""" 自动化回测服务 - 任务执行器 调用 vnpy 4.x BacktestingEngine 执行回测 """ import os import sys import tempfile import traceback from datetime import datetime from typing import Optional import matplotlib matplotlib.use("Agg") # 无头模式，服务器上不能弹窗 import matplotlib.pyplot as plt import pandas as pd # vnpy 4.x import路径（与3.x不同） from vnpy.event import EventEngine from vnpy.trader.engine import MainEngine from vnpy_ctastrategy.backtesting import BacktestingEngine from vnpy.trader.constant import Interval, Exchange from .config import settings from .models import BacktestTask, BacktestResult, BacktestStatistics, TaskStatus, BacktestTaskWithId from .result_storage import storage # vnpy 4.x精简了Interval枚举，不再有FIVE_MINUTE等细分 INTERVAL_MAP = { "1m": Interval.MINUTE, "5m": Interval.MINUTE, "15m": Interval.MINUTE, "30m": Interval.MINUTE, "1h": Interval.HOUR, "4h": Interval.HOUR, "1d": Interval.DAILY, "1w": Interval.WEEKLY, } # 交易所映射 EXCHANGE_MAP = { "SSE": Exchange.SSE, "SZSE": Exchange.SZSE, "CFFEX": Exchange.CFFEX, "SHFE": Exchange.SHFE, "DCE": Exchange.DCE, "CZCE": Exchange.CZCE, "INE": Exchange.INE, "GFEX": Exchange.GFEX, } def _parse_vt_symbol(vt_symbol: str): """解析vt_symbol为symbol和exchange，如 '000001.SZ' → ('000001', Exchange.SZSE)""" if "." in vt_symbol: symbol, exchange_str = vt_symbol.rsplit(".", 1) exchange = EXCHANGE_MAP.get(exchange_str.upper()) if exchange is None: # 尝试模糊匹配 exchange_str_upper = exchange_str.upper() for key, val in EXCHANGE_MAP.items(): if key.startswith(exchange_str_upper[:2]): exchange = val break if exchange is None: exchange = Exchange.SZSE # 默认深交所 return symbol, exchange return vt_symbol, Exchange.SZSE class BacktestExecutor: """回测任务执行器 - 适配vnpy 4.x""" def __init__(self): pass def _load_strategy(self, task: BacktestTask): """动态加载策略代码""" strategy_code = task.strategy_code temp_dir = tempfile.mkdtemp() sys.path.insert(0, temp_dir) strategy_file = os.path.join(temp_dir, "strategy.py") with open(strategy_file, "w", encoding="utf-8") as f: f.write(strategy_code) import importlib spec = importlib.util.spec_from_file_location("dynamic_strategy", strategy_file) module = importlib.util.module_from_spec(spec) sys.modules["dynamic_strategy"] = module spec.loader.exec_module(module) # 找CtaTemplate子类 from vnpy_ctastrategy import CtaTemplate strategy_class = None for attr_name in dir(module): attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, CtaTemplate) and attr is not CtaTemplate: strategy_class = attr break if not strategy_class: raise ValueError("策略代码中没有找到 CtaTemplate 子类，请检查策略代码") return strategy_class def execute_backtest(self, task: BacktestTaskWithId) -> BacktestResult: """执行一次回测""" start_time = datetime.now() started_at = start_time.isoformat() task.status = TaskStatus.RUNNING task.started_at = started_at storage.save_task(task) result = BacktestResult( task_id=task.task_id, strategy_name=task.strategy_name, status=TaskStatus.RUNNING, result_csv_path="", created_at=task.created_at, started_at=started_at, ) try: # 加载策略类 strategy_class = self._load_strategy(task) # 解析vt_symbol symbol, exchange = _parse_vt_symbol(task.symbol) # 获取interval interval = INTERVAL_MAP.get(task.interval, Interval.DAILY) # 创建回测引擎 engine = BacktestingEngine() # 设置回测参数 engine.set_parameters( vt_symbol=task.symbol, interval=interval, start=task.start_date, end=task.end_date, rate=0.3 / 10000, # 手续费率万三 slippage=0.1, # 滑点0.1 size=1, # 合约乘数 pricetick=task.tick_size or 0.01, # 最小价格变动 capital=task.capital, ) # 添加策略 engine.add_strategy(strategy_class, task.parameters) # 加载历史数据 # 优先从CSV文件加载（/app/data目录通过volume挂载NAS数据） data_loaded = False data_dir = settings.base_dir.replace("backtest_jobs", "data") # 尝试多种数据加载方式 try: # 方式1: 使用vnpy内置数据加载 engine.load_data() data_loaded = True except Exception: pass if not data_loaded: raise ValueError( f"无法加载 {task.symbol} 在 [{task.start_date}, {task.end_date}] 的历史数据。" f"请确保数据已导入vnpy数据库或可通过CSV加载。" ) # 运行回测 engine.run_backtesting() # 计算统计结果 df = engine.calculate_result() statistics = engine.calculate_statistics() # 转换为数据模型 stats = BacktestStatistics( start_date=str(task.start_date), end_date=str(task.end_date), total_days=int(statistics.get("total_days", 0)), total_trades=int(statistics.get("total_trades", 0)), winning_trades=int(statistics.get("winning_trades", 0)), losing_trades=int(statistics.get("losing_trades", 0)), win_rate=float(statistics.get("win_rate", 0)), total_return=float(statistics.get("total_return", 0)), annual_return=float(statistics.get("annual_return", 0)), sharpe_ratio=float(statistics.get("sharpe_ratio", 0)), max_drawdown=float(statistics.get("max_drawdown", 0)), max_drawdown_start=str(statistics.get("max_drawdown_start", "")), max_drawdown_end=str(statistics.get("max_drawdown_end", "")), profit_factor=float(statistics.get("profit_factor", 0)), calmar_ratio=float(statistics.get("calmar_ratio", 0)), ) # 保存净值CSV result_csv_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "equity.csv") os.makedirs(os.path.dirname(result_csv_path), exist_ok=True) if df is not None and not df.empty: df.to_csv(result_csv_path) # 绘制收益曲线 png_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "equity_curve.png") self._plot_equity_curve(df, png_path) # 保存成交记录 trades_csv_path = None try: trades = engine.get_all_trades() if hasattr(engine, 'get_all_trades') else [] if trades: trades_df = pd.DataFrame([t.__dict__ for t in trades]) trades_csv_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "trades.csv") trades_df.to_csv(trades_csv_path, index=False) except Exception: pass # 完成结果 result.status = TaskStatus.COMPLETED result.statistics = stats result.result_csv_path = result_csv_path result.equity_curve_png_path = png_path result.trades_csv_path = trades_csv_path result.completed_at = datetime.now().isoformat() storage.save_result(result) return result except Exception as e: error_msg = f"{str(e)}\n{traceback.format_exc()}" result.status = TaskStatus.FAILED result.error_message = error_msg result.completed_at = datetime.now().isoformat() storage.save_result(result) return result def _plot_equity_curve(self, df: pd.DataFrame, output_path: str): """绘制收益曲线""" plt.figure(figsize=(12, 6)) if df is not None and not df.empty: if "equity" in df.columns: plt.plot(df.index, df["equity"], label="净值曲线", linewidth=2) elif "net_pnl" in df.columns: cumulative = df["net_pnl"].cumsum() plt.plot(df.index, cumulative, label="累计收益", linewidth=2) elif "balance" in df.columns: plt.plot(df.index, df["balance"], label="账户余额", linewidth=2) plt.title("回测收益曲线") plt.xlabel("时间") plt.ylabel("净值") plt.grid(True, alpha=0.3) plt.legend() plt.tight_layout() plt.savefig(output_path, dpi=150) plt.close() executor = BacktestExecutor()