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/base.py

@@ -0,0 +1,42 @@
+"""
+ML 模型抽象基类。
+
+统一接口：fit(X, y) → predict(X) → save/load。
+"""
+
+from abc import ABC, abstractmethod
+import pickle
+
+import pandas as pd
+
+
+class BaseModel(ABC):
+    """ML 模型抽象基类。"""
+
+    name: str = ""
+
+    @abstractmethod
+    def fit(self, X: pd.DataFrame, y: pd.Series) -> "BaseModel":
+        """训练模型。返回 self 支持链式调用。"""
+        ...
+
+    @abstractmethod
+    def predict(self, X: pd.DataFrame) -> pd.Series:
+        """返回预测值（回归值）。"""
+        ...
+
+    @abstractmethod
+    def get_feature_importance(self) -> pd.DataFrame:
+        """特征重要性 DataFrame，columns=[feature, importance]。"""
+        ...
+
+    def save(self, path: str) -> None:
+        """保存模型到文件（pickle）。"""
+        with open(path, "wb") as f:
+            pickle.dump(self, f)
+
+    @classmethod
+    def load(cls, path: str) -> "BaseModel":
+        """从文件加载模型。"""
+        with open(path, "rb") as f:
+            return pickle.load(f)