statistics orb strategy

core/trade/orb_trade.py

@@ -4,6 +4,12 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+from openpyxl import Workbook
+from openpyxl.drawing.image import Image
+import openpyxl
+from openpyxl.styles import Font
+from PIL import Image as PILImage
+
 import core.logger as logging
 from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 from core.db.db_market_data import DBMarketData
@@ -19,11 +25,19 @@ logger = logging.logger
 class ORBStrategy:
     def __init__(
         self,
+        symbol: str,
+        bar: str,
+        start_date: str,
+        end_date: str,
         initial_capital=25000,
         max_leverage=4,
         risk_per_trade=0.01,
         commission_per_share=0.0005,
+        profit_target_multiple=10,
         is_us_stock=False,
+        direction=None,
+        by_sar=False,
+        symbol_bar_data=None,
     ):
         """
         初始化ORB策略参数
@@ -37,18 +51,31 @@ class ORBStrategy:
         6. 止损/止盈：根据$R计算，$R=|entry_price-stop_price|
         7. 盈利目标：10R，即10*$R
         8. 账户净值曲线：账户价值与市场价格
+        :param symbol: 股票代码
+        :param bar: K线周期
+        :param start_date: 开始日期
+        :param end_date: 结束日期
         :param initial_capital: 初始账户资金（美元）
         :param max_leverage: 最大杠杆倍数（默认4倍，符合FINRA规定）
         :param risk_per_trade: 单次交易风险比例（默认1%）
         :param commission_per_share: 每股交易佣金（美元，默认0.0005）
+        :param profit_target_multiple: 盈利目标倍数（默认10倍$R，即10R）
+        :param is_us_stock: 是否是美股
+        :param 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(
-            f"初始化ORB策略参数：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}"
+            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}"
         )
+        self.symbol = symbol
+        self.bar = bar
+        self.start_date = start_date
+        self.end_date = end_date
         self.initial_capital = initial_capital
         self.max_leverage = max_leverage
         self.risk_per_trade = risk_per_trade
         self.commission_per_share = commission_per_share
+        self.profit_target_multiple = profit_target_multiple
         self.data = None  # 存储K线数据
         self.trades = []  # 存储交易记录
         self.equity_curve = None  # 存储账户净值曲线
@@ -64,9 +91,35 @@ class ORBStrategy:
         self.db_market_data = DBMarketData(self.db_url)
         self.is_us_stock = is_us_stock
         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)
+        os.makedirs(self.output_excel_folder, exist_ok=True)
+        self.direction = direction
+        self.by_sar = by_sar
+        self.direction_desc = "既做多又做空"
+        if self.direction == "Long":
+            self.direction_desc = "做多"
+        elif self.direction == "Short":
+            self.direction_desc = "做空"
+        
+        self.sar_desc = "不考虑SAR"
+        if self.by_sar:
+            self.sar_desc = "考虑SAR"
+        self.symbol_bar_data = symbol_bar_data
+    
+    def run(self):
+        """
+        运行ORB策略
+        """
+        self.fetch_intraday_data()
+        self.generate_orb_signals()
+        self.backtest()
+        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
 
-    def fetch_intraday_data(self, symbol, start_date, end_date, interval="5m"):
+    def fetch_intraday_data(self):
         """
         获取日内5分钟K线数据（需yfinance支持，部分数据可能有延迟）
         :param ticker: 股票代码（如QQQ、TQQQ）
@@ -74,36 +127,69 @@ class ORBStrategy:
         :param end_date: 结束日期（格式：YYYY-MM-DD）
         :param interval: K线周期（默认5分钟）
         """
-        logger.info(f"开始获取{symbol}数据：{start_date}至{end_date}，间隔{interval}")
+        logger.info(f"开始获取{self.symbol}数据：{self.start_date}至{self.end_date}，间隔{self.bar}")
-        # data = yf.download(
+        if self.symbol_bar_data is None or len(self.symbol_bar_data) == 0:
-        #     symbol, start=start_date, end=end_date, interval=interval, progress=False
+            data = self.db_market_data.query_market_data_by_symbol_bar(
-        # )
+                self.symbol, self.bar, start=self.start_date, end=self.end_date
-        data = self.db_market_data.query_market_data_by_symbol_bar(
+            )
-            symbol, interval, start=start_date, end=end_date
+            data = pd.DataFrame(data)
-        )
+            data.sort_values(by="date_time", inplace=True)
-        data = pd.DataFrame(data)
+            # 保留核心列：开盘价、最高价、最低价、收盘价、成交量
-        data.sort_values(by="date_time", inplace=True)
+            data["Open"] = data["open"]
-        # 保留核心列：开盘价、最高价、最低价、收盘价、成交量
+            data["High"] = data["high"]
-        data["Open"] = data["open"]
+            data["Low"] = data["low"]
-        data["High"] = data["high"]
+            data["Close"] = data["close"]
-        data["Low"] = data["low"]
+            data["Volume"] = data["volume"]
-        data["Close"] = data["close"]
+            if self.is_us_stock:
-        data["Volume"] = data["volume"]
+                date_time_field = "date_time_us"
-        if self.is_us_stock:
+            else:
-            date_time_field = "date_time_us"
+                date_time_field = "date_time"
-        else:
+            data[date_time_field] = pd.to_datetime(data[date_time_field])
-            date_time_field = "date_time"
+            # 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_time_field] = pd.to_datetime(data[date_time_field])
+            data["Date"] = data[date_time_field].dt.date
-        # 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
+            # 将Date转换为datetime64[ns]类型以确保类型一致
-        data["Date"] = data[date_time_field].dt.date
+            data["Date"] = pd.to_datetime(data["Date"])
-        # 将Date转换为datetime64[ns]类型以确保类型一致
+            # 最小data["Date"]
-        data["Date"] = pd.to_datetime(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[
+            self.data = data[
-            ["symbol", "bar", "Date", date_time_field, "Open", "High", "Low", "Close", "Volume"]
+                [
-        ].copy()
+                    "symbol",
-        self.data.rename(columns={date_time_field: "date_time"}, inplace=True)
+                    "bar",
-        logger.info(f"成功获取{symbol}数据：{len(self.data)}根{interval}K线")
+                    "Date",
+                    date_time_field,
+                    "Open",
+                    "High",
+                    "Low",
+                    "Close",
+                    "Volume",
+                    "sar_signal",
+                ]
+            ].copy()
+            self.data.rename(columns={date_time_field: "date_time"}, inplace=True)
+            self.symbol_bar_data = self.data.copy()
+        else:
+            self.data = self.symbol_bar_data.copy()
+
+        # 获取Close的mean
+        self.close_mean = self.data["Close"].mean()
+        if self.close_mean > 10000:
+            self.initial_capital = self.initial_capital * 10000
+        elif self.close_mean > 5000:
+            self.initial_capital = self.initial_capital * 5000
+        elif self.close_mean > 1000:
+            self.initial_capital = self.initial_capital * 1000
+        elif self.close_mean > 500:
+            self.initial_capital = self.initial_capital * 500
+        elif self.close_mean > 100:
+            self.initial_capital = self.initial_capital * 100
+        else:
+            pass
+        logger.info(f"收盘价均值：{self.close_mean}")
+        logger.info(f"初始资金调整为：{self.initial_capital}")
+        logger.info(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 calculate_shares(self, account_value, entry_price, stop_price):
         """
@@ -134,20 +220,15 @@ class ORBStrategy:
 
         return int(max_shares)  # 股数取整
 
-    def generate_orb_signals(self, direction: str = None, by_sar: bool = False):
+    def generate_orb_signals(self):
         """
         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
         """
-        direction_desc = "既做多又做空"
+        logger.info(
-        if direction == "Long":
+            f"开始生成ORB策略信号：{self.direction_desc}，根据SAR指标：{self.by_sar}"
-            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")
 
@@ -164,6 +245,7 @@ class ORBStrategy:
             low1 = first_candle["Low"]
             open1 = first_candle["Open"]
             close1 = first_candle["Close"]
+            sar_signal = first_candle["sar_signal"]
 
             # 第二根5分钟K线（entry信号）
             second_candle = daily_data.iloc[1]
@@ -171,11 +253,19 @@ 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 and (direction == "Long" or direction is None):
+            if (
+                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)
+            ):
                 # 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 and (direction == "Short" or direction is None):
+            elif (
+                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)
+            ):
                 # 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
@@ -213,12 +303,12 @@ class ORBStrategy:
             f"生成信号完成：共{len(signals_df)}个交易日，其中多头{sum(signals_df['Signal']=='Long')}次，空头{sum(signals_df['Signal']=='Short')}次"
         )
 
-    def backtest(self, profit_target_multiple=10):
+    def backtest(self):
         """
         回测ORB策略
         :param profit_target_multiple: 盈利目标倍数（默认10倍$R，即10R）
         """
-        logger.info(f"开始回测ORB策略：盈利目标倍数={profit_target_multiple}")
+        logger.info(f"开始回测ORB策略：盈利目标倍数={self.profit_target_multiple}")
         if "Signal" not in self.data.columns:
             raise ValueError("请先调用generate_orb_signals生成策略信号")
 
@@ -255,9 +345,9 @@ class ORBStrategy:
             low1 = signal_row["Low1"]
             risk_assumed = abs(entry_price - stop_price)  # 计算$R
             profit_target = (
-                entry_price + (risk_assumed * profit_target_multiple)
+                entry_price + (risk_assumed * self.profit_target_multiple)
                 if signal == "Long"
-                else entry_price - (risk_assumed * profit_target_multiple)
+                else entry_price - (risk_assumed * self.profit_target_multiple)
             )
 
             # 计算交易股数
@@ -293,7 +383,7 @@ class ORBStrategy:
                         break
                     elif high >= profit_target:
                         exit_price = profit_target
-                        exit_reason = "Profit Target (10R)"
+                        exit_reason = f"Profit Target ({self.profit_target_multiple}R)"
                         exit_time = row["date_time"]
                         break
                 elif signal == "Short":
@@ -305,7 +395,7 @@ class ORBStrategy:
                         break
                     elif low <= profit_target:
                         exit_price = profit_target
-                        exit_reason = "Profit Target (10R)"
+                        exit_reason = f"Profit Target ({self.profit_target_multiple}R)"
                         exit_time = row["date_time"]
                         break
 
@@ -333,6 +423,10 @@ class ORBStrategy:
             self.trades.append(
                 {
                     "TradeID": trade_id,
+                    "Direction": self.direction_desc,
+                    "BySar": self.sar_desc,
+                    "Symbol": self.symbol,
+                    "Bar": self.bar,
                     "Date": date,
                     "Signal": signal,
                     "EntryTime": signal_row.date_time.strftime("%Y-%m-%d %H:%M:%S"),
@@ -353,40 +447,91 @@ class ORBStrategy:
             equity_history.append(account_value)
             trade_id += 1
 
+        if len(self.trades) == 0:
+            logger.info("没有交易")
+            self.trades_df = pd.DataFrame()
+            self.initial_trade_summary()
+            return
         # 生成净值曲线
         self.create_equity_curve()
 
         # 输出回测结果
-        trades_df = pd.DataFrame(self.trades)
+        self.trades_df = pd.DataFrame(self.trades)
+        self.trades_df.sort_values(by="ExitTime", inplace=True)
         total_return = (
             (account_value - self.initial_capital) / self.initial_capital * 100
         )
         win_rate = (
-            (trades_df["ProfitLoss"] > 0).sum() / len(trades_df) * 100
+            (self.trades_df["ProfitLoss"] > 0).sum() / len(self.trades_df) * 100
-            if len(trades_df) > 0
+            if len(self.trades_df) > 0
             else 0
         )
         # 计算盈亏比
-        profit_sum = trades_df[trades_df["ProfitLoss"] > 0]["ProfitLoss"].sum()
+        profit_sum = self.trades_df[self.trades_df["ProfitLoss"] > 0]["ProfitLoss"].sum()
-        loss_sum = abs(trades_df[trades_df["ProfitLoss"] < 0]["ProfitLoss"].sum())
+        loss_sum = abs(self.trades_df[self.trades_df["ProfitLoss"] < 0]["ProfitLoss"].sum())
         if loss_sum == 0:
-            profit_loss_ratio = float('inf')
+            profit_loss_ratio = float("inf")
         else:
             profit_loss_ratio = (profit_sum / loss_sum) * 100
+        first_entry_price = self.trades_df.iloc[0]["EntryPrice"]
+        last_exit_price = self.trades_df.iloc[-1]["ExitPrice"]
+        natural_return = (last_exit_price - first_entry_price) / first_entry_price * 100
+        self.initial_trade_summary()
+        if len(self.trades_df) > 0:
+            logger.info("\n" + "=" * 50)
+            logger.info("ORB策略回测结果")
+            logger.info("=" * 50)
+            logger.info(f"股票代码：{self.symbol}")
+            logger.info(f"K线周期：{self.bar}")
+            logger.info(f"开始日期：{self.start_date}")
+            logger.info(f"结束日期：{self.end_date}")
+            logger.info(f"盈利目标倍数：{self.profit_target_multiple}")
+            logger.info(f"初始资金：${self.initial_capital:,.2f}")
+            logger.info(f"最终资金：${account_value:,.2f}")
+            self.trades_summary["最终资金$"] = account_value
+            logger.info(f"总收益率：{total_return:.2f}%")
+            self.trades_summary["总收益率%"] = total_return
+            logger.info(f"自然收益率：{natural_return:.2f}%")
+            self.trades_summary["自然收益率%"] = natural_return
+            logger.info(f"总交易次数：{len(self.trades_df)}")
+            self.trades_summary["总交易次数"] = len(self.trades_df)
+            logger.info(f"盈亏比：{profit_loss_ratio:.2f}%")
+            self.trades_summary["盈亏比%"] = profit_loss_ratio
+            logger.info(f"胜率：{win_rate:.2f}%")
+            self.trades_summary["胜率%"] = win_rate
+            logger.info(f"平均每笔盈亏：${self.trades_df['ProfitLoss'].mean():.2f}")
+            self.trades_summary["平均每笔盈亏$"] = self.trades_df["ProfitLoss"].mean()
+            logger.info(f"最大单笔盈利：${self.trades_df['ProfitLoss'].max():.2f}")
+            self.trades_summary["最大单笔盈利$"] = self.trades_df["ProfitLoss"].max()
+            logger.info(f"最大单笔亏损：${abs(self.trades_df['ProfitLoss'].min()):.2f}")
+            self.trades_summary["最大单笔亏损$"] = abs(self.trades_df["ProfitLoss"].min())
+        else:
+            logger.info("没有交易")
+        self.trades_summary_df = pd.DataFrame([self.trades_summary])
+    
+    def initial_trade_summary(self):
+        """
+        初始化交易总结
+        """
+        self.trades_summary = {}
+        self.trades_summary["方向"] = self.direction_desc
+        self.trades_summary["根据SAR"] = self.sar_desc
+        self.trades_summary["股票代码"] = self.symbol
+        self.trades_summary["K线周期"] = self.bar
+        self.trades_summary["开始日期"] = self.start_date
+        self.trades_summary["结束日期"] = self.end_date
+        self.trades_summary["盈利目标倍数"] = self.profit_target_multiple
+        self.trades_summary["初始资金$"] = self.initial_capital
+        self.trades_summary["最终资金$"] = self.initial_capital
+        self.trades_summary["总收益率%"] = 0
+        self.trades_summary["自然收益率%"] = 0
+        self.trades_summary["总交易次数"] = 0
+        self.trades_summary["盈亏比%"] = 0
+        self.trades_summary["胜率%"] = 0
+        self.trades_summary["平均每笔盈亏$"] = 0
+        self.trades_summary["最大单笔盈利$"] = 0
+        self.trades_summary["最大单笔亏损$"] = 0
 
-        logger.info("\n" + "=" * 50)
-        logger.info("ORB策略回测结果")
-        logger.info("=" * 50)
-        logger.info(f"初始资金：${self.initial_capital:,.2f}")
-        logger.info(f"最终资金：${account_value:,.2f}")
-        logger.info(f"总收益率：{total_return:.2f}%")
-        logger.info(f"总交易次数：{len(trades_df)}")
-        logger.info(f"盈亏比：{profit_loss_ratio:.2f}%")
-        logger.info(f"胜率：{win_rate:.2f}%")
-        if len(trades_df) > 0:
-            logger.info(f"平均每笔盈亏：${trades_df['ProfitLoss'].mean():.2f}")
-            logger.info(f"最大单笔盈利：${trades_df['ProfitLoss'].max():.2f}")
-            logger.info(f"最大单笔亏损：${trades_df['ProfitLoss'].min():.2f}")
 
     def create_equity_curve(self):
         """
@@ -437,9 +582,25 @@ class ORBStrategy:
         account_value_to_1 = self.equity_curve["AccountValue"] / first_account_value
         market_price_to_1 = self.equity_curve["MarketPrice"] / first_market_price
         plt.figure(figsize=(12, 6))
-        plt.plot(self.equity_curve["DateTime"], account_value_to_1, label="账户价值", color='blue', linewidth=2, marker='o', markersize=4)
+        plt.plot(
-        plt.plot(self.equity_curve["DateTime"], market_price_to_1, label="市场价格", color='green', linewidth=2, marker='s', markersize=4)
+            self.equity_curve["DateTime"],
-        plt.title(f"ORB策略账户净值曲线 {symbol} {bar}", fontsize=14, fontweight='bold')
+            account_value_to_1,
+            label="账户价值",
+            color="blue",
+            linewidth=2,
+            marker="o",
+            markersize=4,
+        )
+        plt.plot(
+            self.equity_curve["DateTime"],
+            market_price_to_1,
+            label="市场价格",
+            color="green",
+            linewidth=2,
+            marker="s",
+            markersize=4,
+        )
+        plt.title(f"ORB曲线 {symbol} {bar} {self.direction_desc} {self.sar_desc}", fontsize=14, fontweight="bold")
         plt.xlabel("时间", fontsize=12)
         plt.ylabel("涨跌变化", fontsize=12)
         plt.legend(fontsize=11)
@@ -450,54 +611,108 @@ class ORBStrategy:
         if len(self.equity_curve) > 30:
             # 如果数据点较多，选择间隔显示，但确保第一条和最后一条始终显示
             step = max(1, len(self.equity_curve) // 30)
-            
+
             # 创建标签索引列表，确保包含首尾数据
             label_indices = [0]  # 第一条
-            
+
             # 添加中间间隔的标签
             for i in range(step, len(self.equity_curve) - 1, step):
                 label_indices.append(i)
-            
+
             # 添加最后一条（如果还没有包含的话）
             if len(self.equity_curve) - 1 not in label_indices:
                 label_indices.append(len(self.equity_curve) - 1)
-            
+
             # 设置x轴标签
-            plt.xticks(self.equity_curve["DateTime"].iloc[label_indices], 
+            plt.xticks(
-                        self.equity_curve["DateTime"].iloc[label_indices], 
+                self.equity_curve["DateTime"].iloc[label_indices],
-                        rotation=45, ha='right', fontsize=10)
+                self.equity_curve["DateTime"].iloc[label_indices],
+                rotation=45,
+                ha="right",
+                fontsize=10,
+            )
         else:
             # 如果数据点较少，全部显示
-            plt.xticks(self.equity_curve["DateTime"], 
+            plt.xticks(
-                        self.equity_curve["DateTime"], 
+                self.equity_curve["DateTime"],
-                        rotation=45, ha='right', fontsize=10)
+                self.equity_curve["DateTime"],
+                rotation=45,
+                ha="right",
+                fontsize=10,
+            )
         plt.tight_layout()
-        save_path = f"{self.output_chart_folder}/{symbol}_{bar}_orb_strategy_equity_curve.png"
+        self.chart_save_path = (
-        plt.savefig(save_path, dpi=150, bbox_inches='tight')
+            f"{self.output_chart_folder}/{symbol}_{bar}_{self.direction_desc}_{self.sar_desc}_orb_strategy_equity_curve.png"
+        )
+        plt.savefig(self.chart_save_path, dpi=150, bbox_inches="tight")
         plt.close()
+    
+    def output_trade_summary(self):
+        """
+        输出交易明细，交易总结与Chart图片到Excel
+        """
+        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}.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:
+            self.trades_df.to_excel(writer, sheet_name="交易明细", index=False)
+            self.trades_summary_df.to_excel(writer, sheet_name="交易总结", index=False)
+        if os.path.exists(self.chart_save_path):
+            charts_dict = {
+                "账户净值曲线": self.chart_save_path
+            }
+            self.output_chart_to_excel(output_file_path, charts_dict)
+    
+    def output_chart_to_excel(self, excel_file_path: str, charts_dict: dict):
+        """
+        输出Excel文件，包含所有图表
+        charts_dict: 图表数据字典，格式为：
+        {
+            "sheet_name": {
+                "chart_name": "chart_path"
+            }
+        }
+        """
+        logger.info(f"将图表输出到{excel_file_path}")
 
+        # 打开已经存在的Excel文件
+        wb = openpyxl.load_workbook(excel_file_path)
+
+        for sheet_name, chart_path in charts_dict.items():
+            try:
+                ws = wb.create_sheet(title=sheet_name)
+                row_offset = 1
+                # Insert chart image
+                img = Image(chart_path)
+                ws.add_image(img, f"A{row_offset}")
+
+            except Exception as e:
+                logger.error(f"输出Excel Sheet {sheet_name} 失败: {e}")
+                continue
+        # Save Excel file
+        wb.save(excel_file_path)
+        logger.info(f"图表已输出到{excel_file_path}")
+    
 
 # ------------------- 策略示例：回测QQQ的ORB策略（2016-2023） -------------------
 if __name__ == "__main__":
 
     # 初始化ORB策略
     orb_strategy = ORBStrategy(
+        symbol="ETH-USDT",
+        bar="5m",
+        start_date="2025-05-15",
+        end_date="2025-08-20",
         initial_capital=25000,
         max_leverage=4,
         risk_per_trade=0.01,
         commission_per_share=0.0005,
+        profit_target_multiple=10,
+        is_us_stock=False,
+        direction=None,
+        by_sar=False,
     )
 
-    # 1. 获取QQQ的5分钟日内数据（2024-2025，注意：yfinance免费版可能限制历史日内数据，建议用专业数据源）
+    orb_strategy.run()
-    orb_strategy.fetch_intraday_data(
-        symbol="ETH-USDT", start_date="2025-05-15", end_date="2025-08-20", interval="5m"
-    )
-
-    # 2. 生成ORB策略信号
-    orb_strategy.generate_orb_signals()
-
-    # 3. 回测策略（盈利目标10R）
-    orb_strategy.backtest(profit_target_multiple=10)
-
-    # 4. 绘制净值曲线
-    orb_strategy.plot_equity_curve()

orb_trade_main.py

@@ -1,35 +1,343 @@
 from core.trade.orb_trade import ORBStrategy
-from config import US_STOCK_MONITOR_CONFIG
+from config import US_STOCK_MONITOR_CONFIG, OKX_MONITOR_CONFIG
 import core.logger as logging
+from datetime import datetime
+from openpyxl import Workbook
+from openpyxl.drawing.image import Image
+import openpyxl
+import pandas as pd
+import os
 
 logger = logging.logger
 
+
 def main():
-    symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["QQQ"])
+    is_us_stock_list = [True, False]
+    bar = "5m"
+    direction_list = [None, "Long", "Short"]
+    by_sar_list = [False, True]
+    start_date = "2024-01-01"
+    end_date = datetime.now().strftime("%Y-%m-%d")
+    profit_target_multiple = 10
+    initial_capital = 25000
+    max_leverage = 4
+    risk_per_trade = 0.01
+    commission_per_share = 0.0005
+
+    trades_df_list = []
+    trades_summary_df_list = []
+    symbol_data_cache = []
+    for is_us_stock in is_us_stock_list:
+        for direction in direction_list:
+            for by_sar in by_sar_list:
+                if is_us_stock:
+                    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}"
+                    )
+                    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,
+                    )
+                    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:
+                        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)
+    output_excel_folder = r"./output/trade_sandbox/orb_strategy/excel/summary/"
+    os.makedirs(output_excel_folder, exist_ok=True)
+    now_str = datetime.now().strftime("%Y%m%d%H%M%S")
+    excel_file_name = f"orb_strategy_summary_{now_str}.xlsx"
+    output_file_path = os.path.join(output_excel_folder, excel_file_name)
+    with pd.ExcelWriter(output_file_path) as writer:
+        total_trades_df.to_excel(writer, sheet_name="交易详情", index=False)
+        total_trades_summary_df.to_excel(writer, sheet_name="交易总结", index=False)
+        statitics_dict["statistics_summary_df"].to_excel(
+            writer, sheet_name="统计总结", index=False
+        )
+        statitics_dict["max_total_return_record_df"].to_excel(
+            writer, sheet_name="最大总收益率记录", index=False
+        )
+        statitics_dict["max_total_return_record_df_grouped_count"].to_excel(
+            writer, sheet_name="最大总收益率记录_方向和根据SAR的组合", index=False
+        )
+        statitics_dict["max_total_return_record_df_direction_count"].to_excel(
+            writer, sheet_name="最大总收益率记录_方向", index=False
+        )
+        statitics_dict["max_total_return_record_df_sar_count"].to_excel(
+            writer, sheet_name="最大总收益率记录_根据SAR", 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)
+    logger.info(f"交易总结已输出到{output_file_path}")
+
+
+def statistics_summary(trades_summary_df: pd.DataFrame):
+    statistics_summary_list = []
+    summary = {}
+    # 1. 统计总收益率% > 0 的占比
+    total_return_gt_0 = trades_summary_df[trades_summary_df["总收益率%"] > 0].shape[0]
+    total_return_gt_0_ratio = round((total_return_gt_0 / trades_summary_df.shape[0]) * 100, 2)
+    summary["总收益率%>0占比"] = total_return_gt_0_ratio
+    logger.info(f"总收益率% > 0 的占比：{total_return_gt_0_ratio:.2f}%")
+    # 2. 统计总收益率% > 自然收益率% 的占比
+    total_return_gt_natural_return = trades_summary_df[
+        trades_summary_df["总收益率%"] > trades_summary_df["自然收益率%"]
+    ].shape[0]
+    total_return_gt_natural_return_ratio = (
+        round((total_return_gt_natural_return / trades_summary_df.shape[0]) * 100, 2)
+    )
+    summary["总收益率%>自然收益率%占比"] = total_return_gt_natural_return_ratio
+    logger.info(
+        f"总收益率% > 自然收益率% 的占比：{total_return_gt_natural_return_ratio:.2f}%"
+    )
+    statistics_summary_list.append(summary)
+    statistics_summary_df = pd.DataFrame(statistics_summary_list)
+
+    symbol_list = trades_summary_df["股票代码"].unique()
+    max_total_return_record_list = []
+    for symbol in symbol_list:
+        trades_summary_df_copy = trades_summary_df.copy()
+        symbol_trades_summary_df = trades_summary_df_copy[
+            trades_summary_df_copy["股票代码"] == symbol
+        ]
+        symbol_trades_summary_df.reset_index(drop=True, inplace=True)
+        if symbol_trades_summary_df.empty:
+            continue
+        # 过滤掉NaN，避免idxmax报错
+        valid_df = symbol_trades_summary_df[
+            symbol_trades_summary_df["总收益率%"].notna()
+        ]
+        if valid_df.empty:
+            continue
+        # 获得总收益率%最大的记录
+        max_idx = valid_df["总收益率%"].idxmax()
+        max_total_return_record = symbol_trades_summary_df.loc[max_idx]
+        summary = {}
+        summary["股票代码"] = symbol
+        summary["方向"] = max_total_return_record["方向"]
+        summary["根据SAR"] = max_total_return_record["根据SAR"]
+        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)
+    max_total_return_record_df = pd.DataFrame(max_total_return_record_list)
+    # 统计max_total_return_record_df中方向和根据SAR的组合（使用size更稳健，支持空分组与缺失值）
+    # 强制将分组键转为可哈希的标量类型，避免单元格为Series/列表导致的unhashable错误
+    if len(max_total_return_record_df) > 0:
+
+        def _to_hashable_scalar(v):
+            # 标量或None直接返回
+            if isinstance(v, (str, int, float, bool)) or v is None:
+                return v
+            try:
+                import numpy as _np
+
+                if _np.isscalar(v):
+                    return v
+            except Exception:
+                pass
+            # 其它（如Series、list、dict、ndarray等）转字符串
+            return str(v)
+
+        for key_col in ["方向", "根据SAR"]:
+            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"], dropna=False)
+            .size()
+            .reset_index(name="数量")
+        )
+        max_total_return_record_df_grouped_count.sort_values(
+            by="数量", ascending=False, inplace=True
+        )
+        max_total_return_record_df_grouped_count.reset_index(drop=True, inplace=True)
+
+        # 统计方向的记录数目
+        max_total_return_record_df_direction_count = (
+            max_total_return_record_df.groupby(["方向"], dropna=False)
+            .size()
+            .reset_index(name="数量")
+        )
+        max_total_return_record_df_direction_count.sort_values(
+            by="数量", ascending=False, inplace=True
+        )
+        max_total_return_record_df_direction_count.reset_index(drop=True, inplace=True)
+
+        # 统计根据SAR的记录数目
+        max_total_return_record_df_sar_count = (
+            max_total_return_record_df.groupby(["根据SAR"], dropna=False)
+            .size()
+            .reset_index(name="数量")
+        )
+        max_total_return_record_df_sar_count.sort_values(
+            by="数量", ascending=False, inplace=True
+        )
+        max_total_return_record_df_sar_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", "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"])
+
+    result = {
+        "statistics_summary_df": statistics_summary_df,
+        "max_total_return_record_df": max_total_return_record_df,
+        "max_total_return_record_df_grouped_count": max_total_return_record_df_grouped_count,
+        "max_total_return_record_df_direction_count": max_total_return_record_df_direction_count,
+        "max_total_return_record_df_sar_count": max_total_return_record_df_sar_count,
+    }
+    return result
+
+
+def copy_chart_to_excel(chart_path: str, excel_file_path: str):
+    f"""
+    将chart图片复制到excel中
+    算法：
+    1. 读取chart_path
+    2. chart文件名开头是symbol，结尾是.png
+    3. 每个symbol创建一个Excel Sheet，Sheet名称为symbol_chart
+    4. 将chart图片插入到Sheet中
+    5. 要求每张图片大小为800x400
+    6. 要求两张图片左右并列显示
+    7. 要求上下图片间距为20px
+    """
+    # 收集所有图片
+    if not os.path.isdir(chart_path):
+        return
+    chart_files = [f for f in os.listdir(chart_path) if f.lower().endswith(".png")]
+    if len(chart_files) == 0:
+        return
+
+    # 汇总需要处理的symbol列表（去重）
+    symbols = set(US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["QQQ"]))
+    symbols.update(OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["BTC-USDT"]))
+    symbols = list(symbols)
+    symbols.sort()
+    # 每个symbol创建一个sheet并插图
     for symbol in symbols:
-        logger.info(f"开始回测 {symbol}")
+        logger.info(f"开始保存{symbol}的图表")
-        # 初始化ORB策略
+        symbol_files = [f for f in chart_files if f.startswith(symbol)]
-        orb_strategy = ORBStrategy(
+        if len(symbol_files) == 0:
-            initial_capital=25000,
+            continue
-            max_leverage=4,
+        # 排序以稳定显示顺序
-            risk_per_trade=0.01,
+        symbol_files.sort()
-            commission_per_share=0.0005,
+        copy_chart_to_excel_sheet(chart_path, symbol_files, excel_file_path, symbol)
-            is_us_stock=True,
-        )
-        # 1. 获取QQQ的5分钟日内数据（2024-2025，注意：yfinance免费版可能限制历史日内数据，建议用专业数据源）
-        orb_strategy.fetch_intraday_data(
-            symbol=symbol, start_date="2024-11-30", end_date="2025-08-30", interval="5m"
-        )
 
-        # 2. 生成ORB策略信号
-        orb_strategy.generate_orb_signals()
 
-        # 3. 回测策略（盈利目标10R）
+def copy_chart_to_excel_sheet(
-        orb_strategy.backtest(profit_target_multiple=10)
+    chart_path: str, chart_files: list, excel_file_path: str, symbol: str
+):
+    """
+    将chart图片复制到excel中
+    算法：
+    1. 读取chart_files
+    2. 创建一个Excel Sheet，Sheet名称为{symbol}_chart
+    3. 将chart_files中的图片插入到Sheet中
+    4. 要求每张图片大小为800x400
+    5. 要求两张图片左右并列显示, 如6张图片则图片行数为3，列数为2
+    6. 要求上下图片间距为20px
+    """
+    # 打开已经存在的Excel文件
+    wb = openpyxl.load_workbook(excel_file_path)
+    # 如果sheet已存在，先删除，避免重复插入
+    sheet_name = f"{symbol}_chart"
+    if sheet_name in wb.sheetnames:
+        del wb[sheet_name]
+    ws = wb.create_sheet(title=sheet_name)
 
-        # 4. 绘制净值曲线
+    # 两列布局：左列A，右列L；行间距通过起始行步进控制
-        orb_strategy.plot_equity_curve()
+    left_col = "A"
+    right_col = "L"
+    row_step = 26  # 行步进，控制上下间距
+
+    for idx, chart_file in enumerate(chart_files):
+        try:
+            img_path = os.path.join(chart_path, chart_file)
+            img = Image(img_path)
+            # 设置图片尺寸 800x400 像素
+            img.width = 800
+            img.height = 400
+
+            row_block = idx // 2
+            col_block = idx % 2
+            anchor_col = left_col if col_block == 0 else right_col
+            anchor_cell = f"{anchor_col}{1 + row_block * row_step}"
+            ws.add_image(img, anchor_cell)
+        except Exception:
+            continue
+
+    wb.save(excel_file_path)
+    logger.info(f"{symbol}的图表已输出到{excel_file_path}")
+
+
+def test():
+    orb_strategy = ORBStrategy(
+        symbol="BTC-USDT",
+        bar="5m",
+        start_date="2024-01-01",
+        end_date="2025-09-02",
+        is_us_stock=False,
+        direction=None,
+        by_sar=True,
+        profit_target_multiple=10,
+        initial_capital=25000,
+        max_leverage=4,
+        risk_per_trade=0.01,
+        commission_per_share=0.0005,
+    )
+    orb_strategy.run()
 
 
 if __name__ == "__main__":
-    main()
+    # main()
+
+    chart_path = r"./output/trade_sandbox/orb_strategy/chart/"
+    excel_file_path = r"./output/trade_sandbox/orb_strategy/excel/summary/orb_strategy_summary_20250902174203.xlsx"
+    copy_chart_to_excel(chart_path, excel_file_path)
+    # test()