504 lines
22 KiB
Python
504 lines
22 KiB
Python
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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import seaborn as sns
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import core.logger as logging
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from config import OKX_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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# seaborn支持中文
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plt.rcParams["font.family"] = ["SimHei"]
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logger = logging.logger
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class ORBStrategy:
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def __init__(
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self,
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initial_capital=25000,
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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(
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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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self.commission_per_share = commission_per_share
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self.data = None # 存储K线数据
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self.trades = [] # 存储交易记录
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self.equity_curve = None # 存储账户净值曲线
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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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raise ValueError("MySQL password is not set")
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mysql_host = MYSQL_CONFIG.get("host", "localhost")
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mysql_port = MYSQL_CONFIG.get("port", 3306)
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mysql_database = MYSQL_CONFIG.get("database", "okx")
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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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获取日内5分钟K线数据（需yfinance支持，部分数据可能有延迟）
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:param ticker: 股票代码（如QQQ、TQQQ）
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:param start_date: 起始日期（格式：YYYY-MM-DD）
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:param end_date: 结束日期（格式：YYYY-MM-DD）
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:param interval: K线周期（默认5分钟）
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"""
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logger.info(f"开始获取{symbol}数据：{start_date}至{end_date}，间隔{interval}")
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# data = yf.download(
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# symbol, start=start_date, end=end_date, interval=interval, progress=False
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# )
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data = self.db_market_data.query_market_data_by_symbol_bar(
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symbol, interval, start=start_date, end=end_date
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)
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data = pd.DataFrame(data)
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data.sort_values(by="date_time", inplace=True)
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# 保留核心列：开盘价、最高价、最低价、收盘价、成交量
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data["Open"] = data["open"]
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data["High"] = data["high"]
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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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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_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[
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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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"""
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根据ORB公式计算交易股数
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:param account_value: 当前账户价值（美元）
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:param entry_price: 交易entry价格（第二根5分钟K线开盘价）
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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(
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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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return 0 # 无风险时不交易
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# 公式1：基于风险预算的最大股数（风险控制优先）
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shares_risk = (account_value * self.risk_per_trade) / risk_per_trade_dollar
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# 公式2：基于杠杆限制的最大股数（杠杆约束）
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shares_leverage = (self.max_leverage * account_value) / entry_price
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# 取两者最小值（满足风险和杠杆双重约束）
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max_shares = min(shares_risk, shares_leverage)
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# 扣除佣金影响（简化计算：假设佣金从可用资金中扣除）
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commission_cost = max_shares * self.commission_per_share
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if (account_value - commission_cost) < 0:
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return 0 # 扣除佣金后资金不足，不交易
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return int(max_shares) # 股数取整
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def generate_orb_signals(self, direction: str = None, by_sar: bool = False):
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"""
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生成ORB策略信号（每日仅1次交易机会）
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- 第一根5分钟K线：确定开盘区间（High1, Low1）
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- 第二根5分钟K线：根据第一根K线方向生成多空信号
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:param direction: 方向，None=自动，Long=多头，Short=空头
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:param by_sar: 是否根据SAR指标生成信号，True=是，False=否
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"""
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direction_desc = "既做多又做空"
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if direction == "Long":
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direction_desc = "做多"
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elif direction == "Short":
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direction_desc = "做空"
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logger.info(f"开始生成ORB策略信号：{direction_desc}，根据SAR指标：{by_sar}")
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if self.data is None:
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raise ValueError("请先调用fetch_intraday_data获取数据")
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signals = []
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# 按日期分组处理每日数据
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for date, daily_data in self.data.groupby("Date"):
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daily_data = daily_data.sort_index() # 按时间排序
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if len(daily_data) < 2:
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continue # 当日K线不足2根，跳过
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# 第一根5分钟K线（开盘区间）
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first_candle = daily_data.iloc[0]
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high1 = first_candle["High"]
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low1 = first_candle["Low"]
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open1 = first_candle["Open"]
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close1 = first_candle["Close"]
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# 第二根5分钟K线（entry信号）
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second_candle = daily_data.iloc[1]
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entry_price = second_candle["Open"] # entry价格=第二根K线开盘价
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entry_time = second_candle.date_time # entry时间
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# 生成信号：第一根K线方向决定多空（排除十字星：open1 == close1）
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if open1 < close1 and (direction == "Long" or direction is None):
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# 第一根K线收涨→多头信号
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signal = "Long"
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stop_price = low1 # 多头止损=第一根K线最低价
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elif open1 > close1 and (direction == "Short" or direction is None):
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# 第一根K线收跌→空头信号
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signal = "Short"
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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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# 与direction不一致或十字星→无信号
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signal = None
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stop_price = None
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signals.append(
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{
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"Date": date,
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"EntryTime": entry_time,
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"Signal": signal,
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"EntryPrice": entry_price,
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"StopPrice": stop_price,
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"High1": high1,
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"Low1": low1,
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}
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)
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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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# 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(
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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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self.data.drop(columns=["Date_x", "Date_y"], inplace=True)
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logger.info(
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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"
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)
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def backtest(self, profit_target_multiple=10):
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"""
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回测ORB策略
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:param profit_target_multiple: 盈利目标倍数（默认10倍$R，即10R）
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"""
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logger.info(f"开始回测ORB策略：盈利目标倍数={profit_target_multiple}")
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if "Signal" not in self.data.columns:
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raise ValueError("请先调用generate_orb_signals生成策略信号")
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account_value = self.initial_capital # 初始账户价值
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current_position = None # 当前持仓（None=空仓，Long/Short=持仓）
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equity_history = [account_value] # 净值历史
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trade_id = 0 # 交易ID
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# 按时间遍历数据（每日仅处理第二根K线后的信号）
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for date, daily_data in self.data.groupby("Date"):
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daily_data = daily_data.sort_index()
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if len(daily_data) < 2:
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continue
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# 获取当日信号（第二根K线的信号）
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signal_row = (
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daily_data[~pd.isna(daily_data["Signal"])].iloc[0]
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if sum(~pd.isna(daily_data["Signal"])) > 0
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else None
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)
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if signal_row is None:
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# 无信号→当日不交易，净值保持不变
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equity_history.append(account_value)
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continue
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# 提取信号参数
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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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low1 = signal_row["Low1"]
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risk_assumed = abs(entry_price - stop_price) # 计算$R
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profit_target = (
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entry_price + (risk_assumed * profit_target_multiple)
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if signal == "Long"
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else entry_price - (risk_assumed * profit_target_multiple)
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)
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# 计算交易股数
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shares = self.calculate_shares(account_value, entry_price, stop_price)
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if shares == 0:
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# 股数为0→不交易
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equity_history.append(account_value)
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continue
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# 计算佣金（买入/卖出各收一次）
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total_commission = shares * self.commission_per_share * 2 # 往返佣金
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# 模拟日内持仓：寻找止损/止盈触发点，或当日收盘平仓
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daily_prices = daily_data[
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daily_data.date_time > signal_row.date_time
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] # 从entry时间开始遍历
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exit_price = None
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exit_time = None
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exit_reason = None
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for idx, (time, row) in enumerate(daily_prices.iterrows()):
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high = row["High"]
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low = row["Low"]
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close = row["Close"]
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# 检查止损/止盈条件
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if signal == "Long":
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# 多头：跌破止损→止损；突破止盈→止盈
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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 = 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 = 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 = 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 = 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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if exit_price is None:
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exit_price = daily_prices.iloc[-1]["Close"]
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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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# 记录交易
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self.trades.append(
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{
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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.strftime("%Y-%m-%d %H:%M:%S"),
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"EntryPrice": entry_price,
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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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# 记录净值
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equity_history.append(account_value)
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trade_id += 1
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# 生成净值曲线
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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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total_return = (
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(account_value - self.initial_capital) / self.initial_capital * 100
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)
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win_rate = (
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(trades_df["ProfitLoss"] > 0).sum() / len(trades_df) * 100
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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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logger.info("=" * 50)
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logger.info(f"初始资金：${self.initial_capital:,.2f}")
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logger.info(f"最终资金：${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:
|
||
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
|
||
"""
|
||
logger.info("开始绘制账户净值曲线")
|
||
if self.equity_curve is None:
|
||
raise ValueError("请先调用backtest进行回测")
|
||
|
||
# seaborn风格设置
|
||
sns.set_theme(style="whitegrid")
|
||
# plt.rcParams['font.family'] = "SimHei"
|
||
plt.rcParams["font.sans-serif"] = ["SimHei"] # 也可直接用字体名
|
||
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(self.equity_curve["DateTime"], 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策略账户净值曲线 {symbol} {bar}", fontsize=14, fontweight='bold')
|
||
plt.xlabel("时间", fontsize=12)
|
||
plt.ylabel("涨跌变化", fontsize=12)
|
||
plt.legend(fontsize=11)
|
||
plt.grid(True, alpha=0.3)
|
||
|
||
# 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） -------------------
|
||
if __name__ == "__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,
|
||
)
|
||
|
||
# 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()
|
||
|
||
# 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()
|