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/models/features.py

@@ -0,0 +1,142 @@
+"""
+特征工程：因子 → 特征矩阵 + 目标标签。
+
+严禁使用未来数据。所有变换基于 expanding window 或训练集统计。
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import RobustScaler
+
+
+class FeatureEngine:
+    """
+    特征工程引擎。
+
+    参数:
+        lookahead: 预测未来 N 个交易日
+        label_type: 'regression' | 'classification'
+        winsorize_pct: 去极值的分位数边界 (0.01, 0.99)
+        nan_threshold: NaN 占比超过此值的因子直接剔除
+    """
+
+    def __init__(
+        self,
+        lookahead: int = 5,
+        label_type: str = "regression",
+        winsorize_pct: tuple[float, float] = (0.01, 0.99),
+        nan_threshold: float = 0.3,
+    ):
+        self.lookahead = lookahead
+        self.label_type = label_type
+        self.winsorize_pct = winsorize_pct
+        self.nan_threshold = nan_threshold
+        self._scaler = RobustScaler()
+        self._scaler_fitted = False
+        self._valid_features: list[str] = []
+
+    # ── 标签构建 ──────────────────────────────────────────
+
+    def build_labels(self, price_df: pd.DataFrame) -> pd.Series:
+        """
+        构建目标标签。
+
+        regression: (close_{t+N} - close_t) / close_t * 100
+        classification: 1 if return > 0 else 0
+        """
+        close = price_df["close"]
+        future = close.shift(-self.lookahead)
+        ret = (future - close) / close * 100
+
+        if self.label_type == "classification":
+            return (ret > 0).astype(int)
+
+        return ret.rename(f"y_fwd_{self.lookahead}")
+
+    # ── 特征构建 ──────────────────────────────────────────
+
+    def build(
+        self,
+        factor_df: pd.DataFrame,
+        price_df: pd.DataFrame,
+        fit: bool = True,
+    ) -> tuple[pd.DataFrame, pd.Series]:
+        """
+        构建特征矩阵 X 和标签 y。
+
+        参数:
+            factor_df: 因子 DataFrame, index=trade_date, columns=因子名
+            price_df: 价格 DataFrame, 需有 'close'
+            fit: True=训练模式（fit scaler + 记录有效特征），False=预测模式
+
+        返回:
+            X, y（y 在 predict 模式下为 None）
+        """
+        X = factor_df.copy()
+
+        # 1. 剔除 NaN 率过高的列
+        if fit:
+            nan_ratio = X.isna().mean()
+            self._valid_features = list(nan_ratio[nan_ratio <= self.nan_threshold].index)
+            # 排除非因子列
+            self._valid_features = [c for c in self._valid_features
+                                     if c not in ("close", "open", "high", "low", "volume")]
+        X = X[self._valid_features].copy() if self._valid_features else X
+
+        # 2. 缺失值填充：前值填充 → 截面中位数
+        X = X.ffill().fillna(X.median())
+
+        # 3. 去极值（Winsorize）
+        if fit:
+            lo, hi = self.winsorize_pct
+            self._winsor_lower = X.quantile(lo)
+            self._winsor_upper = X.quantile(hi)
+        for col in X.columns:
+            if col in getattr(self, "_winsor_lower", pd.Series()):
+                X[col] = X[col].clip(self._winsor_lower[col], self._winsor_upper[col])
+
+        # 4. 标准化（训练时 fit，预测时 transform）
+        if fit:
+            X_scaled = self._scaler.fit_transform(X)
+            self._scaler_fitted = True
+        else:
+            X_scaled = self._scaler.transform(X)
+
+        X = pd.DataFrame(X_scaled, index=X.index, columns=X.columns)
+
+        # 5. 构建标签
+        y = self.build_labels(price_df) if fit else None
+
+        # 6. 对齐（删掉无法构建标签的行）
+        if fit:
+            valid_idx = X.index.intersection(y.dropna().index)
+            X = X.loc[valid_idx]
+            y = y.loc[valid_idx]
+
+        return X, y
+
+    # ── 多股票构建 ────────────────────────────────────────
+
+    def build_universe(
+        self,
+        factor_universe: dict[str, pd.DataFrame],
+        price_universe: dict[str, pd.DataFrame],
+    ) -> tuple[pd.DataFrame, pd.Series]:
+        """多股票拼接特征矩阵（每只股票独立处理再拼接）。"""
+        X_parts, y_parts = [], []
+        for ts_code in factor_universe:
+            f_df = factor_universe[ts_code]
+            p_df = price_universe.get(ts_code)
+            if p_df is None or f_df.empty or p_df.empty:
+                continue
+            X, y = self.build(f_df, p_df, fit=True)
+            if X.empty:
+                continue
+            X["_ts_code"] = ts_code
+            X_parts.append(X)
+            y_parts.append(y)
+        if not X_parts:
+            return pd.DataFrame(), pd.Series()
+        X_all = pd.concat(X_parts)
+        y_all = pd.concat(y_parts)
+        return X_all.drop(columns=["_ts_code"]), y_all