feat(universe): B1-1 指数历史成分（index_weight）+ Universe 按 as_of 成分过滤

- index_weight 表（migration f5e0d1c2b3a4，MySQL 已应用；index_code+date+symbol 唯一）
  + IndexWeight 实体 + IndexConstituentRepository（members_at：取 <=as_of 最近一期快照，
  Survivorship-free / 无未来成分；latest_date）
- UniverseSpec.index_code + universe.filter_stocks members 交集 + resolve_members；
  Research/Selection/Signal/Replay 服务注入 index repo（历史成分过滤，选股/回测共用）
- tests/test_index_universe.py：快照历史成分（成分变更不入早期结果）、幂等、
  空快照期空集、index_code 过滤下 as_of 一致性；全量 pytest 通过

backend/app/quant/service.py

@@ -25,7 +25,7 @@ from app.domain.repositories.market import (
     StockRepository,
 )
 from app.quant.engine import QuantEngine
-from app.quant.universe import filter_stocks  # noqa: F401 —— 选股/回测共用范围过滤
+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）
 _FRAME_CHUNK_ROWS = 50_000
@@ -110,10 +110,12 @@ class ResearchService:
         stock_repo: StockRepository,
         daily_repo: DailyBarRepository,
         engine: QuantEngine,
+        index_repo=None,
     ) -> None:
         self._stock_repo = stock_repo
         self._daily_repo = daily_repo
         self._engine = engine
+        self._index_repo = index_repo
 
     def run_factor_test(self, spec: ResearchSpec, horizon_days: int = 21) -> FactorTestReport:
         if spec.type != "factor_test":
@@ -133,7 +135,10 @@ class ResearchService:
         start, end = spec.period
         # 回测前预留因子 warmup（lookback≤120 交易日，取 300 自然日余量）
         data_start = start - timedelta(days=300)
-        stocks = filter_stocks(self._stock_repo.list(), spec.universe, as_of=start)
+        stocks = filter_stocks(
+            self._stock_repo.list(), spec.universe, as_of=start,
+            members=resolve_members(self._index_repo, spec.universe, start),
+        )
         # 引擎所需列裁剪（LocalEngine 只取 close + 因子字段；Qlib 回测取全 OHLCV）
         required = self._engine.required_columns(spec)
         return load_daily_df(

backend/app/quant/universe.py

@@ -17,17 +17,30 @@ from app.domain.entities.market import Stock
 from app.domain.entities.research import UniverseSpec
 
 
+def resolve_members(index_repo, universe: UniverseSpec, as_of: date) -> set[str] | None:
+    """若 universe 指定指数成分 → 取 as_of 当日历史成分；否则 None（不过滤）。"""
+    if index_repo is None or not universe.index_code:
+        return None
+    return index_repo.members_at(universe.index_code, as_of)
+
+
 def filter_stocks(
     stocks: Sequence[Stock],
     universe: UniverseSpec,
     as_of: date,
+    members: set[str] | None = None,
 ) -> list[Stock]:
-    """按股票池口径过滤，返回 as_of 时点应纳入的股票列表。"""
+    """按股票池口径过滤，返回 as_of 时点应纳入的股票列表。
+
+    members：指数历史成分集合（resolve_members 结果）；提供时取交集。
+    """
     symbols = set(universe.symbols) if universe.symbols else None
     out: list[Stock] = []
     for s in stocks:
         if symbols is not None and s.symbol not in symbols:
             continue
+        if members is not None and s.symbol not in members:
+            continue
         if s.delist_date is not None and s.delist_date < as_of:
             continue
         if universe.exclude_st and s.name and "ST" in s.name.upper():