feat: 量化引擎加固 — 新增测试 + 数据/因子/回测层优化

- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead）
- 数据层: data_manager / dao 优化，新增 upsert 逻辑
- 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构
- 回测层: vectorbt/engine 大改动（251 行），report 增强
- ML 层: features/backtest_integration 特征工程与回测优化
- CLI: agent_cli 重构
- config/settings 扩充配置项

finance/factors/fundamental/pe_pb.py

@@ -7,6 +7,7 @@ PE / PB 估值因子。
 import pandas as pd
 
 from factors.base import BaseFactor
+from factors.fundamental._mapping import map_fundamental_to_daily
 
 
 class PEFactor(BaseFactor):
@@ -31,7 +32,7 @@ class PEFactor(BaseFactor):
         if self._financial_df is None or self._financial_df.empty:
             return pd.Series(float("nan"), index=df.index)
 
-        eps_series = _map_to_daily(df, self._financial_df, "eps")
+        eps_series = map_fundamental_to_daily(df, self._financial_df, "eps")
         close = df["close"]
         return close / eps_series.replace(0, float("nan"))
 
@@ -56,7 +57,7 @@ class PBFactor(BaseFactor):
         if self._financial_df is None or self._financial_df.empty:
             return pd.Series(float("nan"), index=df.index)
 
-        bvps_series = _map_to_daily(df, self._financial_df, "bvps")
+        bvps_series = map_fundamental_to_daily(df, self._financial_df, "bvps")
         return df["close"] / bvps_series.replace(0, float("nan"))
 
     def get_required_columns(self) -> list[str]:
@@ -80,34 +81,8 @@ class EPFactor(BaseFactor):
         if self._financial_df is None or self._financial_df.empty:
             return pd.Series(float("nan"), index=df.index)
 
-        eps_series = _map_to_daily(df, self._financial_df, "eps")
+        eps_series = map_fundamental_to_daily(df, self._financial_df, "eps")
         return eps_series / df["close"].replace(0, float("nan")) * 100
 
     def get_required_columns(self) -> list[str]:
         return ["close"]
-
-
-def _map_to_daily(
-    daily_df: pd.DataFrame,
-    fina_df: pd.DataFrame,
-    column: str,
-) -> pd.Series:
-    """将季度财务数据填充到日线索引（前值填充）。"""
-    fina = fina_df[["end_date", column]].dropna().copy()
-    fina["end_date"] = fina["end_date"].astype(str)
-    fina = fina.sort_values("end_date")
-
-    result = pd.Series(float("nan"), index=daily_df.index)
-    if fina.empty:
-        return result
-
-    dates = pd.to_datetime(daily_df.index, format="%Y%m%d", errors="coerce")
-    fina_dates = pd.to_datetime(fina["end_date"], format="%Y%m%d", errors="coerce")
-
-    for i, fina_date in enumerate(fina_dates):
-        mask = dates >= fina_date
-        if i + 1 < len(fina_dates):
-            mask &= dates < fina_dates.iloc[i + 1]
-        result[mask] = fina[column].iloc[i]
-
-    return result.astype("float64")