feat(selection): M6.4 回测与选股共用评分引擎（v2 §25 一致性锁定）

- quant/selection.score_panel_for_factors：复合分面板构建收敛为共享函数；
  LocalEngine.run_backtest 与 SelectionEngine.run_score_selection 均调它 ——
  消除「回测一套评分、选股另一套」的隐患
- tests/test_selection_backtest_consistency.py：对回测每个调仓日验证
  SelectionService.select(as_of=d, top_n) 候选 == 该日回测实际持仓（月调仓多时点），
  排序方向一致性亦验证；全量 pytest 通过

backend/app/quant/engine.py

@@ -11,12 +11,8 @@ import pandas as pd
 
 from app.domain.entities.research import BacktestResult, FactorTestReport, ResearchSpec
 from app.quant.factors import FactorError, get_factor
-from app.quant.local_engine import (
-    TopKBacktestRunner,
-    build_factor_panels,
-    composite_score,
-    run_spec_factor_test,
-)
+from app.quant.local_engine import TopKBacktestRunner, run_spec_factor_test
+from app.quant.selection import score_panel_for_factors
 
 # LocalEngine 路径恒需 close（TopK 收盘撮合 / 前瞻收益）
 _CLOSE = {"close"}
@@ -64,7 +60,7 @@ class LocalEngine:
         return report
 
     def run_backtest(self, daily: pd.DataFrame, spec: ResearchSpec) -> BacktestResult:
-        panels = build_factor_panels(daily, spec.factors)
-        score = composite_score(panels)
+        # 评分面板与选股共用同一构建（v2 §25：回测与当前选股同引擎）
+        score = score_panel_for_factors(daily, spec.factors)
         close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index()
         return TopKBacktestRunner(spec, score, close).run()

backend/app/quant/selection.py

@@ -29,6 +29,16 @@ _UNIMPLEMENTED_DEFAULT = [
 ]
 
 
+def score_panel_for_factors(daily: pd.DataFrame, factor_specs) -> pd.DataFrame:
+    """因子加权复合分面板（index=trade_date, columns=symbol）。
+
+    回测（LocalEngine）与选股（run_score_selection）共用同一构建 ——
+    保证 v2 §25/§27「历史回测与当前选股使用同一套引擎」的一致性。
+    """
+    panels = build_factor_panels(daily, factor_specs)  # 未知因子在此抛 FactorError
+    return composite_score(panels)
+
+
 def resolve_observation_date(daily: pd.DataFrame, as_of: date | None) -> pd.Timestamp | None:
     """<= as_of 的最近可用交易日；as_of=None 取数据最新一日。"""
     if daily.empty:
@@ -84,8 +94,9 @@ def run_score_selection(
             config_snapshot=query.model_dump(mode="json"),
         )
 
-    panels = build_factor_panels(view, query.factors)  # 未知因子在此抛 FactorError
-    score = composite_score(panels).loc[obs].dropna().sort_values(ascending=False)
+    score = score_panel_for_factors(view, query.factors).loc[obs].dropna().sort_values(
+        ascending=False
+    )
 
     # 每因子在 obs 行的原始值（factor_values 供展示与解释；与 build_factor_panels 同数据）
     raw: dict[str, pd.Series] = {}