debug log结合豆包对币对行情截图分析, 让grok进行优化 https://x.com/i/grok?conversation=1939204516592906372

freqtrade/templates/freqaiprimer.py

@@ -270,42 +270,50 @@ class FreqaiPrimer(IStrategy):
         ]
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
+            pair = metadata.get('pair', 'Unknown')
-        生成买入信号，优化条件以增加交易机会
+            conditions = []
-        """
-        pair = metadata.get('pair', 'Unknown')
-        conditions = []
 
         # 获取市场趋势得分
         trend_score = self.get_market_trend(dataframe=dataframe, metadata=metadata)
 
-        # 动态调整 cond2 和 cond3 的阈值
+        # 动态调整成交量阈值：牛市（trend_score=100）-> 0.5，熊市（trend_score=0）-> 1.5
-        # volume_z_score 阈值范围：0.5 (强牛市, trend_score=100) 到 2.0 (强熊市, trend_score=0)
         volume_z_score_min = 0.5
-        volume_z_score_max = 2.0
+        volume_z_score_max = 1.5
         volume_z_score_threshold = self.linear_map(trend_score, 0, 100, volume_z_score_max, volume_z_score_min)
 
-        # rsi 阈值范围：40 (强牛市, trend_score=100) 到 60 (强熊市, trend_score=0)
+        # 动态调整 RSI 阈值：牛市（trend_score=100）-> 40，熊市（trend_score=0）-> 60
         rsi_min = 40
         rsi_max = 60
         rsi_threshold = self.linear_map(trend_score, 0, 100, rsi_max, rsi_min)
 
+        # 新增：动态调整 STOCHRSI 阈值，牛市 -> 30，熊市 -> 50
+        stochrsi_min = 30
+        stochrsi_max = 50
+        stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)
+
         if "&-price_value_divergence" in dataframe.columns:
+            # 计算 STOCHRSI
+            stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
+            dataframe["stochrsi_k"] = stochrsi["fastk"]
+
             cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
             cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold)
             cond3 = (dataframe["rsi"] < rsi_threshold)
             cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
-            buy_condition = cond1 & cond2 & cond3 & cond4
+            cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)  # 新增 STOCHRSI 条件
+            buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
             conditions.append(buy_condition)
 
             divergence_value = dataframe['&-price_value_divergence'].iloc[-1] if not dataframe['&-price_value_divergence'].isna().all() else np.nan
             volume_z_score_value = dataframe['volume_z_score'].iloc[-1] if not dataframe['volume_z_score'].isna().all() else np.nan
             rsi_value = dataframe['rsi'].iloc[-1] if not dataframe['rsi'].isna().all() else np.nan
+            stochrsi_value = dataframe['stochrsi_k'].iloc[-1] if not dataframe['stochrsi_k'].isna().all() else np.nan
             logger.debug(f"[{pair}] 买入条件检查 - "
                         f"&-price_value_divergence={divergence_value:.6f} < {self.buy_threshold:.6f}: {cond1.iloc[-1]}, "
                         f"volume_z_score={volume_z_score_value:.2f} > {volume_z_score_threshold:.2f}: {cond2.iloc[-1]}, "
                         f"rsi={rsi_value:.2f} < {rsi_threshold:.2f}: {cond3.iloc[-1]}, "
-                        f"close={dataframe['close'].iloc[-1]:.6f} <= bb_lowerband={dataframe['bb_lowerband'].iloc[-1]:.6f}: {cond4.iloc[-1]}")
+                        f"close={dataframe['close'].iloc[-1]:.6f} <= bb_lowerband={dataframe['bb_lowerband'].iloc[-1]:.6f}: {cond4.iloc[-1]}, "
+                        f"stochrsi_k={stochrsi_value:.2f} < {stochrsi_threshold:.2f}: {cond5.iloc[-1]}")
         else:
             logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过买入信号生成")
 
@@ -320,7 +328,6 @@ class FreqaiPrimer(IStrategy):
             logger.debug(f"[{pair}] 无有效买入条件")
 
         return dataframe
-
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         pair = metadata.get('pair', 'Unknown')
         conditions = []
@@ -515,43 +522,27 @@ class FreqaiPrimer(IStrategy):
         return adjusted_rate
 
     def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
-        """
-        计算市场趋势得分，0 表示强熊，100 表示强牛，50 表示中性。
-        综合 3m、15m 和 1h 时间框架的趋势，使用对数函数映射，非线性增强两端趋势。
-        融合价格趋势、K 线形态、StochRSI 和量价关系。
-
-        参数：
-            dataframe: 可选，外部传入的 BTC/USDT 数据（3m 时间框架），默认为 None（自动获取）。
-            metadata: 可选，包含币种信息。
-
-        返回值：
-            int: 0-100 的趋势得分。
-        """
         try:
-            # 初始化得分和权重
             timeframes = ["3m", "15m", "1h"]
-            weights = {"3m": 0.5, "15m": 0.3, "1h": 0.2}  # 短期框架权重更高
+            weights = {"3m": 0.5, "15m": 0.3, "1h": 0.2}  # 默认权重
             trend_scores = {}
-
             pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'
             logger.debug(f"[{pair}] 正在计算多时间框架市场趋势得分")
 
             for tf in timeframes:
-                # 获取对应时间框架的数据
                 if tf == "3m" and dataframe is not None:
                     btc_df = dataframe
                 else:
                     btc_df = self.dp.get_pair_dataframe("BTC/USDT", tf)
 
-                # 检查数据量
+                min_candles = 200 if tf == "3m" else 100 if tf == "15m" else 50
-                min_candles = 200 if tf == "3m" else 100 if tf == "15m" else 50  # 不同时间框架所需最小数据量
                 if len(btc_df) < min_candles:
                     logger.warning(f"BTC 数据不足（{tf}，{len(btc_df)} 根K线），使用默认得分：50")
                     trend_scores[tf] = 50
                     continue
 
-                # --- 价格趋势 ---
+                # 价格趋势
-                ema_short_period = 50 if tf == "3m" else 20 if tf == "15m" else 12  # 调整周期以适应时间框架
+                ema_short_period = 50 if tf == "3m" else 20 if tf == "15m" else 12
                 ema_long_period = 200 if tf == "3m" else 80 if tf == "15m" else 50
                 btc_df["ema_short"] = ta.EMA(btc_df, timeperiod=ema_short_period)
                 btc_df["ema_long"] = ta.EMA(btc_df, timeperiod=ema_long_period)
@@ -571,7 +562,7 @@ class FreqaiPrimer(IStrategy):
                 elif ema_short_slope < -0.005:
                     price_score -= 15
 
-                # --- K 线形态 ---
+                # K线形态
                 btc_df["bullish_engulfing"] = (
                     (btc_df["close"].shift(1) < btc_df["open"].shift(1)) &
                     (btc_df["close"] > btc_df["open"]) &
@@ -588,13 +579,13 @@ class FreqaiPrimer(IStrategy):
                 kline_score = 0
                 if btc_df["bullish_engulfing"].iloc[-1]:
                     kline_score += 15
-                elif btc_df["bearish_engulfing"].iloc[-1]:
+                elif btc_df["bearish_engulfing"]:
                     kline_score -= 15
                 volatility = btc_df["close"].pct_change(10).std() * 100
                 if volatility > 0.5:
                     kline_score += 10 if price_score > 0 else -10
 
-                # --- StochRSI ---
+                # StochRSI
                 stochrsi = ta.STOCHRSI(btc_df, timeperiod=14, fastk_period=3, fastd_period=3)
                 btc_df["stochrsi_k"] = stochrsi["fastk"]
                 btc_df["stochrsi_d"] = stochrsi["fastd"]
@@ -611,7 +602,7 @@ class FreqaiPrimer(IStrategy):
                 elif stochrsi_k < 50:
                     stochrsi_score -= 5
 
-                # --- 量价关系 ---
+                # 量价关系
                 btc_df["volume_mean_20"] = btc_df["volume"].rolling(20).mean()
                 btc_df["volume_std_20"] = btc_df["volume"].rolling(20).std()
                 btc_df["volume_z_score"] = (btc_df["volume"] - btc_df["volume_mean_20"]) / btc_df["volume_std_20"]
@@ -623,7 +614,7 @@ class FreqaiPrimer(IStrategy):
                 if btc_df["adx"].iloc[-1] > 25:
                     volume_score += 10 if price_score > 0 else -10
 
-                # --- 综合得分 ---
+                # 综合得分
                 raw_score = price_score + kline_score + stochrsi_score + volume_score
                 raw_score = max(min(raw_score, 50), -50)
 
@@ -638,14 +629,19 @@ class FreqaiPrimer(IStrategy):
                             f"价格得分：{price_score}, K线得分：{kline_score}, "
                             f"StochRSI得分：{stochrsi_score}, 量价得分：{volume_score}")
 
-            # 加权融合多时间框架得分
+            # 动态调整权重：当 1h 得分显著高于 3m（差值 > 20），提高 1h 权重
+            if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20:
+                weights = {"3m": 0.3, "15m": 0.3, "1h": 0.4}
+                logger.debug(f"[{pair}] 1h 趋势得分（{trend_scores.get('1h', 50)}）显著高于 3m（{trend_scores.get('3m', 50)}），调整权重为 {weights}")
+
+            # 加权融合
             final_score = sum(trend_scores[tf] * weights[tf] for tf in timeframes)
             final_score = int(round(final_score))
             final_score = max(0, min(100, final_score))
 
             logger.info(f"[{pair}] 最终趋势得分：{final_score}, "
-                    f"3m得分：{trend_scores.get('3m', 50)}, 15m得分：{trend_scores.get('15m', 50)}, "
+                       f"3m得分：{trend_scores.get('3m', 50)}, 15m得分：{trend_scores.get('15m', 50)}, "
-                    f"1h得分：{trend_scores.get('1h', 50)}")
+                       f"1h得分：{trend_scores.get('1h', 50)}")
             return final_score
 
         except Exception as e: