support import binance data

optimize orb strategy

core/db/db_binance_data.py

@@ -7,68 +7,65 @@ logger = logging.logger
 
 
 class DBBinanceData:
-    def __init__(
+    def __init__(self, db_url: str):
-        self, 
-        db_url: str
-    ):
         self.db_url = db_url
         self.table_name = "crypto_binance_data"
         self.columns = [
-        "symbol",
+            "symbol",
-        "bar",
+            "bar",
-        "timestamp",
+            "timestamp",
-        "date_time",
+            "date_time",
-        "date_time_us",
+            "date_time_us",
-        "open",
+            "open",
-        "high",
+            "high",
-        "low",
+            "low",
-        "close",
+            "close",
-        "pre_close",
+            "pre_close",
-        "close_change",
+            "close_change",
-        "pct_chg",
+            "pct_chg",
-        "volume",
+            "volume",
-        "volCcy",
+            "volCcy",
-        "volCCyQuote",
+            "volCCyQuote",
-        "buy_sz",
+            "buy_sz",
-        "sell_sz",
+            "sell_sz",
-        # 技术指标字段
+            # 技术指标字段
-        "ma1",
+            "ma1",
-        "ma2",
+            "ma2",
-        "dif",
+            "dif",
-        "dea",
+            "dea",
-        "macd",
+            "macd",
-        "macd_signal",
+            "macd_signal",
-        "macd_divergence",
+            "macd_divergence",
-        "kdj_k",
+            "kdj_k",
-        "kdj_d",
+            "kdj_d",
-        "kdj_j",
+            "kdj_j",
-        "kdj_signal",
+            "kdj_signal",
-        "kdj_pattern",
+            "kdj_pattern",
-        "sar",
+            "sar",
-        "sar_signal",
+            "sar_signal",
-        "ma5",
+            "ma5",
-        "ma10",
+            "ma10",
-        "ma20",
+            "ma20",
-        "ma30",
+            "ma30",
-        "ma_cross",
+            "ma_cross",
-        "ma5_close_diff",
+            "ma5_close_diff",
-        "ma10_close_diff",
+            "ma10_close_diff",
-        "ma20_close_diff",
+            "ma20_close_diff",
-        "ma30_close_diff",
+            "ma30_close_diff",
-        "ma_close_avg",
+            "ma_close_avg",
-        "ma_long_short",
+            "ma_long_short",
-        "ma_divergence",
+            "ma_divergence",
-        "rsi_14",
+            "rsi_14",
-        "rsi_signal",
+            "rsi_signal",
-        "boll_upper",
+            "boll_upper",
-        "boll_middle",
+            "boll_middle",
-        "boll_lower",
+            "boll_lower",
-        "boll_signal",
+            "boll_signal",
-        "boll_pattern",
+            "boll_pattern",
-        "k_length",
+            "k_length",
-        "k_shape",
+            "k_shape",
-        "k_up_down",
+            "k_up_down",
-        "create_time",
+            "create_time",
         ]
         self.db_manager = DBData(db_url, self.table_name, self.columns)
 
@@ -143,7 +140,9 @@ class DBBinanceData:
         condition_dict = {"symbol": symbol, "bar": bar}
         return self.db_manager.query_data(sql, condition_dict, return_multi=False)
 
-    def query_data_before_timestamp(self, symbol: str, bar: str, timestamp: int, limit: int = 100):
+    def query_data_before_timestamp(
+        self, symbol: str, bar: str, timestamp: int, limit: int = 100
+    ):
         """
         根据时间戳查询之前的数据
         :param symbol: 交易对
@@ -157,7 +156,12 @@ class DBBinanceData:
         ORDER BY timestamp DESC
         LIMIT :limit
         """
-        condition_dict = {"symbol": symbol, "bar": bar, "timestamp": timestamp, "limit": limit}
+        condition_dict = {
+            "symbol": symbol,
+            "bar": bar,
+            "timestamp": timestamp,
+            "limit": limit,
+        }
         return self.db_manager.query_data(sql, condition_dict, return_multi=True)
 
     def query_data_by_technical_indicators(
@@ -170,7 +174,7 @@ class DBBinanceData:
         kdj_signal: str = None,
         rsi_signal: str = None,
         boll_signal: str = None,
-        ma_cross: str = None
+        ma_cross: str = None,
     ):
         """
         根据技术指标查询数据
@@ -230,7 +234,7 @@ class DBBinanceData:
         bar: str,
         signal: str = None,
         start: str = None,
-        end: str = None
+        end: str = None,
     ):
         """
         查询MACD信号数据
@@ -275,7 +279,7 @@ class DBBinanceData:
         signal: str = None,
         pattern: str = None,
         start: str = None,
-        end: str = None
+        end: str = None,
     ):
         """
         查询KDJ信号数据
@@ -325,7 +329,7 @@ class DBBinanceData:
         long_short: str = None,
         divergence: str = None,
         start: str = None,
-        end: str = None
+        end: str = None,
     ):
         """
         查询均线信号数据
@@ -378,7 +382,7 @@ class DBBinanceData:
         signal: str = None,
         pattern: str = None,
         start: str = None,
-        end: str = None
+        end: str = None,
     ):
         """
         查询布林带信号数据
@@ -421,11 +425,7 @@ class DBBinanceData:
         return self.db_manager.query_data(sql, condition_dict, return_multi=True)
 
     def get_technical_statistics(
-        self, 
+        self, symbol: str, bar: str, start: str = None, end: str = None
-        symbol: str, 
-        bar: str, 
-        start: str = None, 
-        end: str = None
     ):
         """
         获取技术指标统计信息
@@ -473,17 +473,28 @@ class DBBinanceData:
 
         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 = None, end: str = None):
+    def query_market_data_by_symbol_bar(
+        self,
+        symbol: str,
+        bar: str,
+        fields: list = None,
+        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
         :param bar: K线周期
+        :param fields: 字段列表
         :param start: 开始时间
         :param end: 结束时间
         """
+        if fields is None:
+            fields = ["*"]
+        fields_str = ", ".join(fields)
         if start is None and end is None:
-            sql = """
+            sql = f"""
-                SELECT * FROM crypto_binance_data
+                SELECT {fields_str} FROM crypto_binance_data
                 WHERE symbol = :symbol AND bar = :bar
                 ORDER BY timestamp ASC
                 """
@@ -502,22 +513,27 @@ class DBBinanceData:
             if start is not None and end is not None:
                 if start > end:
                     start, end = end, start
-                sql = """
+                sql = f"""
-                    SELECT * FROM crypto_binance_data
+                    SELECT {fields_str} FROM crypto_binance_data
                     WHERE symbol = :symbol AND bar = :bar AND timestamp BETWEEN :start AND :end
                     ORDER BY timestamp ASC
                     """
-                condition_dict = {"symbol": symbol, "bar": bar, "start": start, "end": end}
+                condition_dict = {
+                    "symbol": symbol,
+                    "bar": bar,
+                    "start": start,
+                    "end": end,
+                }
             elif start is not None:
-                sql = """
+                sql = f"""
-                    SELECT * FROM crypto_binance_data
+                    SELECT {fields_str} FROM crypto_binance_data
                     WHERE symbol = :symbol AND bar = :bar AND timestamp >= :start
                     ORDER BY timestamp ASC
                     """
                 condition_dict = {"symbol": symbol, "bar": bar, "start": start}
             elif end is not None:
-                sql = """
+                sql = f"""
-                    SELECT * FROM crypto_binance_data
+                    SELECT {fields_str} FROM crypto_binance_data
                     WHERE symbol = :symbol AND bar = :bar AND timestamp <= :end
                     ORDER BY timestamp ASC
                     """

core/db/db_manager.py

@@ -218,13 +218,6 @@ class DBData:
         :param db_url: 数据库连接URL
         """
         try:
-            engine = create_engine(
-                self.db_url,
-                pool_size=5,  # 连接池大小
-                max_overflow=10,  # 允许的最大溢出连接
-                pool_timeout=30,  # 连接超时时间（秒）
-                pool_recycle=1800,  # 连接回收时间（秒），避免长时间闲置
-            )
             with self.db_engine.connect() as conn:
                 result = conn.execute(text(sql), condition_dict)
                 if return_multi:

core/db/db_market_data.py

@@ -473,17 +473,21 @@ 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 = None, end: str = None):
+    def query_market_data_by_symbol_bar(self, symbol: str, bar: str, fields: list = None, 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
         :param bar: K线周期
+        :param fields: 字段列表
         :param start: 开始时间
         :param end: 结束时间
         """
+        if fields is None:
+            fields = ["*"]
+        fields_str = ", ".join(fields)
         if start is None and end is None:
-            sql = """
+            sql = f"""
-                SELECT * FROM crypto_market_data
+                SELECT {fields_str} FROM crypto_market_data
                 WHERE symbol = :symbol AND bar = :bar
                 ORDER BY timestamp ASC
                 """
@@ -502,22 +506,22 @@ class DBMarketData:
             if start is not None and end is not None:
                 if start > end:
                     start, end = end, start
-                sql = """
+                sql = f"""
-                    SELECT * FROM crypto_market_data
+                    SELECT {fields_str} FROM crypto_market_data
                     WHERE symbol = :symbol AND bar = :bar AND timestamp BETWEEN :start AND :end
                     ORDER BY timestamp ASC
                     """
                 condition_dict = {"symbol": symbol, "bar": bar, "start": start, "end": end}
             elif start is not None:
-                sql = """
+                sql = f"""
-                    SELECT * FROM crypto_market_data
+                    SELECT {fields_str} FROM crypto_market_data
                     WHERE symbol = :symbol AND bar = :bar AND timestamp >= :start
                     ORDER BY timestamp ASC
                     """
                 condition_dict = {"symbol": symbol, "bar": bar, "start": start}
             elif end is not None:
-                sql = """
+                sql = f"""
-                    SELECT * FROM crypto_market_data
+                    SELECT {fields_str} FROM crypto_market_data
                     WHERE symbol = :symbol AND bar = :bar AND timestamp <= :end
                     ORDER BY timestamp ASC
                     """

core/trade/orb_trade.py

@@ -9,10 +9,12 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
+from datetime import datetime, timedelta
 
 import core.logger as logging
 from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 from core.db.db_market_data import DBMarketData
+from core.db.db_binance_data import DBBinanceData
 from core.db.db_huge_volume_data import DBHugeVolumeData
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
 
@@ -35,11 +37,14 @@ class ORBStrategy:
         commission_per_share=0.0005,
         profit_target_multiple=10,
         is_us_stock=False,
+        is_binance=False,
         direction=None,
         by_sar=False,
         symbol_bar_data=None,
+        symbol_1h_data=None,
         price_range_mean_as_R=False,
         by_big_k=False,
+        by_1h_k=False,
     ):
         """
         初始化ORB策略参数
@@ -63,10 +68,14 @@ class ORBStrategy:
         :param commission_per_share: 每股交易佣金（美元，默认0.0005）
         :param profit_target_multiple: 盈利目标倍数（默认10倍$R，即10R）
         :param is_us_stock: 是否是美股
+        :param is_binance: 是否是Binance
         :param direction: 方向，None=自动，Long=多头，Short=空头
         :param by_sar: 是否根据SAR指标生成信号，True=是，False=否
+        :param symbol_bar_data: 5分钟K线数据
+        :param symbol_1h_data: 1小时K线数据
         :param price_range_mean_as_R: 是否将价格振幅均值作为$R，True=是，False=否
         :param by_big_k: 是否根据K线实体部分，亦即abs(open-close)超过high-low的50%，True=是，False=否
+        :param by_1h_k: 是否根据1小时K线，True=是，False=否
         """
         logger.info(
             f"初始化ORB策略参数：股票代码={symbol}，K线周期={bar}，开始日期={start_date}，结束日期={end_date}，初始账户资金={initial_capital}，最大杠杆倍数={max_leverage}，单次交易风险比例={risk_per_trade}，每股交易佣金={commission_per_share}"
@@ -90,10 +99,15 @@ class ORBStrategy:
         mysql_host = MYSQL_CONFIG.get("host", "localhost")
         mysql_port = MYSQL_CONFIG.get("port", 3306)
         mysql_database = MYSQL_CONFIG.get("database", "okx")
+        self.is_us_stock = is_us_stock
+        self.is_binance = is_binance
 
         self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
-        self.db_market_data = DBMarketData(self.db_url)
+        if self.is_binance:
-        self.is_us_stock = is_us_stock
+            self.db_market_data = DBBinanceData(self.db_url)
+        else:
+            self.db_market_data = DBMarketData(self.db_url)
+
         self.output_chart_folder = r"./output/trade_sandbox/orb_strategy/chart/"
         self.output_excel_folder = r"./output/trade_sandbox/orb_strategy/excel/"
         os.makedirs(self.output_chart_folder, exist_ok=True)
@@ -110,6 +124,7 @@ class ORBStrategy:
         if self.by_sar:
             self.sar_desc = "考虑SAR"
         self.symbol_bar_data = symbol_bar_data
+        self.symbol_1h_data = symbol_1h_data
         self.price_range_mean_as_R = price_range_mean_as_R
         if self.price_range_mean_as_R:
             self.price_range_mean_as_R_desc = "R为振幅均值"
@@ -121,6 +136,11 @@ class ORBStrategy:
             self.by_big_k_desc = "K线实体过50%"
         else:
             self.by_big_k_desc = "无K线要求"
+        self.by_1h_k = by_1h_k
+        if self.by_1h_k:
+            self.by_1h_k_desc = "参照1小时K线"
+        else:
+            self.by_1h_k_desc = "不参照1小时K线"
 
     def run(self):
         """
@@ -132,7 +152,12 @@ class ORBStrategy:
         if len(self.trades) > 0:
             self.plot_equity_curve()
             self.output_trade_summary()
-        return self.symbol_bar_data, self.trades_df, self.trades_summary_df
+        return (
+            self.symbol_bar_data,
+            self.symbol_1h_data,
+            self.trades_df,
+            self.trades_summary_df,
+        )
 
     def fetch_intraday_data(self):
         """
@@ -146,47 +171,10 @@ class ORBStrategy:
             f"开始获取{self.symbol}数据：{self.start_date}至{self.end_date}，间隔{self.bar}"
         )
         if self.symbol_bar_data is None or len(self.symbol_bar_data) == 0:
-            data = self.db_market_data.query_market_data_by_symbol_bar(
+            self.data = self.get_full_data(bar=self.bar)
-                self.symbol, self.bar, start=self.start_date, end=self.end_date
-            )
-            data = pd.DataFrame(data)
-            data.sort_values(by="date_time", inplace=True)
-            # 保留核心列：开盘价、最高价、最低价、收盘价、成交量
-            data["Open"] = data["open"]
-            data["High"] = data["high"]
-            data["Low"] = data["low"]
-            data["Close"] = data["close"]
-            data["Volume"] = data["volume"]
-            if self.is_us_stock:
-                date_time_field = "date_time_us"
-            else:
-                date_time_field = "date_time"
-            data[date_time_field] = pd.to_datetime(data[date_time_field])
-            # data["Date"]为日期，不包括时分秒，即date_time如果是2025-01-01 10:00:00，则Date为2025-01-01
-            data["Date"] = data[date_time_field].dt.date
-            # 将Date转换为datetime64[ns]类型以确保类型一致
-            data["Date"] = pd.to_datetime(data["Date"])
-            # 最小data["Date"]
-            self.start_date = data["Date"].min().strftime("%Y-%m-%d")
-            # 最大data["Date"]
-            self.end_date = data["Date"].max().strftime("%Y-%m-%d")
-            self.data = data[
-                [
-                    "symbol",
-                    "bar",
-                    "Date",
-                    date_time_field,
-                    "Open",
-                    "High",
-                    "Low",
-                    "Close",
-                    "Volume",
-                    "sar_signal",
-                ]
-            ].copy()
-            self.data.rename(columns={date_time_field: "date_time"}, inplace=True)
             self.calculate_price_range_mean()
             self.symbol_bar_data = self.data.copy()
+            self.symbol_1h_data = self.get_full_data(bar="1H")
         else:
             self.data = self.symbol_bar_data.copy()
 
@@ -210,6 +198,91 @@ class ORBStrategy:
             f"成功获取{self.symbol}数据：{len(self.data)}根{self.bar}K线,开始日期={self.start_date}，结束日期={self.end_date}"
         )
 
+    def get_full_data(self, bar: str = "5m"):
+        """
+        分段获取数据，并将数据合并为完整数据
+        分段依据：如果end_date与start_date相差超过一年，则每次取一年数据
+        """
+        data = pd.DataFrame()
+        start_date = datetime.strptime(self.start_date, "%Y-%m-%d")
+        end_date = datetime.strptime(self.end_date, "%Y-%m-%d") + timedelta(days=1)
+        fields = [
+            "symbol",
+            "bar",
+            "date_time",
+            "date_time_us",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+            "sar_signal",
+            "ma5",
+            "ma10",
+            "ma20",
+            "ma30",
+            "dif",
+            "macd",
+        ]
+        while start_date < end_date:
+            current_end_date = min(start_date + timedelta(days=180), end_date)
+            start_date_str = start_date.strftime("%Y-%m-%d")
+            current_end_date_str = current_end_date.strftime("%Y-%m-%d")
+            logger.info(
+                f"获取{self.symbol}数据：{start_date_str}至{current_end_date_str}"
+            )
+            current_data = self.db_market_data.query_market_data_by_symbol_bar(
+                self.symbol, bar, fields, start=start_date_str, end=current_end_date_str
+            )
+            if current_data is not None and len(current_data) > 0:
+                current_data = pd.DataFrame(current_data)
+                data = pd.concat([data, current_data])
+            start_date = current_end_date
+        data.drop_duplicates(inplace=True)
+        if self.is_us_stock:
+            date_time_field = "date_time_us"
+        else:
+            date_time_field = "date_time"
+        data.sort_values(by=date_time_field, inplace=True)
+        data.reset_index(drop=True, inplace=True)
+        # 保留核心列：开盘价、最高价、最低价、收盘价、成交量
+        data["Open"] = data["open"]
+        data["High"] = data["high"]
+        data["Low"] = data["low"]
+        data["Close"] = data["close"]
+        data["Volume"] = data["volume"]
+        data[date_time_field] = pd.to_datetime(data[date_time_field])
+        # data["Date"]为日期，不包括时分秒，即date_time如果是2025-01-01 10:00:00，则Date为2025-01-01
+        data["Date"] = data[date_time_field].dt.date
+        # 将Date转换为datetime64[ns]类型以确保类型一致
+        data["Date"] = pd.to_datetime(data["Date"])
+        # 最小data["Date"]
+        self.start_date = data["Date"].min().strftime("%Y-%m-%d")
+        # 最大data["Date"]
+        self.end_date = data["Date"].max().strftime("%Y-%m-%d")
+        data = data[
+            [
+                "symbol",
+                "bar",
+                "Date",
+                date_time_field,
+                "Open",
+                "High",
+                "Low",
+                "Close",
+                "Volume",
+                "sar_signal",
+                "ma5",
+                "ma10",
+                "ma20",
+                "ma30",
+                "dif",
+                "macd",
+            ]
+        ]
+        data.rename(columns={date_time_field: "date_time"}, inplace=True)
+        return data
+
     def calculate_shares(self, account_value, entry_price, stop_price, risk_assumed):
         """
         根据ORB公式计算交易股数
@@ -247,7 +320,7 @@ class ORBStrategy:
         - 第二根5分钟K线：根据第一根K线方向生成多空信号
         """
         logger.info(
-            f"开始生成ORB策略信号：{self.direction_desc}，根据SAR指标：{self.by_sar}"
+            f"开始生成ORB策略信号：{self.direction_desc}，根据SAR指标：{self.by_sar}，{self.by_1h_k_desc}"
         )
         if self.data is None:
             raise ValueError("请先调用fetch_intraday_data获取数据")
@@ -261,6 +334,7 @@ class ORBStrategy:
 
             # 第一根5分钟K线（开盘区间）
             first_candle = daily_data.iloc[0]
+            current_date = first_candle["Date"]
             high1 = first_candle["High"]
             low1 = first_candle["Low"]
             open1 = first_candle["Open"]
@@ -273,6 +347,25 @@ class ORBStrategy:
                 if (abs(open1 - close1) / (high1 - low1)) < 0.5:
                     continue
 
+            ma5_1h = None
+            ma10_1h = None
+            dif_1h = None
+            macd_1h = None
+            if self.by_1h_k:
+                if self.symbol_1h_data is None or len(self.symbol_1h_data) == 0:
+                    continue
+                if len(self.symbol_1h_data) < 2:
+                    continue
+                symbol_1h_date_data = self.symbol_1h_data[
+                    self.symbol_1h_data["Date"] == current_date
+                ]
+                if len(symbol_1h_date_data) > 0:
+                    first_candle_1h = symbol_1h_date_data.iloc[0]
+                    ma5_1h = first_candle_1h["ma5"]
+                    ma10_1h = first_candle_1h["ma10"]
+                    dif_1h = first_candle_1h["dif"]
+                    macd_1h = first_candle_1h["macd"]
+
             # 第二根5分钟K线（entry信号）
             second_candle = daily_data.iloc[1]
             entry_price = second_candle["Open"]  # entry价格=第二根K线开盘价
@@ -283,6 +376,22 @@ class ORBStrategy:
                 open1 < close1
                 and (self.direction == "Long" or self.direction is None)
                 and ((self.by_sar and sar_signal == "SAR多头") or not self.by_sar)
+                and (
+                    (
+                        self.by_1h_k
+                        and (
+                            ma5_1h is not None
+                            and ma10_1h is not None
+                            and ma5_1h > ma10_1h
+                        )
+                        and (
+                            dif_1h is not None
+                            and macd_1h is not None
+                            and (dif_1h > 0 or macd_1h > 0)
+                        )
+                    )
+                    or not self.by_1h_k
+                )
             ):
                 # 第一根K线收涨→多头信号
                 signal = "Long"
@@ -291,6 +400,22 @@ class ORBStrategy:
                 open1 > close1
                 and (self.direction == "Short" or self.direction is None)
                 and ((self.by_sar and sar_signal == "SAR空头") or not self.by_sar)
+                and (
+                    (
+                        self.by_1h_k
+                        and (
+                            ma5_1h is not None
+                            and ma10_1h is not None
+                            and ma5_1h < ma10_1h
+                        )
+                        and (
+                            dif_1h is not None
+                            and macd_1h is not None
+                            and (dif_1h < 0 or macd_1h < 0)
+                        )
+                    )
+                    or not self.by_1h_k
+                )
             ):
                 # 第一根K线收跌→空头信号
                 signal = "Short"
@@ -477,6 +602,7 @@ class ORBStrategy:
                     "BySar": self.sar_desc,
                     "PriceRangeMeanAsR": self.price_range_mean_as_R_desc,
                     "ByBigK": self.by_big_k_desc,
+                    "By1hK": self.by_1h_k_desc,
                     "Symbol": self.symbol,
                     "Bar": self.bar,
                     "Date": date,
@@ -576,6 +702,7 @@ class ORBStrategy:
         self.trades_summary["根据SAR"] = self.sar_desc
         self.trades_summary["R算法"] = self.price_range_mean_as_R_desc
         self.trades_summary["K线条件"] = self.by_big_k_desc
+        self.trades_summary["1小时K线条件"] = self.by_1h_k_desc
         self.trades_summary["股票代码"] = self.symbol
         self.trades_summary["K线周期"] = self.bar
         self.trades_summary["开始日期"] = self.start_date
@@ -660,7 +787,7 @@ class ORBStrategy:
             markersize=4,
         )
         plt.title(
-            f"{symbol} {bar} {self.direction_desc} {self.sar_desc} {self.price_range_mean_as_R_desc} {self.by_big_k_desc}",
+            f"{symbol} {bar} {self.direction_desc} {self.sar_desc} {self.price_range_mean_as_R_desc} {self.by_big_k_desc} {self.by_1h_k_desc}",
             fontsize=14,
             fontweight="bold",
         )
@@ -704,7 +831,7 @@ class ORBStrategy:
                 fontsize=10,
             )
         plt.tight_layout()
-        self.chart_save_path = f"{self.output_chart_folder}/{symbol}_{bar}_{self.direction_desc}_{self.sar_desc}_{self.price_range_mean_as_R_desc}_{self.by_big_k_desc}_orb.png"
+        self.chart_save_path = f"{self.output_chart_folder}/{symbol}_{bar}_{self.direction_desc}_{self.sar_desc}_{self.price_range_mean_as_R_desc}_{self.by_big_k_desc}_{self.by_1h_k_desc}_orb.png"
         plt.savefig(self.chart_save_path, dpi=150, bbox_inches="tight")
         plt.close()
 
@@ -714,7 +841,7 @@ class ORBStrategy:
         """
         start_date = self.start_date.replace("-", "")
         end_date = self.end_date.replace("-", "")
-        output_file_name = f"orb_{self.symbol}_{self.bar}_{start_date}_{end_date}_{self.direction_desc}_{self.sar_desc}_{self.price_range_mean_as_R_desc}_{self.by_big_k_desc}.xlsx"
+        output_file_name = f"orb_{self.symbol}_{self.bar}_{start_date}_{end_date}_{self.direction_desc}_{self.sar_desc}_{self.price_range_mean_as_R_desc}_{self.by_big_k_desc}_{self.by_1h_k_desc}.xlsx"
         output_file_path = os.path.join(self.output_excel_folder, output_file_name)
         logger.info(f"导出{output_file_path}")
         with pd.ExcelWriter(output_file_path) as writer:

huge_volume_main.py

@@ -678,9 +678,9 @@ def batch_import_binance_data_by_csv():
 
 def test_import_binance_data_by_csv():
     huge_volume_main = HugeVolumeMain(threshold=2.0, is_us_stock=False, is_binance=True)
-    file_path = "./data/binance/spot/2020-08-11/SOL-USDT_1h.csv"
+    file_path = "./data/binance/spot/2020-08-12/XRP-USDT_1h.csv"
     huge_volume_main.import_binance_data_by_csv(
-        file_path, "SOL-USDT", "1H", [50, 80, 100, 120]
+        file_path, "XRP-USDT", "1H", [50, 80, 100, 120]
     )
 
 
@@ -696,8 +696,8 @@ def test_send_huge_volume_data_to_wechat():
 
 
 if __name__ == "__main__":
-    test_import_binance_data_by_csv()
+    # test_import_binance_data_by_csv()
-    # batch_import_binance_data_by_csv()
+    batch_import_binance_data_by_csv()
     # batch_update_volume_spike(threshold=2.0, is_us_stock=True)
     # test_send_huge_volume_data_to_wechat()
     # batch_initial_detect_volume_spike(threshold=2.0)

orb_trade_main.py

@@ -1,5 +1,5 @@
 from core.trade.orb_trade import ORBStrategy
-from config import US_STOCK_MONITOR_CONFIG, OKX_MONITOR_CONFIG
+from config import US_STOCK_MONITOR_CONFIG, OKX_MONITOR_CONFIG, BINANCE_MONITOR_CONFIG
 import core.logger as logging
 from datetime import datetime
 from openpyxl import Workbook
@@ -12,13 +12,21 @@ logger = logging.logger
 
 
 def main():
-    is_us_stock_list = [True, False]
+    # is_us_stock_list = [True, False]
+    is_us_stock_list = [False]
+    is_binance = True
     bar = "5m"
     direction_list = [None, "Long", "Short"]
     by_sar_list = [False, True]
     price_range_mean_as_R_list = [False, True]
     by_big_k_list = [False, True]
-    start_date = "2024-01-01"
+    by_1h_k_list = [False, True]
+    if is_binance:
+        start_date = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get("initial_date", "2017-08-16 00:00:00")
+        if len(start_date) > 10:
+            start_date = start_date[:10]
+    else:
+        start_date = "2024-01-01"
     end_date = datetime.now().strftime("%Y-%m-%d")
     profit_target_multiple = 10
     initial_capital = 25000
@@ -34,57 +42,68 @@ def main():
             for by_sar in by_sar_list:
                 for price_range_mean_as_R in price_range_mean_as_R_list:
                     for by_big_k in by_big_k_list:
-                        if is_us_stock:
+                        for by_1h_k in by_1h_k_list:
-                            symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                            if is_us_stock:
-                                "symbols", ["QQQ"]
+                                symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-                            )
+                                    "symbols", ["QQQ"]
-                        else:
-                            symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
-                                "symbols", ["BTC-USDT"]
-                            )
-                        for symbol in symbols:
-                            logger.info(
-                                f"开始回测 {symbol}, 交易周期:{bar}, 开始日期:{start_date}, 结束日期:{end_date}, 是否是美股:{is_us_stock}, 交易方向:{direction}, 是否使用SAR:{by_sar}, 是否使用R为entry减stop:{price_range_mean_as_R}, 是否使用K线实体过50%:{by_big_k}"
-                            )
-                            symbol_bar_data = None
-                            found_symbol_bar_data = False
-                            for symbol_data_dict in symbol_data_cache:
-                                if (
-                                    symbol_data_dict["symbol"] == symbol
-                                    and symbol_data_dict["bar"] == bar
-                                ):
-                                    symbol_bar_data = symbol_data_dict["data"]
-                                    found_symbol_bar_data = True
-                                    break
-
-                            orb_strategy = ORBStrategy(
-                                symbol=symbol,
-                                bar=bar,
-                                start_date=start_date,
-                                end_date=end_date,
-                                is_us_stock=is_us_stock,
-                                direction=direction,
-                                by_sar=by_sar,
-                                profit_target_multiple=profit_target_multiple,
-                                initial_capital=initial_capital,
-                                max_leverage=max_leverage,
-                                risk_per_trade=risk_per_trade,
-                                commission_per_share=commission_per_share,
-                                symbol_bar_data=symbol_bar_data,
-                                price_range_mean_as_R=price_range_mean_as_R,
-                                by_big_k=by_big_k,
-                            )
-                            symbol_bar_data, trades_df, trades_summary_df = orb_strategy.run()
-                            if symbol_bar_data is None or len(symbol_bar_data) == 0:
-                                continue
-                            if not found_symbol_bar_data:
-                                symbol_data_cache.append(
-                                    {"symbol": symbol, "bar": bar, "data": symbol_bar_data}
                                 )
-                            if trades_summary_df is None or len(trades_summary_df) == 0:
+                            else:
-                                continue
+                                if is_binance:
-                            trades_summary_df_list.append(trades_summary_df)
+                                    symbols = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
-                            trades_df_list.append(trades_df)
+                                        "symbols", ["BTC-USDT"]
+                                    )
+                                else:
+                                    symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                                        "symbols", ["BTC-USDT"]
+                                    )
+                            for symbol in symbols:
+                                logger.info(
+                                    f"开始回测 {symbol}, 交易周期:{bar}, 开始日期:{start_date}, 结束日期:{end_date}, 是否是美股:{is_us_stock}, 交易方向:{direction}, 是否使用SAR:{by_sar}, 是否使用R为entry减stop:{price_range_mean_as_R}, 是否使用K线实体过50%:{by_big_k}"
+                                )
+                                symbol_bar_data = None
+                                symbol_1h_data = None
+                                found_symbol_bar_data = False
+                                for symbol_data_dict in symbol_data_cache:
+                                    if (
+                                        symbol_data_dict["symbol"] == symbol
+                                        and symbol_data_dict["bar"] == bar
+                                    ):
+                                        symbol_bar_data = symbol_data_dict["data"]
+                                        symbol_1h_data = symbol_data_dict["1h_data"]
+                                        found_symbol_bar_data = True
+                                        break
+
+                                orb_strategy = ORBStrategy(
+                                    symbol=symbol,
+                                    bar=bar,
+                                    start_date=start_date,
+                                    end_date=end_date,
+                                    is_us_stock=is_us_stock,
+                                    is_binance=is_binance,
+                                    direction=direction,
+                                    by_sar=by_sar,
+                                    profit_target_multiple=profit_target_multiple,
+                                    initial_capital=initial_capital,
+                                    max_leverage=max_leverage,
+                                    risk_per_trade=risk_per_trade,
+                                    commission_per_share=commission_per_share,
+                                    symbol_bar_data=symbol_bar_data,
+                                    symbol_1h_data=symbol_1h_data,
+                                    price_range_mean_as_R=price_range_mean_as_R,
+                                    by_big_k=by_big_k,
+                                    by_1h_k=by_1h_k,
+                                )
+                                symbol_bar_data, symbol_1h_data, trades_df, trades_summary_df = orb_strategy.run()
+                                if symbol_bar_data is None or len(symbol_bar_data) == 0:
+                                    continue
+                                if not found_symbol_bar_data:
+                                    symbol_data_cache.append(
+                                        {"symbol": symbol, "bar": bar, "data": symbol_bar_data, "1h_data": symbol_1h_data}
+                                    )
+                                if trades_summary_df is None or len(trades_summary_df) == 0:
+                                    continue
+                                trades_summary_df_list.append(trades_summary_df)
+                                trades_df_list.append(trades_df)
     total_trades_df = pd.concat(trades_df_list)
     total_trades_summary_df = pd.concat(trades_summary_df_list)
     statitics_dict = statistics_summary(total_trades_summary_df)
@@ -117,6 +136,9 @@ def main():
         statitics_dict["max_total_return_record_df_K_count"].to_excel(
             writer, sheet_name="最大总收益率_K线条件", index=False
         )
+        statitics_dict["max_total_return_record_df_1hK_count"].to_excel(
+            writer, sheet_name="最大总收益率_1小时K线条件", index=False
+        )
     chart_path = r"./output/trade_sandbox/orb_strategy/chart/"
     os.makedirs(chart_path, exist_ok=True)
     copy_chart_to_excel(chart_path, output_file_path)
@@ -170,6 +192,7 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
         summary["根据SAR"] = max_total_return_record["根据SAR"]
         summary["R算法"] = max_total_return_record["R算法"]
         summary["K线条件"] = max_total_return_record["K线条件"]
+        summary["1小时K线条件"] = max_total_return_record["1小时K线条件"]
         summary["总收益率%"] = max_total_return_record["总收益率%"]
         summary["自然收益率%"] = max_total_return_record["自然收益率%"]
         max_total_return_record_list.append(summary)
@@ -192,14 +215,14 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
             # 其它（如Series、list、dict、ndarray等）转字符串
             return str(v)
 
-        for key_col in ["方向", "根据SAR", "R算法", "K线条件"]:
+        for key_col in ["方向", "根据SAR", "R算法", "K线条件", "1小时K线条件"]:
             if key_col in max_total_return_record_df.columns:
                 max_total_return_record_df[key_col] = max_total_return_record_df[
                     key_col
                 ].apply(_to_hashable_scalar)
         # 分组统计
         max_total_return_record_df_grouped_count = (
-            max_total_return_record_df.groupby(["方向", "根据SAR", "R算法", "K线条件"], dropna=False)
+            max_total_return_record_df.groupby(["方向", "根据SAR", "R算法", "K线条件", "1小时K线条件"], dropna=False)
             .size()
             .reset_index(name="数量")
         )
@@ -251,18 +274,27 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
             by="数量", ascending=False, inplace=True
         )
         max_total_return_record_df_K_count.reset_index(drop=True, inplace=True)
+
+        # 统计1小时K线条件的记录数目
+        max_total_return_record_df_1hK_count = (
+            max_total_return_record_df.groupby(["1小时K线条件"], dropna=False)
+            .size()
+            .reset_index(name="数量")
+        )
+        max_total_return_record_df_1hK_count.sort_values(
+            by="数量", ascending=False, inplace=True
+        )
+        max_total_return_record_df_1hK_count.reset_index(drop=True, inplace=True)
     else:
         # 构造空结果，保证下游写入Excel不报错
         max_total_return_record_df_grouped_count = pd.DataFrame(
-            columns=["方向", "根据SAR", "R算法", "K线条件", "数量"]
+            columns=["方向", "根据SAR", "R算法", "K线条件", "1小时K线条件", "数量"]
-        )        
-        max_total_return_record_df_direction_count = pd.DataFrame(
-            columns=["方向", "数量"]
         )        
+        max_total_return_record_df_direction_count = pd.DataFrame(columns=["方向", "数量"])
         max_total_return_record_df_sar_count = pd.DataFrame(columns=["根据SAR", "数量"])
         max_total_return_record_df_R_count = pd.DataFrame(columns=["R算法", "数量"])
         max_total_return_record_df_K_count = pd.DataFrame(columns=["K线条件", "数量"])
-
+        max_total_return_record_df_1hK_count = pd.DataFrame(columns=["1小时K线条件", "数量"])
     result = {
         "statistics_summary_df": statistics_summary_df,
         "max_total_return_record_df": max_total_return_record_df,
@@ -271,6 +303,7 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
         "max_total_return_record_df_sar_count": max_total_return_record_df_sar_count,
         "max_total_return_record_df_R_count": max_total_return_record_df_R_count,
         "max_total_return_record_df_K_count": max_total_return_record_df_K_count,
+        "max_total_return_record_df_1hK_count": max_total_return_record_df_1hK_count,
     }
     return result
 

play.py

@@ -2,6 +2,10 @@ import logging
 from core.biz.quant_trader import QuantTrader
 from core.biz.strategy import QuantStrategy
 
+from config import MYSQL_CONFIG
+from sqlalchemy import create_engine, exc, text
+import pandas as pd
+
 logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
 
 def main() -> None:
@@ -94,5 +98,78 @@ def main() -> None:
         else:
             logging.warning("无效选择，请重新输入")
 
+
+def test_query():
+    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")
+    db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
+    db_engine = create_engine(
+                db_url,
+                pool_size=25,  # 连接池大小
+                max_overflow=10,  # 允许的最大溢出连接
+                pool_timeout=30,  # 连接超时时间（秒）
+                pool_recycle=60,  # 连接回收时间（秒），避免长时间闲置
+            )
+    sql = "SELECT symbol, min(date_time), max(date_time) FROM okx.crypto_binance_data where bar='5m' group by symbol;"
+    condition_dict = {}
+    return_multi = True
+    try:
+        result = query_data(db_engine, sql, condition_dict, return_multi)
+        if result is not None and len(result) > 0:
+            data = pd.DataFrame(result)
+            data.columns = ["symbol", "min_date_time", "max_date_time"]
+            print(data)
+            # output to excel
+            data.to_excel("./data/binance/crypto_binance_data_5m.xlsx", index=False)
+    except Exception as e:
+        print(f"查询数据出错: {e}")
+        return None
+
+def transform_data_type(data: dict):
+    """
+    遍历字典，将所有Decimal类型的值转换为float类型
+    """
+    from decimal import Decimal
+    for key, value in data.items():
+        if isinstance(value, Decimal):
+            data[key] = float(value)
+    return data
+
+
+def query_data(db_engine, sql: str, condition_dict: dict, return_multi: bool = True):
+    """
+    查询数据
+    :param sql: 查询SQL
+    :param db_url: 数据库连接URL
+    """
+    try:
+        with db_engine.connect() as conn:
+            result = conn.execute(text(sql), condition_dict)
+            if return_multi:
+                result = result.fetchall()
+                if result:
+                    result_list = [
+                        transform_data_type(dict(row._mapping)) for row in result
+                    ]
+                    return result_list
+                else:
+                    return None
+            else:
+                result = result.fetchone()
+                if result:
+                    result_dict = transform_data_type(dict(result._mapping))
+                    return result_dict
+                else:
+                    return None
+    except Exception as e:
+        print(f"查询数据出错: {e}")
+        return None
+
 if __name__ == "__main__":
-    main()
+    # main()
+    test_query()

sql/query/sql_playground.sql

@@ -1,11 +1,24 @@
+use okx;
+
+select DISTINCT symbol from crypto_huge_volume
+where symbol in ("QQQ", "TQQQ", "MSFT", "AAPL", "GOOG", "NVDA", "META", "AMZN", "TSLA", "AVGO") and bar="30m"
+and window_size=120
+group by symbol
+
 select * from crypto_market_data
-where symbol = "TQQQ" and bar="5m"
+where symbol="PLTR" and bar="5m"
-order by date_time_us DESC;
+order by date_time_us
 
-select * from crypto_huge_volume
-where symbol = "QQQ" and bar="5m"
-order by date_time;
 
+select * from crypto_market_data
+where symbol = "BTC-USDT" and bar="1H" #and open between 114271 and 114272 # AND date_time > "2025-08-21"
+order by date_time desc;
+
+select count(1)  from crypto_market_data where
+symbol in ("QQQ", "TQQQ", "MSFT", "AAPL", "GOOG", "NVDA", "META", "AMZN", "TSLA", "AVGO")
+
+select count(1)   from crypto_huge_volume where
+symbol in ("QQQ", "TQQQ", "MSFT", "AAPL", "GOOG", "NVDA", "META", "AMZN", "TSLA", "AVGO")
 
 
 select symbol, bar, date_time, close, 

sql/table/crypto_binance_data_essential_indexes.sql

@@ -0,0 +1,54 @@
+-- crypto_binance_data表核心索引优化脚本
+-- 针对500万+数据量的关键查询性能优化
+-- 基于实际代码中的查询模式分析
+
+-- 核心索引1: 主要查询模式索引
+-- 覆盖: WHERE symbol = ? AND bar = ? ORDER BY timestamp
+-- 这是代码中最常见的查询模式
+CREATE INDEX idx_symbol_bar_timestamp ON crypto_binance_data (symbol, bar, timestamp);
+
+-- 核心索引2: 时间范围查询索引  
+-- 覆盖: WHERE symbol = ? AND bar = ? AND timestamp BETWEEN ? AND ?
+-- 覆盖: WHERE symbol = ? AND bar = ? AND timestamp >= ? AND timestamp <= ?
+# CREATE INDEX idx_symbol_bar_timestamp_range ON crypto_binance_data (symbol, bar, timestamp);
+
+-- 核心索引3: 分组查询索引
+-- 覆盖: GROUP BY symbol, bar
+-- 覆盖: SELECT symbol, min(date_time), max(date_time) FROM crypto_binance_data WHERE bar='5m' GROUP BY symbol
+CREATE INDEX idx_bar_symbol ON crypto_binance_data (bar, symbol);
+
+-- 核心索引4: 技术指标查询索引
+-- 覆盖: WHERE symbol = ? AND bar = ? AND macd_signal = ?
+-- 覆盖: WHERE symbol = ? AND bar = ? AND kdj_signal = ?
+-- 覆盖: WHERE symbol = ? AND bar = ? AND rsi_signal = ?
+-- 覆盖: WHERE symbol = ? AND bar = ? AND boll_signal = ?
+-- 覆盖: WHERE symbol = ? AND bar = ? AND ma_cross = ?
+CREATE INDEX idx_symbol_bar_indicators ON crypto_binance_data (symbol, bar, macd_signal, kdj_signal, rsi_signal, boll_signal, ma_cross);
+
+-- 核心索引5: 时间戳排序索引
+-- 覆盖: ORDER BY timestamp DESC/ASC
+-- 优化: 查询最新数据 ORDER BY timestamp DESC LIMIT 1
+CREATE INDEX idx_timestamp_desc ON crypto_binance_data (timestamp DESC);
+
+-- 核心索引6: 日期时间查询索引
+-- 覆盖: WHERE date_time >= ? AND date_time <= ?
+-- 覆盖: ORDER BY date_time
+CREATE INDEX idx_date_time ON crypto_binance_data (date_time);
+
+-- 核心索引7: 成交量查询索引
+-- 覆盖: WHERE symbol = ? AND bar = ? AND volume > ?
+-- 覆盖: ORDER BY volume DESC
+CREATE INDEX idx_symbol_bar_volume ON crypto_binance_data (symbol, bar, volume);
+
+-- 核心索引8: 价格查询索引
+-- 覆盖: WHERE symbol = ? AND bar = ? AND close > ? AND close < ?
+-- 覆盖: ORDER BY close DESC/ASC
+CREATE INDEX idx_symbol_bar_close ON crypto_binance_data (symbol, bar, close);
+
+-- 执行前检查现有索引
+-- SHOW INDEX FROM crypto_binance_data;
+
+-- 执行后验证索引效果
+-- EXPLAIN SELECT * FROM crypto_binance_data WHERE symbol = 'BTC-USDT' AND bar = '5m' ORDER BY timestamp DESC LIMIT 100;
+-- EXPLAIN SELECT symbol, min(date_time), max(date_time) FROM crypto_binance_data WHERE bar='5m' GROUP BY symbol;
+-- EXPLAIN SELECT * FROM crypto_binance_data WHERE symbol = 'BTC-USDT' AND bar = '5m' AND timestamp BETWEEN 1640995200000 AND 1641081600000;

sql/table/crypto_binance_data_indexes.sql

@@ -0,0 +1,71 @@
+-- crypto_binance_data表索引优化脚本
+-- 针对500万+数据量的查询性能优化
+
+-- 1. 主要查询索引 - 覆盖最常见的查询模式
+-- 支持: WHERE symbol = ? AND bar = ? ORDER BY timestamp
+CREATE INDEX idx_symbol_bar_timestamp ON crypto_binance_data (symbol, bar, timestamp);
+
+-- 2. 时间范围查询索引 - 优化时间范围查询
+-- 支持: WHERE symbol = ? AND bar = ? AND timestamp BETWEEN ? AND ?
+CREATE INDEX idx_symbol_bar_timestamp_range ON crypto_binance_data (symbol, bar, timestamp);
+
+-- 3. 按symbol分组查询索引 - 优化GROUP BY symbol查询
+-- 支持: GROUP BY symbol, bar
+CREATE INDEX idx_symbol_bar ON crypto_binance_data (symbol, bar);
+
+-- 4. 时间戳排序索引 - 优化ORDER BY timestamp查询
+-- 支持: ORDER BY timestamp DESC/ASC
+CREATE INDEX idx_timestamp ON crypto_binance_data (timestamp);
+
+-- 5. 技术指标查询索引 - 优化技术指标相关查询
+-- 支持: WHERE symbol = ? AND bar = ? AND macd_signal = ?
+CREATE INDEX idx_symbol_bar_macd_signal ON crypto_binance_data (symbol, bar, macd_signal);
+
+-- 支持: WHERE symbol = ? AND bar = ? AND kdj_signal = ?
+CREATE INDEX idx_symbol_bar_kdj_signal ON crypto_binance_data (symbol, bar, kdj_signal);
+
+-- 支持: WHERE symbol = ? AND bar = ? AND rsi_signal = ?
+CREATE INDEX idx_symbol_bar_rsi_signal ON crypto_binance_data (symbol, bar, rsi_signal);
+
+-- 支持: WHERE symbol = ? AND bar = ? AND boll_signal = ?
+CREATE INDEX idx_symbol_bar_boll_signal ON crypto_binance_data (symbol, bar, boll_signal);
+
+-- 支持: WHERE symbol = ? AND bar = ? AND ma_cross = ?
+CREATE INDEX idx_symbol_bar_ma_cross ON crypto_binance_data (symbol, bar, ma_cross);
+
+-- 6. 复合技术指标查询索引 - 优化多条件技术指标查询
+-- 支持: WHERE symbol = ? AND bar = ? AND macd_signal = ? AND kdj_signal = ?
+CREATE INDEX idx_symbol_bar_macd_kdj ON crypto_binance_data (symbol, bar, macd_signal, kdj_signal);
+
+-- 7. 日期时间查询索引 - 优化按日期时间查询
+-- 支持: WHERE date_time >= ? AND date_time <= ?
+CREATE INDEX idx_date_time ON crypto_binance_data (date_time);
+
+-- 8. 交易对时间索引 - 优化特定交易对的时间查询
+-- 支持: WHERE symbol = ? ORDER BY timestamp
+CREATE INDEX idx_symbol_timestamp ON crypto_binance_data (symbol, timestamp);
+
+-- 9. 周期时间索引 - 优化特定周期的时间查询
+-- 支持: WHERE bar = ? ORDER BY timestamp
+CREATE INDEX idx_bar_timestamp ON crypto_binance_data (bar, timestamp);
+
+-- 10. 统计查询优化索引 - 优化COUNT, AVG等聚合查询
+-- 支持: SELECT COUNT(*) FROM crypto_binance_data WHERE symbol = ? AND bar = ?
+CREATE INDEX idx_symbol_bar_volume ON crypto_binance_data (symbol, bar, volume);
+
+-- 11. 价格相关查询索引 - 优化价格相关查询
+-- 支持: WHERE symbol = ? AND bar = ? AND close > ? AND close < ?
+CREATE INDEX idx_symbol_bar_close ON crypto_binance_data (symbol, bar, close);
+
+-- 12. 成交量相关查询索引 - 优化成交量相关查询
+-- 支持: WHERE symbol = ? AND bar = ? AND volume > ?
+CREATE INDEX idx_symbol_bar_volume_high ON crypto_binance_data (symbol, bar, volume);
+
+-- 删除可能存在的重复或低效索引
+-- 注意：在实际执行前请先检查现有索引，避免删除正在使用的索引
+
+-- 查看当前索引状态
+-- SHOW INDEX FROM crypto_binance_data;
+
+-- 分析查询性能
+-- EXPLAIN SELECT * FROM crypto_binance_data WHERE symbol = 'BTC-USDT' AND bar = '5m' ORDER BY timestamp DESC LIMIT 100;

sql/table/crypto_binance_huge_volume_essential_indexes.sql

@@ -0,0 +1,69 @@
+-- crypto_binance_huge_volume表核心索引优化脚本
+-- 针对2000万+数据量的关键查询性能优化
+-- 基于实际代码中的查询模式分析
+
+-- 核心索引1: 主要查询模式索引
+-- 覆盖: WHERE symbol = ? AND bar = ? AND window_size = ? ORDER BY timestamp
+-- 这是代码中最常见的查询模式，用于获取特定交易对、周期、窗口大小的数据
+CREATE INDEX idx_symbol_bar_window_size_timestamp ON crypto_binance_huge_volume (symbol, bar, window_size, timestamp);
+
+-- 核心索引2: 时间范围查询索引
+-- 覆盖: WHERE symbol = ? AND bar = ? AND window_size = ? AND timestamp BETWEEN ? AND ?
+-- 覆盖: WHERE symbol = ? AND bar = ? AND window_size = ? AND timestamp >= ? AND timestamp <= ?
+# CREATE INDEX idx_symbol_bar_window_size_timestamp_range ON crypto_binance_huge_volume (symbol, bar, window_size, timestamp);
+
+-- 核心索引3: 巨量交易查询索引
+-- 覆盖: WHERE huge_volume = 1
+-- 覆盖: WHERE symbol = ? AND bar = ? AND window_size = ? AND huge_volume = 1
+CREATE INDEX idx_huge_volume_symbol_bar_window_size ON crypto_binance_huge_volume (huge_volume, symbol, bar, window_size);
+
+-- 核心索引4: 成交量比率排序索引
+-- 覆盖: ORDER BY volume_ratio DESC
+-- 覆盖: WHERE huge_volume = 1 ORDER BY volume_ratio DESC
+CREATE INDEX idx_volume_ratio_desc ON crypto_binance_huge_volume (volume_ratio DESC);
+
+-- 核心索引5: 价格分位数高点查询索引
+-- 覆盖: WHERE close_80_high = 1, close_90_high = 1
+-- 覆盖: WHERE symbol = ? AND bar = ? AND window_size = ? AND close_80_high = 1
+CREATE INDEX idx_close_80_90_high ON crypto_binance_huge_volume (close_80_high, close_90_high, symbol, bar, window_size);
+
+-- 核心索引6: 价格分位数低点查询索引
+-- 覆盖: WHERE close_20_low = 1, close_10_low = 1
+-- 覆盖: WHERE symbol = ? AND bar = ? AND window_size = ? AND close_20_low = 1
+CREATE INDEX idx_close_20_10_low ON crypto_binance_huge_volume (close_20_low, close_10_low, symbol, bar, window_size);
+
+-- 核心索引7: 最高价分位数查询索引
+-- 覆盖: WHERE high_80_high = 1, high_90_high = 1, high_20_low = 1, high_10_low = 1
+CREATE INDEX idx_high_percentiles ON crypto_binance_huge_volume (high_80_high, high_90_high, high_20_low, high_10_low, symbol, bar, window_size);
+
+-- 核心索引8: 最低价分位数查询索引
+-- 覆盖: WHERE low_80_high = 1, low_90_high = 1, low_20_low = 1, low_10_low = 1
+CREATE INDEX idx_low_percentiles ON crypto_binance_huge_volume (low_80_high, low_90_high, low_20_low, low_10_low, symbol, bar, window_size);
+
+-- 核心索引9: 复合量价尖峰查询索引
+-- 覆盖: WHERE huge_volume = 1 AND (close_80_high = 1 OR close_20_low = 1)
+-- 覆盖: WHERE huge_volume = 1 AND (close_90_high = 1 OR close_10_low = 1)
+CREATE INDEX idx_huge_volume_price_spike ON crypto_binance_huge_volume (huge_volume, close_80_high, close_20_low, close_90_high, close_10_low, symbol, bar, window_size);
+
+-- 核心索引10: 分组统计查询索引
+-- 覆盖: GROUP BY symbol, bar, window_size
+-- 覆盖: GROUP BY symbol, bar
+CREATE INDEX idx_symbol_bar_window_size_group ON crypto_binance_huge_volume (symbol, bar, window_size);
+
+-- 核心索引11: 时间戳排序索引
+-- 覆盖: ORDER BY timestamp DESC/ASC
+-- 优化: 查询最新数据 ORDER BY timestamp DESC LIMIT 1
+CREATE INDEX idx_timestamp_desc ON crypto_binance_huge_volume (timestamp DESC);
+
+-- 核心索引12: 日期时间查询索引
+-- 覆盖: WHERE date_time >= ? AND date_time <= ?
+-- 覆盖: ORDER BY date_time
+CREATE INDEX idx_date_time ON crypto_binance_huge_volume (date_time);
+
+-- 执行前检查现有索引
+-- SHOW INDEX FROM crypto_binance_huge_volume;
+
+-- 执行后验证索引效果
+-- EXPLAIN SELECT * FROM crypto_binance_huge_volume WHERE symbol = 'BTC-USDT' AND bar = '5m' AND window_size = 50 ORDER BY timestamp DESC LIMIT 100;
+-- EXPLAIN SELECT * FROM crypto_binance_huge_volume WHERE huge_volume = 1 AND close_80_high = 1 ORDER BY volume_ratio DESC LIMIT 10;
+-- EXPLAIN SELECT COUNT(*) FROM crypto_binance_huge_volume WHERE symbol = 'BTC-USDT' AND bar = '5m' AND window_size = 50;