Simon
ef09d5b419
- DailyBarRepository.get_range_many / stream_range_many_columns 增加 adjust 参数 （默认 'none'）→ SQL 层过滤口径，消除 stock_daily 混 source/adjust 污染因子的风险 - ResearchSpec / SelectionQuery 增加 price_adjustment（none|qfq），随 config_snapshot 落库可溯源；ResearchService._load_daily 与 SelectionService 装配按口径取数 - tests/test_price_adjustment.py：repo 读取按 adjust 过滤（none/qfq 各自命中）、 spec 默认与字段记录；全量 pytest 通过
224 lines
8.8 KiB
Python
224 lines
8.8 KiB
Python
"""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。
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使用 Fake（内存）Repository + 合成/自定义行情，不触数据库。
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"""
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from __future__ import annotations
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from datetime import date
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from decimal import Decimal
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import pandas as pd
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import pytest
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from app.application.services.selection_service import SelectionService
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from app.domain.entities.market import DailyBar, Stock
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from app.domain.entities.selection import SelectionQuery
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from pydantic import ValidationError
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from conftest_quant import synthetic_daily
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_SYMS = ["600000.SH", "600001.SH", "600002.SH", "600003.SH", "600004.SH"]
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def _mem_stocks() -> list[Stock]:
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return [
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Stock(symbol=s, name=f"测试股份{i}", list_date=date(1999, 1, 1))
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for i, s in enumerate(_SYMS)
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]
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class _MemStockRepo:
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def __init__(self, stocks: list[Stock]) -> None:
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self._stocks = stocks
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def list(self) -> list[Stock]:
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return self._stocks
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def get_by_symbol(self, symbol: str) -> Stock | None:
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return next((s for s in self._stocks if s.symbol == symbol), None)
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class _MemDailyRepo:
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"""内存日线仓库：支持 get_range / get_range_many（无流式 → 走回退路径）。"""
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def __init__(self, df: pd.DataFrame) -> None:
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self._df = df
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def _bars(self, symbols, start, end) -> list[DailyBar]:
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sub = self._df[
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self._df["symbol"].isin(symbols)
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& (self._df["trade_date"] >= start)
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& (self._df["trade_date"] <= end)
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]
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out: list[DailyBar] = []
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for r in sub.itertuples():
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out.append(
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DailyBar(
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symbol=r.symbol,
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trade_date=r.trade_date,
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open=Decimal(str(r.open)),
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high=Decimal(str(r.high)),
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low=Decimal(str(r.low)),
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close=Decimal(str(r.close)),
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volume=Decimal(str(r.volume)),
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amount=Decimal(str(r.amount)),
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)
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)
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return out
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def get_range(self, symbol, start, end) -> list[DailyBar]:
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return self._bars([symbol], start, end)
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def get_range_many(self, symbols, start, end, adjust="none") -> list[DailyBar]:
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return self._bars(list(symbols), start, end)
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def latest_date(self, symbol: str) -> date | None:
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sub = self._df[self._df["symbol"] == symbol]
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return None if sub.empty else sub["trade_date"].max()
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@pytest.fixture()
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def svc():
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drifts = {sym: 0.006 - 0.0015 * i for i, sym in enumerate(_SYMS)}
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df = synthetic_daily(drifts, n=320)
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return SelectionService(_MemStockRepo(_mem_stocks()), _MemDailyRepo(df))
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def _score_query(**kw) -> SelectionQuery:
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base = dict(
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factors=[{"name": "momentum_60", "weight": 1.0}],
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top_n=3,
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as_of=date(2024, 12, 31),
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)
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base.update(kw)
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return SelectionQuery(**base)
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class TestScoreSelection:
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def test_returns_top_n_with_rank_reason(self, svc) -> None:
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res = svc.select(_score_query())
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assert res.method == "score"
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assert len(res.candidates) == 3
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assert [c.rank for c in res.candidates] == [1, 2, 3]
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# 分数降序
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scores = [c.score for c in res.candidates]
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assert scores == sorted(scores, reverse=True)
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# 每个候选带因子值与理由（可解释）
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for c in res.candidates:
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assert c.symbol in _SYMS
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assert "momentum_60" in c.factor_values
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assert any("momentum_60" in r for r in c.selection_reason)
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# 统计：评估数 > 0
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assert res.statistics.evaluated >= 3
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assert res.statistics.selected == 3
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assert res.as_of_date <= date(2024, 12, 31)
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def test_highest_drift_ranked_high(self, svc) -> None:
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res = svc.select(_score_query(top_n=1))
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# 最高漂移股票（600000.SH）应位列前三（动量因子对强趋势敏感）
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assert res.candidates[0].symbol in _SYMS[:3]
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def test_top_pct(self, svc) -> None:
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res = svc.select(_score_query(top_n=None, top_pct=0.4))
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assert 1 <= len(res.candidates) <= 3 # 5 只的 40% ≈ 2
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assert res.candidates[0].rank == 1
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def test_min_score_filters(self, svc) -> None:
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res = svc.select(_score_query(min_score=10_000))
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# 合成数据最高动量约 +60%，min_score=10000 应无候选
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assert res.candidates == []
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class TestAsOfNoFutureFunction:
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def _two_stage_df(self) -> tuple[pd.DataFrame, date, date]:
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"""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。"""
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dates = pd.bdate_range("2024-01-01", periods=200)
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split = dates[100].date() # d101：Y 从这天开始暴涨
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rows: list[dict] = []
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for d in dates:
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# X：恒定 +0.8%/日
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rows.append({"symbol": "600000.SH", "trade_date": d.date(),
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"close": 100 * 1.008 ** ((d - dates[0]).days)})
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y_price = 100.0
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for d in dates:
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if d.date() > split:
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y_price = y_price * 1.05
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rows.append({"symbol": "600001.SH", "trade_date": d.date(), "close": y_price})
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df = pd.DataFrame(rows)
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for col in ("open", "high", "low", "volume", "amount"):
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df[col] = df["close"] * 1.001 if col != "volume" else 1_000_000
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return df, split, dates[-1].date()
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def test_as_of_cutoff_excludes_future(self) -> None:
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df, split, _end = self._two_stage_df()
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stocks = [
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Stock(symbol="600000.SH", name="A", list_date=date(1999, 1, 1)),
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Stock(symbol="600001.SH", name="B", list_date=date(1999, 1, 1)),
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]
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svc = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(df))
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# 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）
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res = svc.select(_score_query(factors=[{"name": "momentum_60", "weight": 1.0}], as_of=split))
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assert res.candidates
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assert res.candidates[0].symbol == "600000.SH"
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# 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）
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y = next((c for c in res.candidates if c.symbol == "600001.SH"), None)
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if y is not None:
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assert y.factor_values["momentum_60"] < 0.1
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class TestUniverseFilter:
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def test_exclude_st(self, svc) -> None:
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stocks = _mem_stocks()[:2]
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stocks[0] = stocks[0].model_copy(update={"name": "ST 风险股份"})
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s = SelectionService(_MemStockRepo(stocks), svc._daily_repo)
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res = s.select(_score_query())
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assert all(c.symbol != "600000.SH" for c in res.candidates)
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def test_min_listing_days(self) -> None:
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stocks = [
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Stock(symbol=_SYMS[0], name="新上市", list_date=date(2024, 12, 1)),
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Stock(symbol=_SYMS[1], name="老股", list_date=date(1999, 1, 1)),
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]
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df = synthetic_daily({_SYMS[0]: 0.01, _SYMS[1]: 0.001}, n=320)
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s = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(df))
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res = s.select(_score_query(as_of=date(2024, 12, 31)))
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assert all(c.symbol != _SYMS[0] for c in res.candidates) # 上市不足 250 自然日被滤除
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def test_delisted_before_as_of(self) -> None:
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stocks = [
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Stock(symbol=_SYMS[0], name="已退市", list_date=date(1990, 1, 1),
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delist_date=date(2023, 6, 30)),
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Stock(symbol=_SYMS[1], name="正常", list_date=date(1999, 1, 1)),
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]
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df = synthetic_daily({_SYMS[0]: 0.01, _SYMS[1]: 0.001}, n=320)
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s = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(df))
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res = s.select(_score_query(as_of=date(2024, 6, 30)))
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assert all(c.symbol != _SYMS[0] for c in res.candidates)
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class TestEmptyAndValidation:
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def test_no_stocks_returns_empty(self) -> None:
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s = SelectionService(_MemStockRepo([]), _MemDailyRepo(pd.DataFrame()))
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res = s.select(_score_query())
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assert res.candidates == []
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assert res.statistics.selected == 0
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def test_query_validation(self) -> None:
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with pytest.raises(ValidationError):
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SelectionQuery(method="score", top_n=5) # score 缺 factors
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with pytest.raises(ValidationError):
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SelectionQuery(method="condition", conditions=[], top_n=5) # condition 缺 conditions
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with pytest.raises(ValidationError):
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SelectionQuery(factors=[{"name": "a", "weight": 1}], top_n=None, top_pct=None)
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with pytest.raises(ValidationError):
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SelectionQuery(
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factors=[{"name": "a", "weight": 1}, {"name": "a", "weight": 2}], top_n=5
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)
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# 合法
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SelectionQuery(factors=[{"name": "a", "weight": 1}], top_n=5)
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def test_query_top_pct_valid(self) -> None:
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q = SelectionQuery(factors=[{"name": "momentum_60", "weight": 1}], top_pct=0.5)
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assert q.top_n is None and q.top_pct == 0.5
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