Initial commit: cc-cursor 全链路量化研究平台

7 Sprints 全部完成: Sprint 0: 基础设施 (DataManager + MariaDB) Sprint 1: 因子引擎 (34因子/12分类) Sprint 2: VectorBT 回测 (5策略+截面) Sprint 3: Optuna 优化 (+Walk-Forward) Sprint 4: ML 模型 (LightGBM+CatBoost) Sprint 5: Qwen 情绪因子 (三源新闻+日期对齐) Sprint 6: Agent 系统 (4Agent+日报.md/.html) 生产加固 (15项): Tushare双源fallback, SSH自动恢复, pool_pre_ping, save_daily先删后插, load_dotenv绝对路径, 日报5d/20d修复, RiskAgent改上证指数, 昨日对比+数据截止, mac_report utf8mb4, CLAUDE-*.md 9条已知Bug, demo全参数化, djapi数据源归一化, indexDatas API修正 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-07 15:59:05 +08:00
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+"""
+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,
+        )