redis存储trend_score

freqtrade/templates/freqaiprimer.py

@@ -1038,6 +1038,39 @@ class FreqaiPrimer(IStrategy):
             logger.error(f"[{pair}] 获取市场趋势失败：{e}", exc_info=True)
             return 50
 
+    def get_trend_score_with_cache(self, pair: str, timeframe: str, timestamp: int, dataframe: DataFrame, metadata: dict) -> float:
+        """
+        获取趋势得分，优先从 Redis 缓存中读取，如果缓存未命中则计算并存储到 Redis。
+        """
+        import redis
+        from datetime import datetime
+
+        # 创建 Redis 客户端
+        if not hasattr(self, 'redis_client') and self.redis_url:
+            self.redis_client = redis.from_url(self.redis_url)
+            logger.info("✅ Redis 客户端已初始化")
+
+        # 生成缓存键
+        cache_key = f"trend_score:{pair}:{timeframe}:{timestamp}"
+
+        # 尝试从 Redis 获取趋势得分
+        if self.redis_client:
+            cached_score = self.redis_client.get(cache_key)
+            if cached_score is not None:
+                logger.info(f"[{pair}] 从 Redis 缓存中获取 trend_score: {float(cached_score):.2f}")
+                return float(cached_score)
+
+        # 缓存未命中，计算趋势得分
+        trend_score = self.get_market_trend(dataframe=dataframe, metadata=metadata)
+        logger.info(f"[{pair}] 计算 trend_score: {trend_score:.2f}")
+
+        # 将趋势得分存储到 Redis，设置过期时间为 1 天
+        if self.redis_client:
+            self.redis_client.setex(cache_key, 86400, trend_score)
+            logger.info(f"[{pair}] 已将 trend_score 存储到 Redis，key={cache_key}, value={trend_score:.2f}")
+
+        return trend_score
+
     def detect_trend_status(self, dataframe: DataFrame, metadata: dict) -> str:
         """
         基于加权分段的trend_score判断趋势状态
@@ -1060,7 +1093,9 @@ class FreqaiPrimer(IStrategy):
                 # 获取历史数据片段
                 hist_df = dataframe.iloc[:i+1] if i != -1 else dataframe
                 if len(hist_df) > 0:
-                    score = self.get_market_trend(hist_df, metadata)
+            # 使用新的 get_trend_score_with_cache 方法替代直接调用 get_market_trend
+            timestamp = int(hist_df.index[-1].timestamp())  # 获取时间戳
+            score = self.get_trend_score_with_cache(pair=pair, timeframe=self.timeframe, timestamp=timestamp, dataframe=hist_df, metadata=metadata)
                     trend_scores_20.append(score)
                 else:
                     trend_scores_20.append(50)  # 默认值