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/tests/test_selection.py

@@ -0,0 +1,223 @@
+"""M6.0 选股契约与服务测试：因子评分 TopN 选股、as_of 未来函数防护、universe 过滤。
+
+使用 Fake（内存）Repository + 合成/自定义行情，不触数据库。
+"""
+
+from __future__ import annotations
+
+from datetime import date
+from decimal import Decimal
+
+import pandas as pd
+import pytest
+from app.application.services.selection_service import SelectionService
+from app.domain.entities.market import DailyBar, Stock
+from app.domain.entities.selection import SelectionQuery
+from pydantic import ValidationError
+
+from conftest_quant import synthetic_daily
+
+_SYMS = ["600000.SH", "600001.SH", "600002.SH", "600003.SH", "600004.SH"]
+
+
+def _mem_stocks() -> list[Stock]:
+    return [
+        Stock(symbol=s, name=f"测试股份{i}", list_date=date(1999, 1, 1))
+        for i, s in enumerate(_SYMS)
+    ]
+
+
+class _MemStockRepo:
+    def __init__(self, stocks: list[Stock]) -> None:
+        self._stocks = stocks
+
+    def list(self) -> list[Stock]:
+        return self._stocks
+
+    def get_by_symbol(self, symbol: str) -> Stock | None:
+        return next((s for s in self._stocks if s.symbol == symbol), None)
+
+
+class _MemDailyRepo:
+    """内存日线仓库：支持 get_range / get_range_many（无流式 → 走回退路径）。"""
+
+    def __init__(self, df: pd.DataFrame) -> None:
+        self._df = df
+
+    def _bars(self, symbols, start, end) -> list[DailyBar]:
+        sub = self._df[
+            self._df["symbol"].isin(symbols)
+            & (self._df["trade_date"] >= start)
+            & (self._df["trade_date"] <= end)
+        ]
+        out: list[DailyBar] = []
+        for r in sub.itertuples():
+            out.append(
+                DailyBar(
+                    symbol=r.symbol,
+                    trade_date=r.trade_date,
+                    open=Decimal(str(r.open)),
+                    high=Decimal(str(r.high)),
+                    low=Decimal(str(r.low)),
+                    close=Decimal(str(r.close)),
+                    volume=Decimal(str(r.volume)),
+                    amount=Decimal(str(r.amount)),
+                )
+            )
+        return out
+
+    def get_range(self, symbol, start, end) -> list[DailyBar]:
+        return self._bars([symbol], start, end)
+
+    def get_range_many(self, symbols, start, end) -> list[DailyBar]:
+        return self._bars(list(symbols), start, end)
+
+    def latest_date(self, symbol: str) -> date | None:
+        sub = self._df[self._df["symbol"] == symbol]
+        return None if sub.empty else sub["trade_date"].max()
+
+
+@pytest.fixture()
+def svc():
+    drifts = {sym: 0.006 - 0.0015 * i for i, sym in enumerate(_SYMS)}
+    df = synthetic_daily(drifts, n=320)
+    return SelectionService(_MemStockRepo(_mem_stocks()), _MemDailyRepo(df))
+
+
+def _score_query(**kw) -> SelectionQuery:
+    base = dict(
+        factors=[{"name": "momentum_60", "weight": 1.0}],
+        top_n=3,
+        as_of=date(2024, 12, 31),
+    )
+    base.update(kw)
+    return SelectionQuery(**base)
+
+
+class TestScoreSelection:
+    def test_returns_top_n_with_rank_reason(self, svc) -> None:
+        res = svc.select(_score_query())
+        assert res.method == "score"
+        assert len(res.candidates) == 3
+        assert [c.rank for c in res.candidates] == [1, 2, 3]
+        # 分数降序
+        scores = [c.score for c in res.candidates]
+        assert scores == sorted(scores, reverse=True)
+        # 每个候选带因子值与理由（可解释）
+        for c in res.candidates:
+            assert c.symbol in _SYMS
+            assert "momentum_60" in c.factor_values
+            assert any("momentum_60" in r for r in c.selection_reason)
+        # 统计：评估数 > 0
+        assert res.statistics.evaluated >= 3
+        assert res.statistics.selected == 3
+        assert res.as_of_date <= date(2024, 12, 31)
+
+    def test_highest_drift_ranked_high(self, svc) -> None:
+        res = svc.select(_score_query(top_n=1))
+        # 最高漂移股票（600000.SH）应位列前三（动量因子对强趋势敏感）
+        assert res.candidates[0].symbol in _SYMS[:3]
+
+    def test_top_pct(self, svc) -> None:
+        res = svc.select(_score_query(top_n=None, top_pct=0.4))
+        assert 1 <= len(res.candidates) <= 3  # 5 只的 40% ≈ 2
+        assert res.candidates[0].rank == 1
+
+    def test_min_score_filters(self, svc) -> None:
+        res = svc.select(_score_query(min_score=10_000))
+        # 合成数据最高动量约 +60%，min_score=10000 应无候选
+        assert res.candidates == []
+
+
+class TestAsOfNoFutureFunction:
+    def _two_stage_df(self) -> tuple[pd.DataFrame, date, date]:
+        """X 前段强、Y 前段平后段暴涨：as_of=split 时 Y 的 60 日动量应为 0/无。"""
+        dates = pd.bdate_range("2024-01-01", periods=200)
+        split = dates[100].date()  # d101：Y 从这天开始暴涨
+        rows: list[dict] = []
+        for d in dates:
+            # X：恒定 +0.8%/日
+            rows.append({"symbol": "600000.SH", "trade_date": d.date(),
+                         "close": 100 * 1.008 ** ((d - dates[0]).days)})
+        y_price = 100.0
+        for d in dates:
+            if d.date() > split:
+                y_price = y_price * 1.05
+            rows.append({"symbol": "600001.SH", "trade_date": d.date(), "close": y_price})
+        df = pd.DataFrame(rows)
+        for col in ("open", "high", "low", "volume", "amount"):
+            df[col] = df["close"] * 1.001 if col != "volume" else 1_000_000
+        return df, split, dates[-1].date()
+
+    def test_as_of_cutoff_excludes_future(self) -> None:
+        df, split, _end = self._two_stage_df()
+        stocks = [
+            Stock(symbol="600000.SH", name="A", list_date=date(1999, 1, 1)),
+            Stock(symbol="600001.SH", name="B", list_date=date(1999, 1, 1)),
+        ]
+        svc = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(df))
+
+        # 历史选股：as_of=split（Y 尚未暴涨）→ Y 不应排第一（其 60 日动量≈0/缺失）
+        res = svc.select(_score_query(factors=[{"name": "momentum_60", "weight": 1.0}], as_of=split))
+        assert res.candidates
+        assert res.candidates[0].symbol == "600000.SH"
+        # Y 若进入候选，其理由值应接近 0（而非泄漏未来暴涨的巨幅动量）
+        y = next((c for c in res.candidates if c.symbol == "600001.SH"), None)
+        if y is not None:
+            assert y.factor_values["momentum_60"] < 0.1
+
+
+class TestUniverseFilter:
+    def test_exclude_st(self, svc) -> None:
+        stocks = _mem_stocks()[:2]
+        stocks[0] = stocks[0].model_copy(update={"name": "ST 风险股份"})
+        s = SelectionService(_MemStockRepo(stocks), svc._daily_repo)
+        res = s.select(_score_query())
+        assert all(c.symbol != "600000.SH" for c in res.candidates)
+
+    def test_min_listing_days(self) -> None:
+        stocks = [
+            Stock(symbol=_SYMS[0], name="新上市", list_date=date(2024, 12, 1)),
+            Stock(symbol=_SYMS[1], name="老股", list_date=date(1999, 1, 1)),
+        ]
+        df = synthetic_daily({_SYMS[0]: 0.01, _SYMS[1]: 0.001}, n=320)
+        s = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(df))
+        res = s.select(_score_query(as_of=date(2024, 12, 31)))
+        assert all(c.symbol != _SYMS[0] for c in res.candidates)  # 上市不足 250 自然日被滤除
+
+    def test_delisted_before_as_of(self) -> None:
+        stocks = [
+            Stock(symbol=_SYMS[0], name="已退市", list_date=date(1990, 1, 1),
+                  delist_date=date(2023, 6, 30)),
+            Stock(symbol=_SYMS[1], name="正常", list_date=date(1999, 1, 1)),
+        ]
+        df = synthetic_daily({_SYMS[0]: 0.01, _SYMS[1]: 0.001}, n=320)
+        s = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(df))
+        res = s.select(_score_query(as_of=date(2024, 6, 30)))
+        assert all(c.symbol != _SYMS[0] for c in res.candidates)
+
+
+class TestEmptyAndValidation:
+    def test_no_stocks_returns_empty(self) -> None:
+        s = SelectionService(_MemStockRepo([]), _MemDailyRepo(pd.DataFrame()))
+        res = s.select(_score_query())
+        assert res.candidates == []
+        assert res.statistics.selected == 0
+
+    def test_query_validation(self) -> None:
+        with pytest.raises(ValidationError):
+            SelectionQuery(method="score", top_n=5)  # score 缺 factors
+        with pytest.raises(ValidationError):
+            SelectionQuery(method="condition", conditions=[], top_n=5)  # condition 缺 conditions
+        with pytest.raises(ValidationError):
+            SelectionQuery(factors=[{"name": "a", "weight": 1}], top_n=None, top_pct=None)
+        with pytest.raises(ValidationError):
+            SelectionQuery(
+                factors=[{"name": "a", "weight": 1}, {"name": "a", "weight": 2}], top_n=5
+            )
+        # 合法
+        SelectionQuery(factors=[{"name": "a", "weight": 1}], top_n=5)
+
+    def test_query_top_pct_valid(self) -> None:
+        q = SelectionQuery(factors=[{"name": "momentum_60", "weight": 1}], top_pct=0.5)
+        assert q.top_n is None and q.top_pct == 0.5