feat(selection): M6.0 选股契约与评分引擎（SelectionQuery/Result + select(as_of)）

- domain/entities/selection.py：SelectionQuery（universe+method+factors+top_n/top_pct/
  min_score+as_of+预热）与 SelectionResult/Candidate/Statistics（v2 §14.2/§21.1 DTO）；
  ConditionSpec 字段就位供 M6.2 条件选股
- quant/selection.py：Selection Engine method=score —— 复合分（zscore×权重×方向）
  → TopN/Top% 截断；observation_date=<=as_of 最近交易日（防未来函数，v2 §9）；
  候选带 factor_values 与 selection_reason（可解释）
- application/services/selection_service.py：选股用例（universe 过滤 → 装配 → 引擎）
- quant/service.py：抽取公共 load_daily_df 供研究/选股共用（行为不变）
- tests/test_selection.py：11 例 —— TopN/排序/理由、as_of 防未来函数、ST/上市天数/
  退市过滤、top_pct/min_score、空数据与查询校验；全量 pytest 通过

backend/app/application/services/selection_service.py

@@ -0,0 +1,50 @@
+"""选股用例入口（ARCHITECTURE_v2 §14 Selection Engine · 业务层）。
+
+- 输入：SelectionQuery（universe + method + factors/conditions + top_n/pct + as_of）
+- 装配：股票池（universe 过滤）→ 行情长表（含预热窗口）→ Selection Engine
+- 输出：SelectionResult（可解释：factor_values / selection_reason）
+- 未来函数红线：全部数据只取到 <= as_of（v2 §9）；财务条件（后续）只取已公告值
+
+MVP 为同步执行（单日全市场因子计算量轻）；如需异步可复用 Job 链路（M6.3 决策）。
+"""
+
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+import pandas as pd
+
+from app.domain.entities.selection import SelectionQuery, SelectionResult
+from app.domain.repositories.market import DailyBarRepository, StockRepository
+from app.quant.selection import factor_columns, run_score_selection
+from app.quant.service import filter_stocks, load_daily_df
+
+
+class SelectionService:
+    """选股用例入口：select(query) → SelectionResult（当前或历史 as_of）。"""
+
+    def __init__(
+        self,
+        stock_repo: StockRepository,
+        daily_repo: DailyBarRepository,
+    ) -> None:
+        self._stock_repo = stock_repo
+        self._daily_repo = daily_repo
+
+    def select(self, query: SelectionQuery) -> SelectionResult:
+        as_of = query.as_of or date.today()
+        stocks = filter_stocks(self._stock_repo.list(), query.universe, as_of=as_of)
+        if not stocks:
+            return run_score_selection(pd.DataFrame(), query, as_of)
+        symbols = [s.symbol for s in stocks]
+        columns = sorted(factor_columns(query)) if query.method == "score" else ["close"]
+        daily = load_daily_df(
+            self._daily_repo,
+            symbols,
+            as_of - timedelta(days=query.warmup_days),
+            as_of,
+            columns,
+        )
+        if daily.empty:
+            return run_score_selection(daily, query, as_of)
+        return run_score_selection(daily, query, as_of)