入场条件更严格, 考虑4h stochrsi

freqtrade/templates/freqaiprimer.py

@@ -9,6 +9,7 @@ from functools import reduce
 from freqtrade.persistence import Trade
 import talib.abstract as ta
 from pandas import DataFrame
+import pandas as pd
 from typing import Dict
 from freqtrade.strategy import (DecimalParameter, IStrategy, IntParameter)
 
@@ -160,6 +161,62 @@ class FreqaiPrimer(IStrategy):
         dataframe["volume_z_score"] = dataframe["volume_z_score"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
 
         return dataframe
+    def is_stochrsi_overbought(self, dataframe: DataFrame, period=10, threshold=85) -> bool:
+        """
+        判断当前 STOCHRSI 是否在过去 N 根 K 线中平均高于阈值（如 85）
+        """
+        if 'stochrsi_k' not in dataframe.columns:
+            # 如果列不存在，返回全 False 的 Series
+            return pd.Series([False] * len(dataframe), index=dataframe.index)
+        # 计算滚动平均值并判断是否超过阈值
+        avg_stochrsi = dataframe['stochrsi_k'].rolling(window=period).mean()
+        return avg_stochrsi > threshold
+
+    def is_bearish_market(self, dataframe: DataFrame, metadata: dict, timeframe: str = "4h") -> DataFrame:
+        """
+        判断是否为熊市信号（使用已有的 4h 指标）
+        返回一个与 dataframe 等长的布尔 Series
+        """
+
+        # 条件1：价格低于布林带中轨
+        cond1 = dataframe['close'] < dataframe['bb_middle']
+
+        # 条件2：STOCHRSI 超买后反转（K > 80 并开始向下交叉 D）
+        cond2 = (dataframe['stochrsi_k'] > 80) & (dataframe['stochrsi_k'] < dataframe['stochrsi_d'])
+
+        # 条件3：出现看跌蜡烛图形态（如乌云盖顶、看跌吞没等）
+        def detect_candlestick_bearish(df, i):
+            open = df['open'].iloc[i]
+            high = df['high'].iloc[i]
+            low = df['low'].iloc[i]
+            close = df['close'].iloc[i]
+            prev_open = df['open'].iloc[i - 1] if i > 0 else open
+            prev_close = df['close'].iloc[i - 1] if i > 0 else close
+
+            # 看跌吞没形态
+            if open < prev_close and close > prev_open and close < prev_close:
+                return True
+            # 乌云盖顶
+            if close < prev_open and open > prev_close and close < ((prev_open + prev_close) / 2):
+                return True
+            # 射击之星
+            body = abs(open - close)
+            upper_wick = high - max(open, close)
+            lower_wick = min(open, close) - low
+            if upper_wick > 2 * body and lower_wick < body:
+                return True
+
+            return False
+
+        # 在 dataframe 上逐行应用蜡烛图识别
+        bearish_candle = pd.Series(index=dataframe.index, dtype=bool)
+        for i in range(len(dataframe)):
+            bearish_candle.iloc[i] = detect_candlestick_bearish(dataframe, i)
+
+        # 构造最终的熊市信号
+        bearish_signal = cond1 & cond2 & bearish_candle
+
+        return bearish_signal
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         pair = metadata.get('pair', 'Unknown')
@@ -190,10 +247,24 @@ class FreqaiPrimer(IStrategy):
         dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
         dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
         dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
-        # 计算 STOCHRSI
-        dataframe['stochrsi_k'], dataframe['stochrsi_d'] = stochrsi(dataframe['close'], length=14, k=3, d=3, fillna=True)
-        # 计算 MASTCHRIS3 (假设为 STOCHRSI K 的 3 周期 SMA)
-        dataframe['mastchris3'] = sma(dataframe['stochrsi_k'], length=3)
+        # 获取 4h 时间框架的数据
+        dataframe_4h = self.dp.get_pair_dataframe(pair=pair, timeframe="4h")
+
+        # 在 4h 上计算 STOCHRSI 和布林带等指标
+        stochrsi = ta.STOCHRSI(dataframe_4h, timeperiod=14, fastk_period=3, fastd_period=3)
+        dataframe_4h['stochrsi_k'] = stochrsi['fastk']
+        dataframe_4h['stochrsi_d'] = stochrsi['fastd']
+
+        real = ta.TYPPRICE(dataframe_4h)
+        upperband, middleband, lowerband = ta.BBANDS(real, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
+        dataframe_4h['bb_upper'] = upperband
+        dataframe_4h['bb_middle'] = middleband
+        dataframe_4h['bb_lower'] = lowerband
+
+        # 将 4h 指标映射回主时间框架（3m）
+        for col in ['stochrsi_k', 'stochrsi_d', 'bb_upper', 'bb_middle', 'bb_lower']:
+            dataframe[col + '_4h'] = dataframe_4h[col].reindex(dataframe.index, method='ffill').fillna(method='bfill')
+
         # 数据清理
         for col in ["ema200", "bb_upperband", "bb_middleband", "bb_lowerband", "rsi", "volume_z_score", "&-price_value_divergence", "price_value_divergence"]:
             dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
@@ -292,49 +363,98 @@ class FreqaiPrimer(IStrategy):
         # 获取市场趋势得分
         trend_score = self.get_market_trend(dataframe=dataframe, metadata=metadata)
 
-        # 动态调整成交量阈值：牛市（trend_score=100）-> 0.5，熊市（trend_score=0）-> 1.5
+        # 动态调整成交量阈值
         volume_z_score_min = 0.5
         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)
 
-        # 动态调整 RSI 阈值：牛市（trend_score=100）-> 40，熊市（trend_score=0）-> 60
+        # 动态调整 RSI 阈值
         rsi_min = 35
         rsi_max = 55
         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
+        # 新增：动态调整 STOCHRSI 阈值
         stochrsi_min = 25
         stochrsi_max = 45
         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"]
-            # 添加 15m 时间框架的 EMA200
-            dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
-            dataframe_15m["ema200_15m"] = ta.EMA(dataframe_15m, timeperiod=200)
-            dataframe["ema200_15m"] = dataframe_15m["ema200_15m"].reindex(dataframe.index, method="ffill")
+        # 获取 4h 时间框架的数据
+        dataframe_4h = self.dp.get_pair_dataframe(pair=pair, timeframe="4h")
+        # 确保指标存在（或提前在 populate_indicators 中计算好）
+        if not dataframe_4h.empty and 'stochrsi_k' in dataframe_4h.columns:
+            stochrsi_overbought = self.is_stochrsi_overbought(dataframe_4h, period=10, threshold=85)
+        else:
+            stochrsi_overbought = pd.Series([False] * len(dataframe), index=dataframe.index)
 
+        # 映射回主时间框架
+        stochrsi_overbought_aligned = stochrsi_overbought.reindex(dataframe.index, method='ffill').fillna(True)
+        # 计算指标（或提前在 populate_indicators 中完成）
+        stochrsi = ta.STOCHRSI(dataframe_4h, timeperiod=14, fastk_period=3, fastd_period=3)
+        dataframe_4h['stochrsi_k'] = stochrsi['fastk']
+        dataframe_4h['stochrsi_d'] = stochrsi['fastd']
+
+        real = ta.TYPPRICE(dataframe_4h)
+        upperband, middleband, lowerband = ta.BBANDS(real, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
+        dataframe_4h['bb_upper'] = upperband
+        dataframe_4h['bb_middle'] = middleband
+        dataframe_4h['bb_lower'] = lowerband
+
+        # 使用已经准备好的 dataframe_4h
+        bearish_signal = self.is_bearish_market(dataframe_4h, metadata, timeframe="4h")
+
+        # 映射回主时间框架
+        dataframe['bearish_signal'] = bearish_signal.reindex(dataframe.index, method='ffill').fillna(False)
+
+
+        # 现在你可以使用 dataframe_4h 做任何判断了
+        if not dataframe_4h.empty:
+            logger.info(f"[{pair}] 成功获取 4h 数据，共 {len(dataframe_4h)} 根 K 线")
+            # 示例：打印最新一根 K 线的收盘价
+            latest_close = dataframe_4h['close'].iloc[-1]
+            logger.info(f"[{pair}] 4h 最新收盘价: {latest_close}")
+        else:
+            logger.warning(f"[{pair}] 无法获取 4h 数据")
+        if "&-price_value_divergence" in dataframe.columns:
+            divergence_value = dataframe['&-price_value_divergence'].iloc[-1]
+            volume_z_score_value = dataframe['volume_z_score'].iloc[-1]
+            rsi_value = dataframe['rsi'].iloc[-1]
+            stochrsi_value = dataframe['stochrsi_k'].iloc[-1]
+            bb_close_value = dataframe['close'].iloc[-1]
+            bb_lower_value = dataframe['bb_lowerband'].iloc[-1]
             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"])
             cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
-            buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
+            cond6 = ~self.is_bearish_market(dataframe_4h, metadata, timeframe="4h")
+            # cond7 定义
+            cond7 = ~stochrsi_overbought_aligned
+
+            buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7
+
+            conditions.append(buy_condition)
+
+            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={bb_close_value:.6f} <= bb_lowerband={bb_lower_value:.6f}: {cond4.iloc[-1]}, "
+                    f"stochrsi_k={stochrsi_value:.2f} < {stochrsi_threshold:.2f}: {cond5.iloc[-1]}, "
+                    f"非熊市={cond6.iloc[-1]}, "
+                    f"STOCHRSI未持续超买={cond7.iloc[-1]}")
+            bearish_signal = cond6.iloc[-1]
+
+            buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6
 
             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"stochrsi_k={stochrsi_value:.2f} < {stochrsi_threshold:.2f}: {cond5.iloc[-1]}")
+                        f"close={bb_close_value:.6f} <= bb_lowerband={bb_lower_value:.6f}: {cond4.iloc[-1]}, "
+                        f"stochrsi_k={stochrsi_value:.2f} < {stochrsi_threshold:.2f}: {cond5.iloc[-1]}, "
+                        f"非熊市={bearish_signal}")
         else:
             logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过买入信号生成")
 
@@ -553,10 +673,6 @@ class FreqaiPrimer(IStrategy):
         # 获取当前数据
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1]
-        
-        # 检查 STOCHRSI K 和 MASTCHRIS3 是否均 > 85
-        if last_candle['stochrsi_k'] > 85 and last_candle['mastchris3'] > 85:
-            return False  # 阻止开多
 
         market_trend_score = self.get_market_trend(dataframe=DataFrame, metadata={'pair': pair})
         cooldown_period_minutes = self.COOLDOWN_PERIOD_MINUTES.value if market_trend_score > 50 else self.COOLDOWN_PERIOD_MINUTES.value // 2
@@ -576,7 +692,6 @@ class FreqaiPrimer(IStrategy):
             return False
 
         return True
-
     def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
              rate: float, time_in_force: str, exit_reason: str,
              current_time: datetime, **kwargs) -> bool: