support SAR

config.py

@@ -73,7 +73,7 @@ OKX_MONITOR_CONFIG = {
 US_STOCK_MONITOR_CONFIG = {
     "volume_monitor":{
         "symbols": ["QQQ", "TQQQ", "MSFT", "AAPL", "GOOG", "NVDA", "META", "AMZN", "TSLA", "AVGO"],
-        "bars": ["5", "15m", "30m", "1H"],
+        "bars": ["5m", "15m", "30m", "1H"],
         "initial_date": "2015-08-31 00:00:00"
     }
 }

core/biz/metrics_calculation.py

@@ -137,6 +137,64 @@ class MetricsCalculation:
         ] = "死叉"
         return df
 
+    def sar(self, df: pd.DataFrame, acceleration=0.02, maximum=0.2):
+        """
+        计算SAR（抛物线转向指标）
+
+        Args:
+            df: 包含high, low, close列的DataFrame
+            acceleration: 加速因子，默认0.02。控制SAR值随价格变化的加速程度
+            maximum: 最大加速因子，默认0.2。设定加速因子的上限，防止过度增加
+
+        参数说明：
+        - acceleration=0.02: 标准设置，适合大多数市场
+        - maximum=0.2: 标准设置，防止SAR过度敏感
+        - 对于高波动性市场（如加密货币），可适当增加acceleration到0.03-0.04
+        - 对于低波动性市场，可降低acceleration到0.015-0.02
+        """
+        logger.info(f"计算SAR指标，acceleration={acceleration}, maximum={maximum}")
+        # 初始化sar和sar_signal列
+        df["sar"] = np.nan
+        df["sar_signal"] = ""
+
+        df["sar"] = tb.SAR(
+            df["high"], df["low"], acceleration=acceleration, maximum=maximum
+        )
+        # sar_position = df["sar"] > df["close"]
+        # df.loc[
+        #     sar_position[
+        #         (sar_position == True) & (sar_position.shift() == False)
+        #     ].index,
+        #     "sar_signal",
+        # ] = "SAR多头"
+        # df.loc[
+        #     sar_position[
+        #         (sar_position == False) & (sar_position.shift() == True)
+        #     ].index,
+        #     "sar_signal",
+        # ] = "SAR空头"
+        # df.loc[sar_position[sar_position == False].index, "sar_signal"] = "SAR观望"
+
+        # 生成交易信号
+        # SAR多头: SAR < close
+        # SAR空头: SAR > close
+        # SAR观望: SAR == close 或 SAR为NaN
+        df["sar_signal"] = np.where(
+            df["sar"].isna(),
+            "SAR观望",
+            np.where(
+                df["sar"] < df["close"],
+                "SAR多头",
+                np.where(df["sar"] > df["close"], "SAR空头", "SAR观望"),
+            ),
+        )
+
+        # 确保sar列为float类型
+        df["sar"] = df["sar"].astype(float)
+        # 确保sar_signal列为str类型
+        df["sar_signal"] = df["sar_signal"].astype(str)
+        return df
+
     def set_kdj_pattern(self, df: pd.DataFrame):
         """
         设置每一根K线数据对应的KDJ形态超买超卖情况
@@ -209,7 +267,7 @@ class MetricsCalculation:
         # 震荡条件已经在初始化时设置，无需额外处理
         data["ma_long_short"] = "震荡"
         data = self._trend_strength_method(data)
-        
+
         # 计算各均线偏离度的标准差和均值
         data["ma_divergence"] = "未知"
         ma_diffs = data[
@@ -421,7 +479,7 @@ class MetricsCalculation:
         df["ma30"] = df["close"].rolling(window=30).mean().dropna()
 
         df["ma_cross"] = ""
-        
+
         # 定义均线交叉检测函数
         def detect_cross(short_ma, long_ma, short_name, long_name):
             """检测均线交叉"""
@@ -429,22 +487,22 @@ class MetricsCalculation:
             cross_up = (position == True) & (position.shift() == False)
             cross_down = (position == False) & (position.shift() == True)
             return cross_up, cross_down
-        
+
         # 检测所有均线交叉
         crosses = {}
-        
+
         # MA5与其他均线的交叉
         ma5_ma10_up, ma5_ma10_down = detect_cross("ma5", "ma10", "5", "10")
         ma5_ma20_up, ma5_ma20_down = detect_cross("ma5", "ma20", "5", "20")
         ma5_ma30_up, ma5_ma30_down = detect_cross("ma5", "ma30", "5", "30")
-        
+
         # MA10与其他均线的交叉
         ma10_ma20_up, ma10_ma20_down = detect_cross("ma10", "ma20", "10", "20")
         ma10_ma30_up, ma10_ma30_down = detect_cross("ma10", "ma30", "10", "30")
-        
+
         # MA20与MA30的交叉
         ma20_ma30_up, ma20_ma30_down = detect_cross("ma20", "ma30", "20", "30")
-        
+
         # 存储上穿信号
         crosses["5上穿10"] = ma5_ma10_up
         crosses["5上穿20"] = ma5_ma20_up
@@ -452,7 +510,7 @@ class MetricsCalculation:
         crosses["10上穿20"] = ma10_ma20_up
         crosses["10上穿30"] = ma10_ma30_up
         crosses["20上穿30"] = ma20_ma30_up
-        
+
         # 存储下穿信号
         crosses["10下穿5"] = ma5_ma10_down
         crosses["20下穿10"] = ma10_ma20_down
@@ -460,22 +518,22 @@ class MetricsCalculation:
         crosses["30下穿20"] = ma20_ma30_down
         crosses["30下穿10"] = ma10_ma30_down
         crosses["30下穿5"] = ma5_ma30_down
-        
+
         # 分析每个时间点的交叉组合
         for idx in df.index:
             current_crosses = []
-            
+
             # 检查当前时间点的所有交叉信号
             for cross_name, cross_signal in crosses.items():
                 if cross_signal.loc[idx]:
                     current_crosses.append(cross_name)
-            
+
             # 根据交叉类型组合信号
             if len(current_crosses) > 0:
                 # 分离上穿和下穿信号
                 up_crosses = [c for c in current_crosses if "上穿" in c]
                 down_crosses = [c for c in current_crosses if "下穿" in c]
-                
+
                 # 组合信号
                 if len(up_crosses) > 1:
                     # 多个上穿信号
@@ -486,7 +544,7 @@ class MetricsCalculation:
                 else:
                     # 单个交叉信号
                     df.loc[idx, "ma_cross"] = current_crosses[0]
-        
+
         return df
 
     def rsi(self, df: pd.DataFrame):
@@ -726,13 +784,21 @@ class MetricsCalculation:
         )  # 下影线长度
 
         # 计算实体占比
-        df["open_close_fill"] = df["open_close_diff"] / df["high_low_diff"].replace(0, np.nan)
+        df["open_close_fill"] = df["open_close_diff"] / df["high_low_diff"].replace(
+            0, np.nan
+        )
         df["open_close_fill"] = df["open_close_fill"].fillna(1.0)  # 处理除零情况
 
         # 计算影线占比
-        df["upper_shadow_ratio"] = df["high_close_diff"] / df["high_low_diff"].replace(0, np.nan)
+        df["upper_shadow_ratio"] = df["high_close_diff"] / df["high_low_diff"].replace(
-        df["lower_shadow_ratio"] = df["low_close_diff"] / df["high_low_diff"].replace(0, np.nan)
+            0, np.nan
-        df["upper_shadow_ratio"] = df["upper_shadow_ratio"].fillna(0)  # 无波动时影线占比为 0
+        )
+        df["lower_shadow_ratio"] = df["low_close_diff"] / df["high_low_diff"].replace(
+            0, np.nan
+        )
+        df["upper_shadow_ratio"] = df["upper_shadow_ratio"].fillna(
+            0
+        )  # 无波动时影线占比为 0
         df["lower_shadow_ratio"] = df["lower_shadow_ratio"].fillna(0)
 
         # 初始化k_shape列
@@ -760,15 +826,23 @@ class MetricsCalculation:
         )
 
         # 计算滚动窗口内 price_range_ratio 和 price_range_zscore 的分位数
-        df["price_range_ratio_p75"] = df["price_range_ratio"].rolling(window=window_size, min_periods=1).quantile(0.75)
+        df["price_range_ratio_p75"] = (
-        df["price_range_zscore_p75"] = df["price_range_zscore"].rolling(window=window_size, min_periods=1).quantile(0.75)
+            df["price_range_ratio"]
+            .rolling(window=window_size, min_periods=1)
+            .quantile(0.75)
+        )
+        df["price_range_zscore_p75"] = (
+            df["price_range_zscore"]
+            .rolling(window=window_size, min_periods=1)
+            .quantile(0.75)
+        )
 
         # 识别“一字”形态：波动极小（Z 分数 < -1.0 或 price_range_ratio < 0.05%）且无影线
         one_line_condition = (
-            ((df["price_range_zscore"] < -1.0) | (df["price_range_ratio"] < 0.05)) &
+            ((df["price_range_zscore"] < -1.0) | (df["price_range_ratio"] < 0.05))
-            (df["upper_shadow_ratio"] <= 0.01) &  # 上影线极小或无
+            & (df["upper_shadow_ratio"] <= 0.01)  # 上影线极小或无
-            (df["lower_shadow_ratio"] <= 0.01) &  # 下影线极小或无
+            & (df["lower_shadow_ratio"] <= 0.01)  # 下影线极小或无
-            (df["open_close_diff"] / df["close"] < 0.0005)  # 开收盘价差小于0.05%
+            & (df["open_close_diff"] / df["close"] < 0.0005)  # 开收盘价差小于0.05%
         )
         df.loc[one_line_condition, "k_shape"] = "一字"
 
@@ -857,10 +931,18 @@ class MetricsCalculation:
             & (df["open_close_fill"] <= 0.55)
             & (df["k_shape"] != "一字")
         )
-        df.loc[small_body_condition_2 
+        df.loc[
-        & (df["upper_shadow_ratio"] >= 0.25) & (df["k_shape"] == "未知"), "k_shape"] = "长上影线纺锤体"
+            small_body_condition_2
-        df.loc[small_body_condition_2 
+            & (df["upper_shadow_ratio"] >= 0.25)
-        & (df["lower_shadow_ratio"] >= 0.25) & (df["k_shape"] == "未知"), "k_shape"] = "长下影线纺锤体"
+            & (df["k_shape"] == "未知"),
+            "k_shape",
+        ] = "长上影线纺锤体"
+        df.loc[
+            small_body_condition_2
+            & (df["lower_shadow_ratio"] >= 0.25)
+            & (df["k_shape"] == "未知"),
+            "k_shape",
+        ] = "长下影线纺锤体"
         df.loc[small_body_condition_2 & (df["k_shape"] == "未知"), "k_shape"] = "小实体"
 
         # 大实体：实体占比55%-90%
@@ -873,16 +955,20 @@ class MetricsCalculation:
 
         # 识别“超大实体”形态：实体占比 75%-90%，价格波动显著，且非“一字”或“大实体”
         super_large_body_condition = (
-            (df["open_close_fill"] > 0.75) &
+            (df["open_close_fill"] > 0.75)
-            (df["open_close_fill"] <= 1) &
+            & (df["open_close_fill"] <= 1)
-            (df["price_range_ratio"] >= df["price_range_ratio_p75"]) &  # 价格波动范围超过75th分位数
+            & (
-            (df["k_shape"] != "一字")
+                df["price_range_ratio"] >= df["price_range_ratio_p75"]
+            )  # 价格波动范围超过75th分位数
+            & (df["k_shape"] != "一字")
         )
         df.loc[super_large_body_condition, "k_shape"] = "超大实体"
 
         # 光头光脚：实体占比>90%（非一字情况）
         bald_body_condition = (df["open_close_fill"] > 0.9) & (df["k_shape"] != "一字")
-        df.loc[bald_body_condition & (df["k_shape"] == "超大实体"), "k_shape"] = "超大实体+光头光脚"
+        df.loc[bald_body_condition & (df["k_shape"] == "超大实体"), "k_shape"] = (
+            "超大实体+光头光脚"
+        )
         df.loc[bald_body_condition & (df["k_shape"] == "未知"), "k_shape"] = "光头光脚"
 
         # 清理临时列
@@ -911,7 +997,7 @@ class MetricsCalculation:
     def set_ma_long_short_advanced(self, data: pd.DataFrame, method="weighted_voting"):
         """
         高级均线多空判定方法，提供多种科学的判定策略
-        
+
         Args:
             data: 包含均线数据的DataFrame
             method: 判定方法
@@ -922,7 +1008,7 @@ class MetricsCalculation:
                 - "hybrid": 混合方法
         """
         logger.info(f"使用{method}方法设置均线多空")
-        
+
         if method == "weighted_voting":
             return self._weighted_voting_method(data)
         elif method == "trend_strength":
@@ -936,20 +1022,20 @@ class MetricsCalculation:
         else:
             logger.warning(f"未知的方法: {method}，使用默认加权投票方法")
             return self._weighted_voting_method(data)
-    
+
     def _weighted_voting_method(self, data: pd.DataFrame):
         """加权投票机制：短期均线权重更高"""
         # 权重设置：短期均线权重更高
         weights = {
-            "ma5_close_diff": 0.4,   # 40%权重
+            "ma5_close_diff": 0.4,  # 40%权重
             "ma10_close_diff": 0.3,  # 30%权重
             "ma20_close_diff": 0.2,  # 20%权重
-            "ma30_close_diff": 0.1   # 10%权重
+            "ma30_close_diff": 0.1,  # 10%权重
         }
-        
+
         # 计算加权得分
         weighted_score = sum(data[col] * weight for col, weight in weights.items())
-        
+
         # 动态阈值：基于历史分布
         window_size = min(50, len(data) // 4)
         if window_size > 10:
@@ -960,69 +1046,78 @@ class MetricsCalculation:
         else:
             long_threshold = 0.3
             short_threshold = -0.3
-        
+
         # 判定逻辑
         data.loc[weighted_score > long_threshold, "ma_long_short"] = "多"
         data.loc[weighted_score < short_threshold, "ma_long_short"] = "空"
-        
+
         return data
-    
+
     def _trend_strength_method(self, data: pd.DataFrame):
         """趋势强度评估：考虑偏离幅度和趋势持续性"""
         # 计算趋势强度（考虑偏离幅度）
         trend_strength = data["ma_close_avg"]
-        
+
         # 计算趋势持续性（连续同向的周期数）
         trend_persistence = self._calculate_trend_persistence(data)
-        
+
         # 综合评分
         strength_threshold = 0.5
         persistence_threshold = 3  # 至少连续3个周期
-        
+
-        long_condition = (trend_strength > strength_threshold) & (trend_persistence >= persistence_threshold)
+        long_condition = (trend_strength > strength_threshold) & (
-        short_condition = (trend_strength < -strength_threshold) & (trend_persistence >= persistence_threshold)
+            trend_persistence >= persistence_threshold
-        
+        )
+        short_condition = (trend_strength < -strength_threshold) & (
+            trend_persistence >= persistence_threshold
+        )
+
         data.loc[long_condition, "ma_long_short"] = "多"
         data.loc[short_condition, "ma_long_short"] = "空"
-        
+
         return data
-    
+
     def _ma_alignment_method(self, data: pd.DataFrame):
         """均线排列分析：检查均线的排列顺序和间距"""
         # 检查均线排列顺序
         ma_alignment_score = 0
-        
+
         # 多头排列：MA5 > MA10 > MA20 > MA30
         bullish_alignment = (
-            (data["ma5_close_diff"] > data["ma10_close_diff"]) &
+            (data["ma5_close_diff"] > data["ma10_close_diff"])
-            (data["ma10_close_diff"] > data["ma20_close_diff"]) &
+            & (data["ma10_close_diff"] > data["ma20_close_diff"])
-            (data["ma20_close_diff"] > data["ma30_close_diff"])
+            & (data["ma20_close_diff"] > data["ma30_close_diff"])
         )
-        
+
         # 空头排列：MA5 < MA10 < MA20 < MA30
         bearish_alignment = (
-            (data["ma5_close_diff"] < data["ma10_close_diff"]) &
+            (data["ma5_close_diff"] < data["ma10_close_diff"])
-            (data["ma10_close_diff"] < data["ma20_close_diff"]) &
+            & (data["ma10_close_diff"] < data["ma20_close_diff"])
-            (data["ma20_close_diff"] < data["ma30_close_diff"])
+            & (data["ma20_close_diff"] < data["ma30_close_diff"])
         )
-        
+
         # 计算均线间距的合理性
         ma_spacing = self._calculate_ma_spacing(data)
-        
+
         # 综合判定
         long_condition = bullish_alignment & (ma_spacing > 0.2)
         short_condition = bearish_alignment & (ma_spacing > 0.2)
-        
+
         data.loc[long_condition, "ma_long_short"] = "多"
         data.loc[short_condition, "ma_long_short"] = "空"
-        
+
         return data
-    
+
     def _statistical_method(self, data: pd.DataFrame):
         """统计分布方法：基于历史分位数和Z-score"""
         # 计算各均线偏离度的Z-score
-        ma_cols = ["ma5_close_diff", "ma10_close_diff", "ma20_close_diff", "ma30_close_diff"]
+        ma_cols = [
-        
+            "ma5_close_diff",
+            "ma10_close_diff",
+            "ma20_close_diff",
+            "ma30_close_diff",
+        ]
+
         # 使用滚动窗口计算Z-score
         window_size = min(30, len(data) // 4)
         if window_size > 10:
@@ -1031,44 +1126,46 @@ class MetricsCalculation:
                 rolling_mean = data[col].rolling(window=window_size).mean()
                 rolling_std = data[col].rolling(window=window_size).std()
                 z_scores[col] = (data[col] - rolling_mean) / rolling_std
-            
+
             # 计算综合Z-score
             avg_z_score = z_scores.mean(axis=1)
-            
+
             # 基于Z-score判定
             long_condition = avg_z_score > 0.5
             short_condition = avg_z_score < -0.5
-            
+
             data.loc[long_condition, "ma_long_short"] = "多"
             data.loc[short_condition, "ma_long_short"] = "空"
-        
+
         return data
-    
+
     def _hybrid_method(self, data: pd.DataFrame):
         """混合方法：结合多种判定策略"""
         # 1. 加权投票得分
-        weights = {"ma5_close_diff": 0.4, "ma10_close_diff": 0.3, 
+        weights = {
-                  "ma20_close_diff": 0.2, "ma30_close_diff": 0.1}
+            "ma5_close_diff": 0.4,
+            "ma10_close_diff": 0.3,
+            "ma20_close_diff": 0.2,
+            "ma30_close_diff": 0.1,
+        }
         weighted_score = sum(data[col] * weight for col, weight in weights.items())
-        
+
         # 2. 均线排列得分
         alignment_score = (
-            (data["ma5_close_diff"] >= data["ma10_close_diff"]) * 0.25 +
+            (data["ma5_close_diff"] >= data["ma10_close_diff"]) * 0.25
-            (data["ma10_close_diff"] >= data["ma20_close_diff"]) * 0.25 +
+            + (data["ma10_close_diff"] >= data["ma20_close_diff"]) * 0.25
-            (data["ma20_close_diff"] >= data["ma30_close_diff"]) * 0.25 +
+            + (data["ma20_close_diff"] >= data["ma30_close_diff"]) * 0.25
-            (data["ma_close_avg"] > 0) * 0.25
+            + (data["ma_close_avg"] > 0) * 0.25
         )
-        
+
         # 3. 趋势强度得分
         strength_score = data["ma_close_avg"].abs()
-        
+
         # 4. 综合评分
         composite_score = (
-            weighted_score * 0.4 +
+            weighted_score * 0.4 + alignment_score * 0.3 + strength_score * 0.3
-            alignment_score * 0.3 +
-            strength_score * 0.3
         )
-        
+
         # 动态阈值
         window_size = min(50, len(data) // 4)
         if window_size > 10:
@@ -1079,38 +1176,44 @@ class MetricsCalculation:
         else:
             long_threshold = 0.4
             short_threshold = -0.4
-        
+
         # 判定
         long_condition = composite_score > long_threshold
         short_condition = composite_score < short_threshold
-        
+
         data.loc[long_condition, "ma_long_short"] = "多"
         data.loc[short_condition, "ma_long_short"] = "空"
-        
+
         return data
-    
+
     def _calculate_trend_persistence(self, data: pd.DataFrame):
         """计算趋势持续性"""
         trend_persistence = pd.Series(0, index=data.index)
-        
+
         for i in range(1, len(data)):
-            if data["ma_close_avg"].iloc[i] > 0 and data["ma_close_avg"].iloc[i-1] > 0:
+            if (
-                trend_persistence.iloc[i] = trend_persistence.iloc[i-1] + 1
+                data["ma_close_avg"].iloc[i] > 0
-            elif data["ma_close_avg"].iloc[i] < 0 and data["ma_close_avg"].iloc[i-1] < 0:
+                and data["ma_close_avg"].iloc[i - 1] > 0
-                trend_persistence.iloc[i] = trend_persistence.iloc[i-1] + 1
+            ):
+                trend_persistence.iloc[i] = trend_persistence.iloc[i - 1] + 1
+            elif (
+                data["ma_close_avg"].iloc[i] < 0
+                and data["ma_close_avg"].iloc[i - 1] < 0
+            ):
+                trend_persistence.iloc[i] = trend_persistence.iloc[i - 1] + 1
             else:
                 trend_persistence.iloc[i] = 0
-        
+
         return trend_persistence
-    
+
     def _calculate_ma_spacing(self, data: pd.DataFrame):
         """计算均线间距的合理性"""
         # 计算相邻均线之间的间距
         spacing_5_10 = abs(data["ma5_close_diff"] - data["ma10_close_diff"])
         spacing_10_20 = abs(data["ma10_close_diff"] - data["ma20_close_diff"])
         spacing_20_30 = abs(data["ma20_close_diff"] - data["ma30_close_diff"])
-        
+
         # 平均间距
         avg_spacing = (spacing_5_10 + spacing_10_20 + spacing_20_30) / 3
-        
+
         return avg_spacing

core/db/db_market_data.py

@@ -44,6 +44,8 @@ class DBMarketData:
         "kdj_j",
         "kdj_signal",
         "kdj_pattern",
+        "sar",
+        "sar_signal",
         "ma5",
         "ma10",
         "ma20",
@@ -471,7 +473,7 @@ class DBMarketData:
         
         return self.db_manager.query_data(sql, condition_dict, return_multi=False)
     
-    def query_market_data_by_symbol_bar(self, symbol: str, bar: str, start: str, end: str):
+    def query_market_data_by_symbol_bar(self, symbol: str, bar: str, start: str = None, end: str = None):
         """
         根据交易对和K线周期查询数据
         :param symbol: 交易对

core/trade/orb_trade.py

@@ -134,13 +134,20 @@ class ORBStrategy:
 
         return int(max_shares)  # 股数取整
 
-    def generate_orb_signals(self):
+    def generate_orb_signals(self, direction: str = None, by_sar: bool = False):
         """
         生成ORB策略信号（每日仅1次交易机会）
         - 第一根5分钟K线：确定开盘区间（High1, Low1）
         - 第二根5分钟K线：根据第一根K线方向生成多空信号
+        :param direction: 方向，None=自动，Long=多头，Short=空头
+        :param by_sar: 是否根据SAR指标生成信号，True=是，False=否
         """
-        logger.info("开始生成ORB策略信号")
+        direction_desc = "既做多又做空"
+        if direction == "Long":
+            direction_desc = "做多"
+        elif direction == "Short":
+            direction_desc = "做空"
+        logger.info(f"开始生成ORB策略信号：{direction_desc}，根据SAR指标：{by_sar}")
         if self.data is None:
             raise ValueError("请先调用fetch_intraday_data获取数据")
 
@@ -164,16 +171,16 @@ class ORBStrategy:
             entry_time = second_candle.date_time  # entry时间
 
             # 生成信号：第一根K线方向决定多空（排除十字星：open1 == close1）
-            if open1 < close1:
+            if open1 < close1 and (direction == "Long" or direction is None):
                 # 第一根K线收涨→多头信号
                 signal = "Long"
                 stop_price = low1  # 多头止损=第一根K线最低价
-            elif open1 > close1:
+            elif open1 > close1 and (direction == "Short" or direction is None):
                 # 第一根K线收跌→空头信号
                 signal = "Short"
                 stop_price = high1  # 空头止损=第一根K线最高价
             else:
-                # 十字星→无信号
+                # 与direction不一致或十字星→无信号
                 signal = None
                 stop_price = None
 

huge_volume_main.py

@@ -512,7 +512,7 @@ def test_send_huge_volume_data_to_wechat():
 if __name__ == "__main__":
     # test_send_huge_volume_data_to_wechat()
     # batch_initial_detect_volume_spike(threshold=2.0)
-    batch_update_volume_spike(threshold=2.0, is_us_stock=True)
+    batch_update_volume_spike(threshold=2.0, is_us_stock=False)
     # huge_volume_main = HugeVolumeMain(threshold=2.0)
     # huge_volume_main.batch_next_periods_rise_or_fall(output_excel=True)
     # data_file_path = "./output/huge_volume_statistics/next_periods_rise_or_fall_stat_20250731200304.xlsx"

market_data_main.py

@@ -252,6 +252,8 @@ class MarketDataMain:
         data["kdj_j"] = None
         data["kdj_signal"] = None
         data["kdj_pattern"] = None
+        data["sar"] = None
+        data["sar_signal"] = None
         data["ma5"] = None
         data["ma10"] = None
         data["ma20"] = None
@@ -301,6 +303,8 @@ class MarketDataMain:
         kdj_j DOUBLE DEFAULT NULL COMMENT 'KDJ指标J值',
         kdj_signal VARCHAR(15) DEFAULT NULL COMMENT 'KDJ金叉死叉信号',
         kdj_pattern varchar(25) DEFAULT NULL COMMENT 'KDJ超买，超卖，徘徊',
+        sar DOUBLE DEFAULT NULL COMMENT 'SAR指标',
+        sar_signal VARCHAR(15) DEFAULT NULL COMMENT 'SAR多头，SAR空头，SAR观望',
         ma5 DOUBLE DEFAULT NULL COMMENT '5移动平均线',
         ma10 DOUBLE DEFAULT NULL COMMENT '10移动平均线',
         ma20 DOUBLE DEFAULT NULL COMMENT '20移动平均线',
@@ -331,6 +335,7 @@ class MarketDataMain:
         data = metrics_calculation.pre_close(data)
         data = metrics_calculation.macd(data)
         data = metrics_calculation.kdj(data)
+        data = metrics_calculation.sar(data)
         data = metrics_calculation.set_kdj_pattern(data)
         data = metrics_calculation.update_macd_divergence_column_simple(data)
         data = metrics_calculation.ma5102030(data)

sql/table/crypto_market_data.sql

@@ -33,6 +33,8 @@ CREATE TABLE IF NOT EXISTS crypto_market_data (
     kdj_j DOUBLE DEFAULT NULL COMMENT 'KDJ指标J值',
     kdj_signal VARCHAR(15) DEFAULT NULL COMMENT 'KDJ金叉死叉信号',
     kdj_pattern varchar(25) DEFAULT NULL COMMENT 'KDJ超买，超卖，徘徊',
+    sar DOUBLE DEFAULT NULL COMMENT 'SAR指标值',
+    sar_signal VARCHAR(15) DEFAULT NULL COMMENT 'SAR多头空头信号',
     ma5 DOUBLE DEFAULT NULL COMMENT '5移动平均线',
     ma10 DOUBLE DEFAULT NULL COMMENT '10移动平均线',
     ma20 DOUBLE DEFAULT NULL COMMENT '20移动平均线',
@@ -64,3 +66,7 @@ ALTER TABLE crypto_market_data MODIFY COLUMN ma_cross VARCHAR(150) DEFAULT NULL
 
 --添加date_time_us字段
 ALTER TABLE crypto_market_data ADD COLUMN date_time_us VARCHAR(50) NULL COMMENT '美国时间格式的日期时间' AFTER date_time;
+
+--添加SAR相关字段
+ALTER TABLE crypto_market_data ADD COLUMN sar DOUBLE DEFAULT NULL COMMENT 'SAR指标值' AFTER kdj_pattern;
+ALTER TABLE crypto_market_data ADD COLUMN sar_signal VARCHAR(15) DEFAULT NULL COMMENT 'SAR多头空头信号' AFTER sar;

update_data_main.py

@@ -0,0 +1,69 @@
+import pandas as pd
+
+from core.db.db_market_data import DBMarketData
+from core.biz.metrics_calculation import MetricsCalculation
+from config import MYSQL_CONFIG, US_STOCK_MONITOR_CONFIG, OKX_MONITOR_CONFIG
+import core.logger as logging
+
+logger = logging.logger
+
+class UpdateDataMain:
+    def __init__(self):
+        mysql_user = MYSQL_CONFIG.get("user", "xch")
+        mysql_password = MYSQL_CONFIG.get("password", "")
+        if not mysql_password:
+            raise ValueError("MySQL password is not set")
+        mysql_host = MYSQL_CONFIG.get("host", "localhost")
+        mysql_port = MYSQL_CONFIG.get("port", 3306)
+        mysql_database = MYSQL_CONFIG.get("database", "okx")
+
+        self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
+        self.db_market_data = DBMarketData(self.db_url)
+        self.metrics_calculation = MetricsCalculation()
+    
+    def batch_update_data(self, is_us_stock: bool = False):
+        """
+        批量更新数据
+        """
+        if is_us_stock:
+            symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", [])
+            bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get("bars", ["5m", "15m", "1H", "1D"])
+        else:
+            symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", [])
+            bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("bars", ["5m", "15m", "1H", "1D"])
+        for symbol in symbols:
+            for bar in bars:
+                self.update_data(symbol, bar)
+    
+    def update_data(self, symbol: str, bar: str):
+        """
+        更新数据
+        """
+        logger.info(f"开始更新数据: {symbol} {bar}")
+        data = self.db_market_data.query_market_data_by_symbol_bar(symbol, bar)
+        logger.info(f"查询数据完成: {symbol} {bar},共有{len(data)}条数据")
+        data = pd.DataFrame(data)
+        data.sort_values(by="timestamp", inplace=True)
+        data = self.update_date_time_us(data)
+        logger.info("更新SAR指标")
+        data = self.metrics_calculation.sar(data)
+        logger.info("更新SAR指标完成")
+        logger.info(f"开始保存数据: {symbol} {bar}")
+        self.db_market_data.insert_data_to_mysql(data)
+        logger.info(f"保存数据完成: {symbol} {bar}")
+
+    def update_date_time_us(self, data: pd.DataFrame):
+        """
+        更新日期时间
+        """
+        logger.info(f"开始更新美东日期时间: {data.shape[0]}条数据")
+        dt_us_series = pd.to_datetime(data['timestamp'].astype(int), unit='ms', utc=True, errors='coerce').dt.tz_convert('America/New_York')
+        data['date_time_us'] = dt_us_series.dt.strftime('%Y-%m-%d %H:%M:%S')
+        logger.info(f"更新美东日期时间完成: {data.shape[0]}条数据")
+        return data
+
+
+if __name__ == "__main__":
+    update_data_main = UpdateDataMain()
+    update_data_main.batch_update_data(is_us_stock=True)
+    update_data_main.batch_update_data(is_us_stock=False)