From 6ee64abaf514d29bebce5a086949d673b9641c99 Mon Sep 17 00:00:00 2001 From: blade <8019068@qq.com> Date: Tue, 2 Sep 2025 12:44:34 +0800 Subject: [PATCH] support SAR --- config.py | 2 +- core/biz/metrics_calculation.py | 295 ++++++++++++------ .../db_market_data.cpython-312.pyc | Bin 16925 -> 16944 bytes core/db/db_market_data.py | 4 +- core/trade/orb_trade.py | 17 +- huge_volume_main.py | 2 +- market_data_main.py | 5 + sql/table/crypto_market_data.sql | 6 + update_data_main.py | 69 ++++ 9 files changed, 296 insertions(+), 104 deletions(-) create mode 100644 update_data_main.py diff --git a/config.py b/config.py index fbf098f..667c6cf 100644 --- a/config.py +++ b/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" } } diff --git a/core/biz/metrics_calculation.py b/core/biz/metrics_calculation.py index 225c10d..659c26b 100644 --- a/core/biz/metrics_calculation.py +++ b/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["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["upper_shadow_ratio"] = df["high_close_diff"] / df["high_low_diff"].replace( + 0, np.nan + ) + 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_zscore_p75"] = df["price_range_zscore"].rolling(window=window_size, min_periods=1).quantile(0.75) + df["price_range_ratio_p75"] = ( + 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["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["open_close_diff"] / df["close"] < 0.0005) # 开收盘价差小于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["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.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["upper_shadow_ratio"] >= 0.25) & (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["upper_shadow_ratio"] >= 0.25) + & (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"] <= 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["open_close_fill"] > 0.75) + & (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.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) - short_condition = (trend_strength < -strength_threshold) & (trend_persistence >= persistence_threshold) - + + long_condition = (trend_strength > strength_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["ma10_close_diff"] > data["ma20_close_diff"]) & - (data["ma20_close_diff"] > data["ma30_close_diff"]) + (data["ma5_close_diff"] > data["ma10_close_diff"]) + & (data["ma10_close_diff"] > data["ma20_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["ma10_close_diff"] < data["ma20_close_diff"]) & - (data["ma20_close_diff"] < data["ma30_close_diff"]) + (data["ma5_close_diff"] < data["ma10_close_diff"]) + & (data["ma10_close_diff"] < data["ma20_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, - "ma20_close_diff": 0.2, "ma30_close_diff": 0.1} + weights = { + "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["ma10_close_diff"] >= data["ma20_close_diff"]) * 0.25 + - (data["ma20_close_diff"] >= data["ma30_close_diff"]) * 0.25 + - (data["ma_close_avg"] > 0) * 0.25 + (data["ma5_close_diff"] >= data["ma10_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["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 + - alignment_score * 0.3 + - strength_score * 0.3 + weighted_score * 0.4 + 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: - 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 + if ( + 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 + 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 diff --git a/core/db/__pycache__/db_market_data.cpython-312.pyc b/core/db/__pycache__/db_market_data.cpython-312.pyc index 274f19e14d1d611d4afa059d230f8060c7ac71de..07c0daa68cba44fcdc16cc7b47f04df8590ac2f6 100644 GIT binary patch delta 2107 zcmb`IUrbw79LIZZ|4?oz6jp?`D20Ji1q9>jEFBw#RX3cOpkY&3xwLns(Ds&d?=Y)l 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--- a/core/db/db_market_data.py +++ b/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 diff --git a/core/trade/orb_trade.py b/core/trade/orb_trade.py index 4d247dc..1ecf580 100644 --- a/core/trade/orb_trade.py +++ b/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 diff --git a/huge_volume_main.py b/huge_volume_main.py index e77b2d2..789c58b 100644 --- a/huge_volume_main.py +++ b/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" diff --git a/market_data_main.py b/market_data_main.py index f8c2e3a..f485827 100644 --- a/market_data_main.py +++ b/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) diff --git a/sql/table/crypto_market_data.sql b/sql/table/crypto_market_data.sql index 44876f4..62faa95 100644 --- a/sql/table/crypto_market_data.sql +++ b/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; diff --git a/update_data_main.py b/update_data_main.py new file mode 100644 index 0000000..a6a5b37 --- /dev/null +++ b/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) \ No newline at end of file