404 lines
17 KiB
Python
404 lines
17 KiB
Python
import yfinance as yf
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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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):
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"""
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初始化ORB策略参数
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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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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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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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# 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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# 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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# 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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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(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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# 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):
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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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"""
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logger.info("开始生成ORB策略信号")
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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:
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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:
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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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# 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(signals_df, left_on="date_time", right_on="EntryTime", how="left")
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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 = 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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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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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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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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# 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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# 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,
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"EntryPrice": entry_price,
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"ExitTime": exit_time,
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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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"ExitReason": exit_reason,
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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.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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# 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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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：${account_value:,.2f}")
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logger.info(f"总收益率：{total_return:.2f}%")
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logger.info(f"总交易次数：{len(trades_df)}")
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logger.info(f"胜率：{win_rate:.2f}%")
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if len(trades_df) > 0:
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logger.info(f"平均每笔盈亏：${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 plot_equity_curve(self):
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"""
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绘制账户净值曲线
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"""
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logger.info("开始绘制账户净值曲线")
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if self.equity_curve is None:
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raise ValueError("请先调用backtest进行回测")
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# seaborn风格设置
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sns.set_theme(style="whitegrid")
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# plt.rcParams['font.family'] = "SimHei"
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plt.rcParams["font.sans-serif"] = ["SimHei"] # 也可直接用字体名
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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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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.grid(True, alpha=0.3)
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plt.show()
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# ------------------- 策略示例：回测QQQ的ORB策略（2016-2023） -------------------
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if __name__ == "__main__":
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# 初始化ORB策略
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orb_strategy = ORBStrategy(
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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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)
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# 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）
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orb_strategy.fetch_intraday_data(
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symbol="ETH-USDT", start_date="2025-05-15", end_date="2025-08-20", interval="5m"
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)
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# 2. 生成ORB策略信号
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orb_strategy.generate_orb_signals()
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# 3. 回测策略（盈利目标10R）
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orb_strategy.backtest(profit_target_multiple=10)
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# 4. 绘制净值曲线
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orb_strategy.plot_equity_curve()
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