""" VectorBT 回测引擎封装。 统一接口：engine.run(strategy, price_df, factor_df) → BacktestReport """ import numpy as np import pandas as pd import vectorbt as vbt from backtest.base import BaseStrategy from backtest.report import BacktestReport class VectorBTEngine: """ VectorBT 回测引擎。 只做多，不做空。 """ def __init__( self, initial_capital: float = 100_000, commission: float = 0.0003, # 万三 freq: str = "D", ): self.initial_capital = initial_capital self.commission = commission self.freq = freq # ── 单股票回测 ──────────────────────────────────────── def run( self, strategy: BaseStrategy, price_df: pd.DataFrame, factor_df: pd.DataFrame | None = None, ) -> BacktestReport: """ 单股票回测。 参数: strategy: 策略实例 price_df: 价格数据，index=trade_date，必须有 'close' 列 factor_df: 因子数据，index=trade_date。 None 时使用 price_df 作为因子数据源。 返回: BacktestReport """ if factor_df is None: factor_df = price_df # 1. 对齐日期 common_idx = price_df.index.intersection(factor_df.index) if len(common_idx) < 2: return BacktestReport() price_df = price_df.loc[common_idx].sort_index() factor_df = factor_df.loc[common_idx].sort_index() # 2. 合并 close 到 factor_df（策略可能需要） if "close" not in factor_df.columns: factor_df = factor_df.copy() factor_df["close"] = price_df["close"] # 3. 生成信号 raw_signals = strategy.generate_signals(factor_df) # 4. 信号 → VectorBT entries/exits entries, exits = self._signals_to_entries(raw_signals, price_df.index) # 5. 运行回测 close = price_df["close"] pf = vbt.Portfolio.from_signals( close, entries=entries, exits=exits, init_cash=self.initial_capital, fees=self.commission, freq=self.freq, direction="longonly", ) return BacktestReport.from_vbt_result(pf, close) # ── 截面回测（多股票） ────────────────────────────────── def run_cross_section( self, strategy: BaseStrategy, price_universe: dict[str, pd.DataFrame], factor_universe: dict[str, pd.DataFrame] | None = None, rebalance_freq: str = "M", ) -> BacktestReport: """ 截面策略回测（多股票 + 定期调仓）。 对每只股票独立回测，合并权益曲线。 参数: strategy: 策略实例 price_universe: {ts_code: price_df} factor_universe: {ts_code: factor_df} rebalance_freq: 调仓频率 'D'/'W'/'M'，用于合并时对齐 返回: BacktestReport """ if factor_universe is None: factor_universe = price_universe stock_equities = {} stock_reports = {} # 逐股票回测 for ts_code in price_universe: price_df = price_universe[ts_code] if "close" not in price_df.columns or price_df.empty: continue factor_df = factor_universe.get(ts_code, price_df) report = self.run(strategy, price_df, factor_df) if report is not None and len(report.equity_curve) > 0: stock_equities[ts_code] = report.equity_curve stock_reports[ts_code] = report if not stock_equities: return BacktestReport() # 合并：等权分配资金到各股票 return self._merge_equities(stock_equities) # ── 信号转换 ────────────────────────────────────────── @staticmethod def _signals_to_entries( raw_signals: pd.Series, target_index: pd.Index, ) -> tuple[pd.Series, pd.Series]: """ 将策略信号转为 VectorBT entries/exits。 信号格式: 1 → 买入 0 → 平仓 -1 → 继续持有/不操作 entries: True 时开仓 exits: True 时平仓 """ # 对齐到目标 index aligned = pd.Series(-1, index=target_index) common = target_index.intersection(raw_signals.index) aligned.loc[common] = raw_signals.loc[common].values entries = pd.Series(False, index=target_index) exits = pd.Series(False, index=target_index) in_position = False for i in range(len(aligned)): sig = aligned.iloc[i] if not in_position and sig == 1: entries.iloc[i] = True in_position = True elif in_position and sig == 0: exits.iloc[i] = True in_position = False return entries, exits # ── 合并多股票权益 ───────────────────────────────────── def _merge_equities( self, stock_equities: dict[str, pd.Series] ) -> BacktestReport: """等权合并多股票权益曲线，构建组合级报告。""" equity_df = pd.DataFrame(stock_equities) equity_df = equity_df.ffill().fillna(0) # 转为 DatetimeIndex if not isinstance(equity_df.index, pd.DatetimeIndex): equity_df.index = pd.to_datetime(equity_df.index, format="%Y%m%d") n_stocks = len(stock_equities) weight = 1.0 / n_stocks if n_stocks > 0 else 1.0 # 加权组合收益 returns_df = equity_df.pct_change().fillna(0) portfolio_ret = returns_df.mean(axis=1) # 等权 = 逐行平均 # 组合净值 portfolio_equity = self.initial_capital * (1 + portfolio_ret).cumprod() dd = portfolio_equity / portfolio_equity.cummax() - 1 years = max(len(portfolio_ret) / 252, 0.02) total_return = (portfolio_equity.iloc[-1] / portfolio_equity.iloc[0] - 1) * 100 cagr = ((total_return / 100 + 1) ** (1 / years) - 1) * 100 mdd = dd.min() * 100 mean_ret = portfolio_ret.mean() * 252 std_ret = portfolio_ret.std() * np.sqrt(252) sharpe = mean_ret / std_ret if std_ret > 0 else 0 calmar = cagr / abs(mdd) if abs(mdd) > 0 else 0 try: monthly = portfolio_equity.resample("ME").last().pct_change() except Exception: monthly = pd.Series(dtype=float) return BacktestReport( total_return=round(total_return, 2), cagr=round(cagr, 2), max_drawdown=round(mdd, 2), sharpe_ratio=round(sharpe, 2), calmar_ratio=round(calmar, 2), annual_volatility=round(std_ret * 100 if std_ret != 0 else 0, 2), total_trades=0, equity_curve=portfolio_equity, drawdown_curve=dd, monthly_returns=monthly, )