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/catboost/model.py

@@ -0,0 +1,113 @@
+"""
+CatBoost 模型封装。
+"""
+
+import numpy as np
+import pandas as pd
+from catboost import CatBoostRegressor, Pool
+
+from models.base import BaseModel
+from sklearn.model_selection import TimeSeriesSplit
+
+_DEFAULT_PARAMS = {
+    "loss_function": "RMSE",
+    "iterations": 1000,
+    "learning_rate": 0.03,
+    "depth": 5,
+    "random_seed": 42,
+    "verbose": False,
+    "allow_writing_files": False,
+    "min_data_in_leaf": 20,
+}
+
+
+class CatBoostModel(BaseModel):
+    """CatBoost 回归模型。"""
+
+    name = "catboost"
+
+    def __init__(
+        self,
+        params: dict | None = None,
+        early_stopping: int = 50,
+        eval_ratio: float = 0.2,
+        random_seed: int = 42,
+    ):
+        self.params = params or _DEFAULT_PARAMS.copy()
+        self.params["random_seed"] = random_seed
+        self.early_stopping = early_stopping
+        self.eval_ratio = eval_ratio
+        self._model: CatBoostRegressor | None = None
+        self._feature_names: list[str] = []
+
+    def fit(self, X: pd.DataFrame, y: pd.Series) -> "CatBoostModel":
+        self._feature_names = list(X.columns)
+
+        n = len(X)
+        val_size = int(n * self.eval_ratio)
+
+        X_train, y_train = X, y
+        eval_set = None
+
+        if val_size >= 50:
+            split_idx = n - val_size
+            X_train, X_val = X.iloc[:split_idx], X.iloc[split_idx:]
+            y_train, y_val = y.iloc[:split_idx], y.iloc[split_idx:]
+            eval_set = Pool(X_val, y_val)
+
+        self._model = CatBoostRegressor(**self.params)
+        self._model.fit(
+            X_train, y_train,
+            eval_set=eval_set,
+            early_stopping_rounds=self.early_stopping if eval_set else None,
+            verbose=False,
+        )
+        return self
+
+    def predict(self, X: pd.DataFrame) -> pd.Series:
+        if self._model is None:
+            raise RuntimeError("模型尚未训练")
+        preds = self._model.predict(X[self._feature_names])
+        return pd.Series(preds, index=X.index, name="pred")
+
+    def get_feature_importance(self) -> pd.DataFrame:
+        if self._model is None:
+            return pd.DataFrame()
+        imp = self._model.get_feature_importance()
+        names = self._feature_names
+        df = pd.DataFrame({"feature": names, "importance": imp})
+        total = df["importance"].sum()
+        df["importance_pct"] = df["importance"] / total * 100 if total > 0 else 0
+        return df.sort_values("importance", ascending=False)
+
+    def cv_evaluate(
+        self, X: pd.DataFrame, y: pd.Series, n_folds: int = 5
+    ) -> pd.DataFrame:
+        """时间序列交叉验证评估。"""
+        tscv = TimeSeriesSplit(n_splits=n_folds)
+        results = []
+        for fold, (train_idx, test_idx) in enumerate(tscv.split(X)):
+            X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
+            y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
+
+            model = CatBoostModel(
+                params=self.params,
+                early_stopping=self.early_stopping,
+                eval_ratio=0.0,
+            )
+            model.fit(X_train, y_train)
+            preds = model.predict(X_test)
+            ic = preds.corr(y_test)
+            mse = ((preds - y_test) ** 2).mean()
+            results.append({"fold": fold, "ic": round(ic, 4), "mse": round(mse, 4)})
+
+        df = pd.DataFrame(results)
+        df.loc["mean"] = df.mean()
+        return df
+
+    @property
+    def n_estimators_used(self) -> int | None:
+        """实际使用的树数量。"""
+        if self._model is None:
+            return None
+        return self._model.tree_count_