optimize ORB strategy
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parent
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@ -73,7 +73,7 @@ OKX_MONITOR_CONFIG = {
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US_STOCK_MONITOR_CONFIG = {
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"volume_monitor":{
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"symbols": ["QQQ", "TQQQ", "MSFT", "AAPL", "GOOG", "NVDA", "META", "AMZN", "TSLA", "AVGO"],
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"bars": ["5m"],
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"bars": ["5", "15m", "30m", "1H"],
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"initial_date": "2015-08-31 00:00:00"
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}
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}
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@ -12,7 +12,7 @@ from openpyxl.drawing.image import Image
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import openpyxl
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from openpyxl.styles import Font
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from PIL import Image as PILImage
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from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
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from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
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from core.db.db_market_data import DBMarketData
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from core.db.db_huge_volume_data import DBHugeVolumeData
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from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
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@ -32,7 +32,7 @@ class MaBreakStatistics:
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之间的涨跌幅
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"""
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def __init__(self):
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def __init__(self, is_us_stock: bool = False):
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mysql_user = MYSQL_CONFIG.get("user", "xch")
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mysql_password = MYSQL_CONFIG.get("password", "")
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if not mysql_password:
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@ -44,10 +44,20 @@ class MaBreakStatistics:
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self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
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self.db_market_data = DBMarketData(self.db_url)
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self.db_huge_volume_data = DBHugeVolumeData(self.db_url)
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self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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if is_us_stock:
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self.symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["QQQ"]
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)
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else:
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self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["XCH-USDT"]
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)
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self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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if is_us_stock:
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self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"bars", ["5m"]
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)
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else:
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self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"bars", ["5m", "15m", "30m", "1H"]
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)
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self.stats_output_dir = "./output/trade_sandbox/ma_strategy/excel/"
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@ -1,4 +1,5 @@
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import yfinance as yf
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import os
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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@ -22,15 +23,28 @@ class ORBStrategy:
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max_leverage=4,
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risk_per_trade=0.01,
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commission_per_share=0.0005,
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is_us_stock=False,
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):
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"""
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初始化ORB策略参数
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ORB策略说明：
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1. 每天仅1次交易机会，多头或空头,排除十字星：open1 == close1
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2. 第一根5分钟K线：确定开盘区间（High1, Low1）
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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
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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）
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4. 多头：跌破止损→止损；突破止盈→止盈
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5. 空头：突破止损→止损；跌破止盈→止盈
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6. 止损/止盈：根据$R计算，$R=|entry_price-stop_price|
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7. 盈利目标：10R，即10*$R
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8. 账户净值曲线：账户价值与市场价格
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:param initial_capital: 初始账户资金（美元）
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:param max_leverage: 最大杠杆倍数（默认4倍，符合FINRA规定）
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:param risk_per_trade: 单次交易风险比例（默认1%）
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:param commission_per_share: 每股交易佣金（美元，默认0.0005）
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"""
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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}")
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logger.info(
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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}"
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)
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self.initial_capital = initial_capital
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self.max_leverage = max_leverage
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self.risk_per_trade = risk_per_trade
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@ -48,6 +62,9 @@ class ORBStrategy:
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self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
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self.db_market_data = DBMarketData(self.db_url)
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self.is_us_stock = is_us_stock
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self.output_chart_folder = r"./output/trade_sandbox/orb_strategy/chart/"
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os.makedirs(self.output_chart_folder, exist_ok=True)
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def fetch_intraday_data(self, symbol, start_date, end_date, interval="5m"):
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"""
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@ -72,14 +89,20 @@ class ORBStrategy:
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data["Low"] = data["low"]
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data["Close"] = data["close"]
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data["Volume"] = data["volume"]
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# 将data["date_time"]从字符串类型转换为日期
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data["date_time"] = pd.to_datetime(data["date_time"])
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if self.is_us_stock:
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date_time_field = "date_time_us"
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else:
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date_time_field = "date_time"
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data[date_time_field] = pd.to_datetime(data[date_time_field])
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# 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
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data["Date"] = data["date_time"].dt.date
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data["Date"] = data[date_time_field].dt.date
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# 将Date转换为datetime64[ns]类型以确保类型一致
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data["Date"] = pd.to_datetime(data["Date"])
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self.data = data[["Date", "date_time", "Open", "High", "Low", "Close", "Volume"]].copy()
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self.data = data[
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["symbol", "bar", "Date", date_time_field, "Open", "High", "Low", "Close", "Volume"]
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].copy()
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self.data.rename(columns={date_time_field: "date_time"}, inplace=True)
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logger.info(f"成功获取{symbol}数据：{len(self.data)}根{interval}K线")
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def calculate_shares(self, account_value, entry_price, stop_price):
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@ -90,7 +113,9 @@ class ORBStrategy:
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: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）
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:return: 整数股数（Shares）
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"""
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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}")
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logger.info(
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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}"
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)
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# 计算单交易风险金额（$R）
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risk_per_trade_dollar = abs(entry_price - stop_price) # 风险金额取绝对值
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if risk_per_trade_dollar <= 0:
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@ -149,7 +174,7 @@ class ORBStrategy:
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stop_price = high1 # 空头止损=第一根K线最高价
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else:
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# 十字星→无信号
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signal = "None"
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signal = None
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stop_price = None
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signals.append(
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@ -167,10 +192,12 @@ class ORBStrategy:
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# 将信号合并到原始数据
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signals_df = pd.DataFrame(signals)
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# 确保Date列类型一致，将Date转换为datetime64[ns]类型
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signals_df['Date'] = pd.to_datetime(signals_df['Date'])
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signals_df["Date"] = pd.to_datetime(signals_df["Date"])
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# 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
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# TODO: 这里需要优化
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self.data = self.data.merge(signals_df, left_on="date_time", right_on="EntryTime", how="left")
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self.data = self.data.merge(
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signals_df, left_on="date_time", right_on="EntryTime", how="left"
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)
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# 将Date_x和Date_y合并为Date
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self.data["Date"] = self.data["Date_x"].combine_first(self.data["Date_y"])
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# 删除Date_x和Date_y
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@ -214,6 +241,7 @@ class ORBStrategy:
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signal = signal_row["Signal"]
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if pd.isna(signal):
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continue
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entry_price = signal_row["EntryPrice"]
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stop_price = signal_row["StopPrice"]
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high1 = signal_row["High1"]
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@ -254,24 +282,24 @@ class ORBStrategy:
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if low <= stop_price:
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exit_price = stop_price
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exit_reason = "Stop Loss"
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exit_time = time
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exit_time = row["date_time"]
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break
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elif high >= profit_target:
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exit_price = profit_target
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exit_reason = "Profit Target (10R)"
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exit_time = time
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exit_time = row["date_time"]
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break
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elif signal == "Short":
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# 空头：突破止损→止损；跌破止盈→止盈
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if high >= stop_price:
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exit_price = stop_price
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exit_reason = "Stop Loss"
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exit_time = time
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exit_time = row["date_time"]
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break
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elif low <= profit_target:
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exit_price = profit_target
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exit_reason = "Profit Target (10R)"
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exit_time = time
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exit_time = row["date_time"]
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break
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# 若未触发止损/止盈，当日收盘平仓
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@ -280,12 +308,16 @@ class ORBStrategy:
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exit_reason = "End of Day (EoD)"
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exit_time = daily_prices.iloc[-1].date_time
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initial_account_value = account_value
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# 计算盈亏
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if signal == "Long":
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profit_loss = (exit_price - entry_price) * shares - total_commission
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else: # Short
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profit_loss = (entry_price - exit_price) * shares - total_commission
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# 计算盈亏百分比，profit_loss除以当期初始资金
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profit_loss_percentage = (profit_loss / initial_account_value) * 100
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# 更新账户价值
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account_value += profit_loss
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account_value = max(account_value, 0) # 账户价值不能为负
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@ -296,14 +328,16 @@ class ORBStrategy:
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"TradeID": trade_id,
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"Date": date,
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"Signal": signal,
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"EntryTime": signal_row.date_time,
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"EntryTime": signal_row.date_time.strftime("%Y-%m-%d %H:%M:%S"),
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"EntryPrice": entry_price,
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"ExitTime": exit_time,
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"ExitTime": exit_time.strftime("%Y-%m-%d %H:%M:%S"),
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"ExitPrice": exit_price,
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"Shares": shares,
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"RiskAssumed": risk_assumed,
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"ProfitLoss": profit_loss,
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"ProfitLossPercentage": profit_loss_percentage,
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"ExitReason": exit_reason,
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"AccountValueInitial": initial_account_value,
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"AccountValueAfter": account_value,
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}
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)
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@ -313,12 +347,7 @@ class ORBStrategy:
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trade_id += 1
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# 生成净值曲线
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self.equity_curve = pd.Series(
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equity_history,
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index=pd.date_range(
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start=self.data.index[0].date(), periods=len(equity_history), freq="D"
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),
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)
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self.create_equity_curve()
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# 输出回测结果
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trades_df = pd.DataFrame(self.trades)
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@ -330,6 +359,13 @@ class ORBStrategy:
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if len(trades_df) > 0
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else 0
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)
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# 计算盈亏比
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profit_sum = trades_df[trades_df["ProfitLoss"] > 0]["ProfitLoss"].sum()
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loss_sum = abs(trades_df[trades_df["ProfitLoss"] < 0]["ProfitLoss"].sum())
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if loss_sum == 0:
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profit_loss_ratio = float('inf')
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else:
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profit_loss_ratio = (profit_sum / loss_sum) * 100
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logger.info("\n" + "=" * 50)
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logger.info("ORB策略回测结果")
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@ -338,12 +374,40 @@ class ORBStrategy:
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logger.info(f"最终资金：${account_value:,.2f}")
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logger.info(f"总收益率：{total_return:.2f}%")
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logger.info(f"总交易次数：{len(trades_df)}")
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logger.info(f"盈亏比：{profit_loss_ratio:.2f}%")
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logger.info(f"胜率：{win_rate:.2f}%")
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if len(trades_df) > 0:
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logger.info(f"平均每笔盈亏：${trades_df['ProfitLoss'].mean():.2f}")
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logger.info(f"最大单笔盈利：${trades_df['ProfitLoss'].max():.2f}")
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logger.info(f"最大单笔亏损：${trades_df['ProfitLoss'].min():.2f}")
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def create_equity_curve(self):
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"""
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创建账户净值曲线
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"""
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equity_curve_list = []
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# 将self.data.index[0].Date的值转换为字符串，且格式为YYYY-MM-DD
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first_date = self.data.iloc[0].date_time.strftime("%Y-%m-%d %H:%M:%S")
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first_open = float(self.data.iloc[0].Open)
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equity_curve_list.append(
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{
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"DateTime": first_date,
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"AccountValue": self.initial_capital,
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"MarketPrice": first_open,
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}
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)
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for trade in self.trades:
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equity_curve_list.append(
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{
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"DateTime": trade["ExitTime"],
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"AccountValue": trade["AccountValueAfter"],
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"MarketPrice": trade["ExitPrice"],
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}
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)
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self.equity_curve = pd.DataFrame(equity_curve_list)
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self.equity_curve.sort_values(by="DateTime", inplace=True)
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self.equity_curve.reset_index(drop=True, inplace=True)
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def plot_equity_curve(self):
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"""
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绘制账户净值曲线
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@ -359,22 +423,51 @@ class ORBStrategy:
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plt.rcParams["font.size"] = 11 # 设置字体大小
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plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题
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symbol = self.data.iloc[0].symbol
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bar = self.data.iloc[0].bar
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first_account_value = self.equity_curve.iloc[0]["AccountValue"]
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first_market_price = self.equity_curve.iloc[0]["MarketPrice"]
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account_value_to_1 = self.equity_curve["AccountValue"] / first_account_value
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market_price_to_1 = self.equity_curve["MarketPrice"] / first_market_price
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plt.figure(figsize=(12, 6))
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plt.plot(
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self.equity_curve.index,
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self.equity_curve.values,
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label="ORB策略净值",
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color="blue",
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)
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plt.axhline(
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y=self.initial_capital, color="red", linestyle="--", label="初始资金"
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)
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plt.title("ORB策略账户净值曲线", fontsize=14)
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plt.xlabel("日期", fontsize=12)
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plt.ylabel("账户价值（美元）", fontsize=12)
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plt.legend()
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plt.plot(self.equity_curve["DateTime"], account_value_to_1, label="账户价值", color='blue', linewidth=2, marker='o', markersize=4)
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plt.plot(self.equity_curve["DateTime"], market_price_to_1, label="市场价格", color='green', linewidth=2, marker='s', markersize=4)
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plt.title(f"ORB策略账户净值曲线 {symbol} {bar}", fontsize=14, fontweight='bold')
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plt.xlabel("时间", fontsize=12)
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plt.ylabel("涨跌变化", fontsize=12)
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plt.legend(fontsize=11)
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plt.grid(True, alpha=0.3)
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plt.show()
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# 设置x轴标签，避免matplotlib警告
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# 选择合适的时间间隔显示标签，避免过于密集
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if len(self.equity_curve) > 30:
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# 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
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step = max(1, len(self.equity_curve) // 30)
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# 创建标签索引列表，确保包含首尾数据
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label_indices = [0] # 第一条
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# 添加中间间隔的标签
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for i in range(step, len(self.equity_curve) - 1, step):
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label_indices.append(i)
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# 添加最后一条（如果还没有包含的话）
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if len(self.equity_curve) - 1 not in label_indices:
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label_indices.append(len(self.equity_curve) - 1)
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# 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） -------------------
|
||||
|
|
|
|||
|
|
@ -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"]:
|
||||
|
|
|
|||
|
|
@ -1,27 +1,34 @@
|
|||
from core.trade.orb_trade import ORBStrategy
|
||||
from config import US_STOCK_MONITOR_CONFIG
|
||||
import core.logger as logging
|
||||
|
||||
logger = logging.logger
|
||||
|
||||
def main():
|
||||
# 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,
|
||||
)
|
||||
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=symbol, start_date="2024-11-30", end_date="2025-08-30", interval="5m"
|
||||
)
|
||||
|
||||
# 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"
|
||||
)
|
||||
# 2. 生成ORB策略信号
|
||||
orb_strategy.generate_orb_signals()
|
||||
|
||||
# 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)
|
||||
|
||||
# 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()
|
||||
# 4. 绘制净值曲线
|
||||
orb_strategy.plot_equity_curve()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -25,8 +25,8 @@ from config import (
|
|||
logger = logging.logger
|
||||
|
||||
class TradeMaStrategyMain:
|
||||
def __init__(self):
|
||||
self.ma_break_statistics = MaBreakStatistics()
|
||||
def __init__(self, is_us_stock: bool = False):
|
||||
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()
|
||||
Loading…
Reference in New Issue