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>

finance/backtest/report.py

@@ -0,0 +1,144 @@
+"""
+标准化回测报告。
+
+与回测引擎解耦，后续换引擎只需改构造函数。
+"""
+
+from dataclasses import dataclass, field
+
+import numpy as np
+import pandas as pd
+
+
+@dataclass
+class BacktestReport:
+    """回测报告"""
+
+    # 核心收益指标
+    total_return: float = 0.0
+    cagr: float = 0.0
+    max_drawdown: float = 0.0
+    sharpe_ratio: float = 0.0
+    calmar_ratio: float = 0.0
+    annual_volatility: float = 0.0
+
+    # 交易统计
+    win_rate: float = 0.0
+    profit_factor: float = 0.0
+    total_trades: int = 0
+    avg_hold_days: float = 0.0
+    best_trade_pct: float = 0.0
+    worst_trade_pct: float = 0.0
+
+    # 序列数据
+    equity_curve: pd.Series = field(default_factory=pd.Series)
+    drawdown_curve: pd.Series = field(default_factory=pd.Series)
+    monthly_returns: pd.Series = field(default_factory=pd.Series)
+    trades_df: pd.DataFrame = field(default_factory=pd.DataFrame)
+
+    # 原始 stats
+    stats_dict: dict = field(default_factory=dict)
+
+    @classmethod
+    def from_vbt_result(cls, pf, close: pd.Series) -> "BacktestReport":
+        """从 VectorBT Portfolio 结果构建报告。"""
+        stats = pf.stats()
+
+        def _pct(v):
+            """VectorBT stats 值已为百分比 float，直接返回。"""
+            if v is None:
+                return 0.0
+            try:
+                return float(v)
+            except (ValueError, TypeError):
+                return 0.0
+
+        def _duration_days(v):
+            """Timedelta → 天数。"""
+            if v is None:
+                return 0.0
+            try:
+                return v.total_seconds() / 86400
+            except AttributeError:
+                return float(v) if v is not None else 0.0
+
+        equity = pf.value()
+        equity = pd.Series(equity.values, index=close.index[: len(equity)])
+
+        if not isinstance(equity.index, pd.DatetimeIndex):
+            equity.index = pd.to_datetime(equity.index, format="%Y%m%d")
+
+        dd = equity / equity.cummax() - 1
+        daily_ret = equity.pct_change().dropna()
+
+        years = len(daily_ret) / 252 if len(daily_ret) > 0 else 1
+        total_ret = (equity.iloc[-1] / equity.iloc[0] - 1) * 100 if len(equity) > 1 else 0
+        cagr = ((total_ret / 100 + 1) ** (1 / years) - 1) * 100 if years > 0 else 0
+        mdd = dd.min() * 100
+        ann_vol = daily_ret.std() * np.sqrt(252) * 100 if len(daily_ret) > 0 else 0
+
+        mean_ret = daily_ret.mean() * 252
+        std_ret = daily_ret.std() * np.sqrt(252)
+        sharpe = mean_ret / std_ret if std_ret > 0 else 0
+        calmar = cagr / abs(mdd) if mdd != 0 else 0
+
+        trades = pf.trades.records_readable if hasattr(pf, "trades") else pd.DataFrame()
+
+        try:
+            monthly = equity.resample("ME").last().pct_change()
+        except Exception:
+            monthly = pd.Series(dtype=float)
+
+        pf_factor = stats.get("Profit Factor", 0)
+        if pf_factor is None or np.isinf(float(pf_factor)):
+            pf_factor = 0.0
+        else:
+            pf_factor = float(pf_factor)
+
+        return cls(
+            total_return=round(total_ret, 2),
+            cagr=round(cagr, 2),
+            max_drawdown=round(mdd, 2),
+            sharpe_ratio=round(sharpe, 2),
+            calmar_ratio=round(calmar, 2),
+            annual_volatility=round(ann_vol, 2),
+            win_rate=_pct(stats.get("Win Rate [%]", 0)),
+            profit_factor=pf_factor,
+            total_trades=int(stats.get("Total Trades", 0)),
+            avg_hold_days=_duration_days(stats.get("Avg Winning Trade Duration", None)),
+            best_trade_pct=_pct(stats.get("Best Trade [%]", 0)),
+            worst_trade_pct=_pct(stats.get("Worst Trade [%]", 0)),
+            equity_curve=equity,
+            drawdown_curve=dd,
+            monthly_returns=monthly,
+            trades_df=trades,
+            stats_dict={k: str(v) for k, v in stats.items()},
+        )
+
+    def to_dict(self) -> dict:
+        """核心指标转字典。"""
+        return {
+            "total_return": self.total_return,
+            "cagr": self.cagr,
+            "max_drawdown": self.max_drawdown,
+            "sharpe_ratio": self.sharpe_ratio,
+            "calmar_ratio": self.calmar_ratio,
+            "annual_volatility": self.annual_volatility,
+            "win_rate": self.win_rate,
+            "profit_factor": self.profit_factor,
+            "total_trades": self.total_trades,
+        }
+
+    def summary(self) -> str:
+        """一行摘要。"""
+        return (
+            f"收益={self.total_return:.1f}% "
+            f"年化={self.cagr:.1f}% "
+            f"回撤={self.max_drawdown:.1f}% "
+            f"夏普={self.sharpe_ratio:.2f} "
+            f"胜率={self.win_rate:.1f}% "
+            f"交易={self.total_trades}笔"
+        )
+
+    def __repr__(self) -> str:
+        return self.summary()