增加交易策略、交易指标、量化库代码等文件夹
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import matplotlib.pyplot as plt
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import pandas as pd
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# from datetime import datetime
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import gzip
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import numpy as np
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import os
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import io
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from 专享08策略 import 专享08of # 导入您的 MyTrader 类
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class BacktestEngine:
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def __init__(self, trader_class, initial_capital=1000000):
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self.trader = trader_class()
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self.initial_capital = initial_capital
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self.equity_curve = []
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self.positions = {} # {instrument_id: {'long': {'today': 0, 'yesterday': 0}, 'short': {'today': 0, 'yesterday': 0}}}
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self.cash = initial_capital
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self.current_date = None
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def run(self, data, start=0, end=None, start_date=None, end_date=None):
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for i, (_, row) in enumerate(data.iloc[start:end].iterrows()):
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tick = row.to_dict()
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action_day = pd.to_datetime(tick["ActionDay"]).strftime("%Y-%m-%d")
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update_time = pd.to_datetime(tick["UpdateTime"]).strftime("%H:%M:%S")
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created_at = f"{action_day} {update_time}.{tick['UpdateMillisec']:03d}"
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current_date = pd.to_datetime(created_at)
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if start_date is not None and current_date < start_date:
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continue
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if end_date is not None and current_date > end_date:
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break
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tick_date = pd.to_datetime(created_at).date()
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if self.current_date is None or tick_date != self.current_date:
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self.update_positions_day()
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self.current_date = tick_date
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self.trader.Join(tickdata=tick)
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self.update_account(created_at, tick["LastPrice"], tick["InstrumentID"])
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def update_positions_day(self):
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for position in self.positions.values():
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position["long"]["yesterday"] += position["long"]["today"]
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position["long"]["today"] = 0
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position["short"]["yesterday"] += position["short"]["today"]
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position["short"]["today"] = 0
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def update_account(self, datetime, last_price, instrument_id):
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position_value = 0
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for inst, pos in self.positions.items():
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price = (
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last_price
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if inst == instrument_id
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else self.positions[inst]["last_price"]
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)
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long_value = (pos["long"]["today"] + pos["long"]["yesterday"]) * price
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short_value = (pos["short"]["today"] + pos["short"]["yesterday"]) * price
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position_value += long_value - short_value
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current_equity = self.cash + position_value
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self.equity_curve.append((datetime, current_equity))
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def mock_insert_order(
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self, exchange_id, instrument_id, price, volume, direction, offset
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):
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is_buy = direction == b"0"
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is_open = offset == b"0"
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is_close_today = offset == b"3"
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if instrument_id not in self.positions:
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self.positions[instrument_id] = {
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"long": {"today": 0, "yesterday": 0},
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"short": {"today": 0, "yesterday": 0},
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"last_price": price,
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}
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position = self.positions[instrument_id]
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if is_open:
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if is_buy:
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position["long"]["today"] += volume
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self.cash -= price * volume
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else:
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position["short"]["today"] += volume
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self.cash += price * volume
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else: # close
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if is_buy: # buy to close short
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if is_close_today:
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position["short"]["today"] -= volume
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else:
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if position["short"]["yesterday"] >= volume:
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position["short"]["yesterday"] -= volume
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else:
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remaining = volume - position["short"]["yesterday"]
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position["short"]["yesterday"] = 0
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position["short"]["today"] -= remaining
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self.cash -= price * volume
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else: # sell to close long
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if is_close_today:
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position["long"]["today"] -= volume
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else:
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if position["long"]["yesterday"] >= volume:
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position["long"]["yesterday"] -= volume
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else:
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remaining = volume - position["long"]["yesterday"]
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position["long"]["yesterday"] = 0
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position["long"]["today"] -= remaining
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self.cash += price * volume
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position["last_price"] = price
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def calculate_performance(self):
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df = pd.DataFrame(self.equity_curve, columns=["time", "equity"])
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df["time"] = pd.to_datetime(df["time"])
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df.set_index("time", inplace=True)
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if len(df) < 2:
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print("警告：回测数据点不足，无法计算性能指标。")
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return {
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"total_return": 0,
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"sharpe_ratio": 0,
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"max_drawdown": 0,
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"equity_curve": df,
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}
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df["returns"] = df["equity"].pct_change()
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total_return = (df["equity"].iloc[-1] - df["equity"].iloc[0]) / df[
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"equity"
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].iloc[0]
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sharpe_ratio = np.sqrt(len(df)) * df["returns"].mean() / df["returns"].std()
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drawdown = df["equity"] / df["equity"].cummax() - 1
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max_drawdown = drawdown.min()
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return {
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"total_return": total_return,
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"sharpe_ratio": sharpe_ratio,
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"max_drawdown": max_drawdown,
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"equity_curve": df,
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}
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def plot_performance(self):
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performance = self.calculate_performance()
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equity_curve = performance["equity_curve"]
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plt.figure(figsize=(12, 8))
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plt.plot(equity_curve.index, equity_curve["equity"])
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plt.title("Equity Curve")
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plt.xlabel("Time")
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plt.ylabel("Equity")
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plt.grid(True)
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plt.show()
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print(f"Total Return: {performance['total_return']:.6%}")
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print(f"Sharpe Ratio: {performance['sharpe_ratio']:.6f}")
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print(f"Max Drawdown: {performance['max_drawdown']:.6%}")
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# 定义中文表头到英文表头的映射
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header_mapping = {
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"交易日": "TradingDay",
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"合约代码": "InstrumentID",
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"交易所代码": "ExchangeID",
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"合约在交易所的代码": "ExchangeInstID",
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"最新价": "LastPrice",
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"上次结算价": "PreSettlementPrice",
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"昨收盘": "PreClosePrice",
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"昨持仓量": "PreOpenInterest",
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"今开盘": "OpenPrice",
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"最高价": "HighestPrice",
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"最低价": "LowestPrice",
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"数量": "Volume",
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"成交金额": "Turnover",
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"持仓量": "OpenInterest",
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"今收盘": "ClosePrice",
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"本次结算价": "SettlementPrice",
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"涨停板价": "UpperLimitPrice",
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"跌停板价": "LowerLimitPrice",
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"昨虚实度": "PreDelta",
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"今虚实度": "CurrDelta",
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"最后修改时间": "UpdateTime",
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"最后修改毫秒": "UpdateMillisec",
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"申买价一": "BidPrice1",
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"申买量一": "BidVolume1",
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"申卖价一": "AskPrice1",
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"申卖量一": "AskVolume1",
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"申买价二": "BidPrice2",
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"申买量二": "BidVolume2",
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"申卖价二": "AskPrice2",
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"申卖量二": "AskVolume2",
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"申买价三": "BidPrice3",
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"申买量三": "BidVolume3",
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"申卖价三": "AskPrice3",
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"申卖量三": "AskVolume3",
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"申买价四": "BidPrice4",
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"申买量四": "BidVolume4",
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"申卖价四": "AskPrice4",
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"申卖量四": "AskVolume4",
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"申买价五": "BidPrice5",
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"申买量五": "BidVolume5",
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"申卖价五": "AskPrice5",
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"申卖量五": "AskVolume5",
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"当日均价": "AveragePrice",
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"业务日期": "ActionDay",
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}
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def load_and_process_data(folder_path):
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dfs = []
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for filename in os.listdir(folder_path):
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file_path = os.path.join(folder_path, filename)
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try:
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if filename.endswith(".gz"):
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# 处理 GZ 文件
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with gzip.open(file_path, "rt", encoding="gbk") as gz_file:
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csv_data = io.StringIO(gz_file.read())
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df = pd.read_csv(csv_data, parse_dates=["业务日期", "最后修改时间"])
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elif filename.endswith(".csv"):
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# 处理 CSV 文件
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df = pd.read_csv(
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file_path,
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encoding="utf-8",
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parse_dates=["业务日期", "最后修改时间"],
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)
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else:
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# 跳过非 GZ 和非 CSV 文件
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print(f"Skipping {filename}: not a GZ or CSV file")
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continue
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# 重命名列
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df.rename(columns=header_mapping, inplace=True)
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dfs.append(df)
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print(f"Successfully read {filename}")
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except Exception as e:
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print(f"Error reading {filename}: {str(e)}")
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print("Skipping this file.")
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continue
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if dfs:
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data = pd.concat(dfs, ignore_index=True)
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data.sort_values(["ActionDay", "UpdateTime", "UpdateMillisec"], inplace=True)
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data = data.reset_index(drop=True) # 重置索引
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return data
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else:
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print("没有找到可读取的GZ或CSV文件")
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return None
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# 使用示例
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if __name__ == "__main__":
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# 使用示例
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folder_path = "./回测数据" # 替换为您的数据文件夹路径
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# 读取，排序，合并tick数据
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data = load_and_process_data(folder_path)
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print(data)
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if data is not None:
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# 初始化回测引擎,设置策略和初始资金
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backtest = BacktestEngine(专享08of, initial_capital=10000)
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# 替换MyTrader中的insert_order方法
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backtest.trader.insert_order = backtest.mock_insert_order
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# # 运行回测
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# backtest.run(data)
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backtest.run(
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data,
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# start=1000,
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# end=3000,
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# start_date=pd.to_datetime('2023-01-01'),
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# end_date=pd.to_datetime('2023-01-31')
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)
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# 显示回测结果
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backtest.plot_performance()
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