lowerband", bb_close_value, "<=", bb_lower_value, cond4.iloc[-1]),
+                ("stochrsi_k", stochrsi_value, "<", stochrsi_threshold, cond5.iloc[-1]),
+                ("非熊市", None, None, None, cond6.iloc[-1]),
+                ("STOCHRSI未持续超买", None, None, None, cond7.iloc[-1]),
+            ]
+
+            # 输出每个条件的状态
+            logger.info(f"[{pair}] === 买入条件检查 ===")
+            satisfied_conditions = []
+            for name, value, operator, threshold, result in conditions_summary:
+                status = "✅" if result else "❌"
+                if value is not None and threshold is not None:
+                    logger.info(f"[{pair}] {status} {name}: {value:.6f} {operator} {threshold:.6f}")
+                else:
+                    logger.info(f"[{pair}] {status} {name}")
+                if result:
+                    satisfied_conditions.append(name)
+
+            # 总结满足的条件
             if combined_condition.any():
-                logger.info(f"[{pair}] 买入信号触发，趋势得分：{trend_score:.2f}")
+                logger.info(f"[{pair}] ✅ 买入信号触发，满足条件: {', '.join(satisfied_conditions)}，趋势得分：{trend_score:.2f}")
             else:
-                logger.info(f"[{pair}] 买入条件未满足")
+                logger.info(f"[{pair}] ❌ 买入条件未满足")
+
         else:
             logger.info(f"[{pair}] 无有效买入条件")
 
+        # 记录各条件触发率
         logger.info(f"[{pair}] 各条件触发率 - "
              f"cond1: {cond1.mean():.2%}, "
              f"cond2: {cond2.mean():.2%}, "