feat(factor): C1 因子相关性分析（横截面 Spearman 矩阵 + API）

- evaluation.factor_correlation_report：多因子共同日期 ∩ 后逐日横截面 Spearman 相关
  取均值 → FactorCorrelationReport（冗余剔除前置，v3 §12 Correlation→Redundancy）
- ResearchService.run_factor_correlation + POST /api/factor-correlations
  （universe/factors/period；与其它研究同装配口径）
- tests/test_factor_correlation.py：矩阵对角=1/近线性±相关符号/对称/无共同日期补零、
  API 冒烟；全量 pytest 通过

backend/app/quant/service.py

@@ -17,6 +17,7 @@ import pandas as pd
 
 from app.domain.entities.research import (
     BacktestResult,
+    FactorCorrelationReport,
     FactorTestReport,
     ResearchSpec,
 )
@@ -24,7 +25,9 @@ from app.domain.repositories.market import (
     DailyBarRepository,
     StockRepository,
 )
+from app.quant.composite import build_factor_panels
 from app.quant.engine import QuantEngine
+from app.quant.evaluation import factor_correlation_report
 from app.quant.universe import filter_stocks, resolve_members  # noqa: F401 —— 范围过滤
 
 # 流式路径每攒多少行落一个 DataFrame 分片（控制 concat 峰值）
@@ -129,6 +132,12 @@ class ResearchService:
         daily = self._load_daily(spec)
         return self._engine.run_backtest(daily, spec)
 
+    def run_factor_correlation(self, spec: ResearchSpec) -> FactorCorrelationReport:
+        """多因子两两相关（v3 §12）：同 universe/period 装配 → 横截面相关矩阵。"""
+        daily = self._load_daily(spec)
+        panels = {fs.name: build_factor_panels(daily, [fs])[0][1] for fs in spec.factors}
+        return factor_correlation_report(panels)
+
     # ---- 数据装配 ----
 
     def _load_daily(self, spec: ResearchSpec) -> pd.DataFrame: