optimize ORB strategy

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": ["5m"],
+        "bars": ["5", "15m", "30m", "1H"],
         "initial_date": "2015-08-31 00:00:00"
     }
 }

core/trade/ma_break_statistics.py

@@ -12,7 +12,7 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
-from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 from core.db.db_market_data import DBMarketData
 from core.db.db_huge_volume_data import DBHugeVolumeData
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
@@ -32,7 +32,7 @@ class MaBreakStatistics:
     之间的涨跌幅
     """
 
-    def __init__(self):
+    def __init__(self, is_us_stock: bool = False):
         mysql_user = MYSQL_CONFIG.get("user", "xch")
         mysql_password = MYSQL_CONFIG.get("password", "")
         if not mysql_password:
@@ -44,9 +44,19 @@ class MaBreakStatistics:
         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.db_huge_volume_data = DBHugeVolumeData(self.db_url)
+        if is_us_stock:
+            self.symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "symbols", ["QQQ"]
+            )
+        else:
             self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "symbols", ["XCH-USDT"]
         )
+        if is_us_stock:
+            self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "bars", ["5m"]
+            )
+        else:
             self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "bars", ["5m", "15m", "30m", "1H"]
         )

core/trade/orb_trade.py

@@ -1,4 +1,5 @@
 import yfinance as yf
+import os
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -22,15 +23,28 @@ class ORBStrategy:
         max_leverage=4,
         risk_per_trade=0.01,
         commission_per_share=0.0005,
+        is_us_stock=False,
     ):
         """
         初始化ORB策略参数
+        ORB策略说明：
+        1. 每天仅1次交易机会，多头或空头,排除十字星：open1 == close1
+        2. 第一根5分钟K线：确定开盘区间（High1, Low1）
+        3. 第二根5分钟K线：根据第一根K线方向生成多空信号，open1<close1为多头，open1>close1为空头
+        entry_price=第二根K线开盘价，stop_price=第一根K线最低价（多头）或第一根K线最高价（空头）
+        4. 多头：跌破止损→止损；突破止盈→止盈
+        5. 空头：突破止损→止损；跌破止盈→止盈
+        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 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）
         """
-        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}")
+        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}"
+        )
         self.initial_capital = initial_capital
         self.max_leverage = max_leverage
         self.risk_per_trade = risk_per_trade
@@ -48,6 +62,9 @@ class ORBStrategy:
 
         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.is_us_stock = is_us_stock
+        self.output_chart_folder = r"./output/trade_sandbox/orb_strategy/chart/"
+        os.makedirs(self.output_chart_folder, exist_ok=True)
 
     def fetch_intraday_data(self, symbol, start_date, end_date, interval="5m"):
         """
@@ -72,14 +89,20 @@ class ORBStrategy:
         data["Low"] = data["low"]
         data["Close"] = data["close"]
         data["Volume"] = data["volume"]
-        # 将data["date_time"]从字符串类型转换为日期
+        if self.is_us_stock:
-        data["date_time"] = pd.to_datetime(data["date_time"])
+            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"].dt.date
+        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"])
 
-        self.data = data[["Date", "date_time", "Open", "High", "Low", "Close", "Volume"]].copy()
+        self.data = data[
+            ["symbol", "bar", "Date", date_time_field, "Open", "High", "Low", "Close", "Volume"]
+        ].copy()
+        self.data.rename(columns={date_time_field: "date_time"}, inplace=True)
         logger.info(f"成功获取{symbol}数据：{len(self.data)}根{interval}K线")
 
     def calculate_shares(self, account_value, entry_price, stop_price):
@@ -90,7 +113,9 @@ class ORBStrategy:
         :param stop_price: 止损价格（多头=第一根K线最低价，空头=第一根K线最高价）
         :return: 整数股数（Shares）
         """
-        logger.info(f"开始计算交易股数：账户价值={account_value}，entry价格={entry_price}，止损价格={stop_price}")
+        logger.info(
+            f"开始计算交易股数：账户价值={account_value}，entry价格={entry_price}，止损价格={stop_price}"
+        )
         # 计算单交易风险金额（$R）
         risk_per_trade_dollar = abs(entry_price - stop_price)  # 风险金额取绝对值
         if risk_per_trade_dollar <= 0:
@@ -149,7 +174,7 @@ class ORBStrategy:
                 stop_price = high1  # 空头止损=第一根K线最高价
             else:
                 # 十字星→无信号
-                signal = "None"
+                signal = None
                 stop_price = None
 
             signals.append(
@@ -167,10 +192,12 @@ class ORBStrategy:
         # 将信号合并到原始数据
         signals_df = pd.DataFrame(signals)
         # 确保Date列类型一致，将Date转换为datetime64[ns]类型
-        signals_df['Date'] = pd.to_datetime(signals_df['Date'])
+        signals_df["Date"] = pd.to_datetime(signals_df["Date"])
         # 使用merge而不是join来合并数据,根据signals_df的EntryTime与self.data的date_time进行匹配
         # TODO: 这里需要优化
-        self.data = self.data.merge(signals_df, left_on="date_time", right_on="EntryTime", how="left")
+        self.data = self.data.merge(
+            signals_df, left_on="date_time", right_on="EntryTime", how="left"
+        )
         # 将Date_x和Date_y合并为Date
         self.data["Date"] = self.data["Date_x"].combine_first(self.data["Date_y"])
         # 删除Date_x和Date_y
@@ -214,6 +241,7 @@ class ORBStrategy:
             signal = signal_row["Signal"]
             if pd.isna(signal):
                 continue
+
             entry_price = signal_row["EntryPrice"]
             stop_price = signal_row["StopPrice"]
             high1 = signal_row["High1"]
@@ -254,24 +282,24 @@ class ORBStrategy:
                     if low <= stop_price:
                         exit_price = stop_price
                         exit_reason = "Stop Loss"
-                        exit_time = time
+                        exit_time = row["date_time"]
                         break
                     elif high >= profit_target:
                         exit_price = profit_target
                         exit_reason = "Profit Target (10R)"
-                        exit_time = time
+                        exit_time = row["date_time"]
                         break
                 elif signal == "Short":
                     # 空头：突破止损→止损；跌破止盈→止盈
                     if high >= stop_price:
                         exit_price = stop_price
                         exit_reason = "Stop Loss"
-                        exit_time = time
+                        exit_time = row["date_time"]
                         break
                     elif low <= profit_target:
                         exit_price = profit_target
                         exit_reason = "Profit Target (10R)"
-                        exit_time = time
+                        exit_time = row["date_time"]
                         break
 
             # 若未触发止损/止盈，当日收盘平仓
@@ -280,12 +308,16 @@ class ORBStrategy:
                 exit_reason = "End of Day (EoD)"
                 exit_time = daily_prices.iloc[-1].date_time
 
+            initial_account_value = account_value
             # 计算盈亏
             if signal == "Long":
                 profit_loss = (exit_price - entry_price) * shares - total_commission
             else:  # Short
                 profit_loss = (entry_price - exit_price) * shares - total_commission
 
+            # 计算盈亏百分比，profit_loss除以当期初始资金
+            profit_loss_percentage = (profit_loss / initial_account_value) * 100
+
             # 更新账户价值
             account_value += profit_loss
             account_value = max(account_value, 0)  # 账户价值不能为负
@@ -296,14 +328,16 @@ class ORBStrategy:
                     "TradeID": trade_id,
                     "Date": date,
                     "Signal": signal,
-                    "EntryTime": signal_row.date_time,
+                    "EntryTime": signal_row.date_time.strftime("%Y-%m-%d %H:%M:%S"),
                     "EntryPrice": entry_price,
-                    "ExitTime": exit_time,
+                    "ExitTime": exit_time.strftime("%Y-%m-%d %H:%M:%S"),
                     "ExitPrice": exit_price,
                     "Shares": shares,
                     "RiskAssumed": risk_assumed,
                     "ProfitLoss": profit_loss,
+                    "ProfitLossPercentage": profit_loss_percentage,
                     "ExitReason": exit_reason,
+                    "AccountValueInitial": initial_account_value,
                     "AccountValueAfter": account_value,
                 }
             )
@@ -313,12 +347,7 @@ class ORBStrategy:
             trade_id += 1
 
         # 生成净值曲线
-        self.equity_curve = pd.Series(
+        self.create_equity_curve()
-            equity_history,
-            index=pd.date_range(
-                start=self.data.index[0].date(), periods=len(equity_history), freq="D"
-            ),
-        )
 
         # 输出回测结果
         trades_df = pd.DataFrame(self.trades)
@@ -330,6 +359,13 @@ class ORBStrategy:
             if len(trades_df) > 0
             else 0
         )
+        # 计算盈亏比
+        profit_sum = trades_df[trades_df["ProfitLoss"] > 0]["ProfitLoss"].sum()
+        loss_sum = abs(trades_df[trades_df["ProfitLoss"] < 0]["ProfitLoss"].sum())
+        if loss_sum == 0:
+            profit_loss_ratio = float('inf')
+        else:
+            profit_loss_ratio = (profit_sum / loss_sum) * 100
 
         logger.info("\n" + "=" * 50)
         logger.info("ORB策略回测结果")
@@ -338,12 +374,40 @@ class ORBStrategy:
         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):
+        """
+        创建账户净值曲线
+        """
+        equity_curve_list = []
+        # 将self.data.index[0].Date的值转换为字符串，且格式为YYYY-MM-DD
+        first_date = self.data.iloc[0].date_time.strftime("%Y-%m-%d %H:%M:%S")
+        first_open = float(self.data.iloc[0].Open)
+        equity_curve_list.append(
+            {
+                "DateTime": first_date,
+                "AccountValue": self.initial_capital,
+                "MarketPrice": first_open,
+            }
+        )
+        for trade in self.trades:
+            equity_curve_list.append(
+                {
+                    "DateTime": trade["ExitTime"],
+                    "AccountValue": trade["AccountValueAfter"],
+                    "MarketPrice": trade["ExitPrice"],
+                }
+            )
+        self.equity_curve = pd.DataFrame(equity_curve_list)
+        self.equity_curve.sort_values(by="DateTime", inplace=True)
+        self.equity_curve.reset_index(drop=True, inplace=True)
+
     def plot_equity_curve(self):
         """
         绘制账户净值曲线
@@ -359,22 +423,51 @@ class ORBStrategy:
         plt.rcParams["font.size"] = 11  # 设置字体大小
         plt.rcParams["axes.unicode_minus"] = False  # 解决负号显示问题
 
+        symbol = self.data.iloc[0].symbol
+        bar = self.data.iloc[0].bar
+        first_account_value = self.equity_curve.iloc[0]["AccountValue"]
+        first_market_price = self.equity_curve.iloc[0]["MarketPrice"]
+        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(
+        plt.plot(self.equity_curve["DateTime"], account_value_to_1, label="账户价值", color='blue', linewidth=2, marker='o', markersize=4)
-            self.equity_curve.index,
+        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.values,
+        plt.title(f"ORB策略账户净值曲线 {symbol} {bar}", fontsize=14, fontweight='bold')
-            label="ORB策略净值",
+        plt.xlabel("时间", fontsize=12)
-            color="blue",
+        plt.ylabel("涨跌变化", fontsize=12)
-        )
+        plt.legend(fontsize=11)
-        plt.axhline(
-            y=self.initial_capital, color="red", linestyle="--", label="初始资金"
-        )
-        plt.title("ORB策略账户净值曲线", fontsize=14)
-        plt.xlabel("日期", fontsize=12)
-        plt.ylabel("账户价值（美元）", fontsize=12)
-        plt.legend()
         plt.grid(True, alpha=0.3)
-        plt.show()
+
+        # 设置x轴标签，避免matplotlib警告
+        # 选择合适的时间间隔显示标签，避免过于密集
+        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], 
+                        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"], 
+                        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"
+        plt.savefig(save_path, dpi=150, bbox_inches='tight')
+        plt.close()
 
 
 # ------------------- 策略示例：回测QQQ的ORB策略（2016-2023） -------------------

market_data_main.py

@@ -109,14 +109,16 @@ class MarketDataMain:
         # 如果bar为1D, 则end_time_ts与start_time_ts相差超过10天，则按照10天为单位
         # 获取数据，直到end_time_ts
         threshold = None
-        if bar in ["5m", "15m", "30m"]:
+        if bar in ["5m", "15m", "30m", "1H"]:
             if self.is_us_stock:
                 if bar == "5m":
                     threshold = 86400000 * 4
                 elif bar == "15m":
-                    threshold = 86400000 * 4 * 3
+                    threshold = 86400000 * 6
                 elif bar == "30m":
-                    threshold = 86400000 * 4 * 6
+                    threshold = 86400000 * 12
+                elif bar == "1H":
+                    threshold = 86400000 * 24
             else:
                 threshold = 86400000
         elif bar in ["1H", "4H"]:

orb_trade_main.py

@@ -1,17 +1,24 @@
 from core.trade.orb_trade import ORBStrategy
+from config import US_STOCK_MONITOR_CONFIG
+import core.logger as logging
+
+logger = logging.logger
 
 def main():
+    symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["QQQ"])
+    for symbol in symbols:
+        logger.info(f"开始回测 {symbol}")
         # 初始化ORB策略
         orb_strategy = ORBStrategy(
             initial_capital=25000,
             max_leverage=4,
             risk_per_trade=0.01,
             commission_per_share=0.0005,
+            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="ETH-USDT", start_date="2025-05-15", end_date="2025-08-20", interval="5m"
+            symbol=symbol, start_date="2024-11-30", end_date="2025-08-30", interval="5m"
         )
 
         # 2. 生成ORB策略信号

trade_ma_strategy_main.py

@@ -25,8 +25,8 @@ from config import (
 logger = logging.logger
 
 class TradeMaStrategyMain:
-    def __init__(self):
+    def __init__(self, is_us_stock: bool = False):
-        self.ma_break_statistics = MaBreakStatistics()
+        self.ma_break_statistics = MaBreakStatistics(is_us_stock=is_us_stock)
     
     def batch_ma_break_statistics(self):
         """
@@ -59,5 +59,5 @@ class TradeMaStrategyMain:
 
 
 if __name__ == "__main__":
-    trade_ma_strategy_main = TradeMaStrategyMain()
+    trade_ma_strategy_main = TradeMaStrategyMain(is_us_stock=True)
     trade_ma_strategy_main.batch_ma_break_statistics()