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
126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
"""M6.4 一致性回归：回测（TopKBacktestRunner）与独立 select(as_of) 使用同一评分引擎。
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v2 §25/§27 红线验证：对任意调仓日 d，SelectionService.select(as_of=d, top_n)
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的候选集合 == 该日回测实际买入持仓集合 —— 证明「当前选股 = 历史回测选股」，
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防止回测一套逻辑、实际选股另一套逻辑。
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"""
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from __future__ import annotations
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from datetime import date
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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 Stock
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from app.domain.entities.research import ResearchSpec
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from app.domain.entities.selection import SelectionQuery
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from app.quant.engine import LocalEngine
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from conftest_quant import synthetic_daily
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_SYMS = ["60000" + str(i) + ".SH" for i in range(5)] # 600000~600004
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class _MemStockRepo:
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def __init__(self, stocks):
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self._stocks = stocks
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def list(self):
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return self._stocks
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def get_by_symbol(self, symbol):
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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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def __init__(self, df: pd.DataFrame) -> None:
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from conftest_quant import bars_dataframe_to_daily_bars
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self._bars = bars_dataframe_to_daily_bars(df)
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def get_range(self, symbol, start, end):
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return [b for b in self._bars if b.symbol == symbol and start <= b.trade_date <= end]
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def get_range_many(self, symbols, start, end, adjust="none"):
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syms = set(symbols)
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return [b for b in self._bars if b.symbol in syms and start <= b.trade_date <= end]
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def latest_date(self, symbol):
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rows = [b.trade_date for b in self._bars if b.symbol == symbol]
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return max(rows) if rows else None
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@pytest.fixture()
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def daily_df() -> pd.DataFrame:
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drifts = {s: 0.006 - 0.0015 * i for i, s in enumerate(_SYMS)}
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return synthetic_daily(drifts, n=320) # 2024-01-01 起 ~320 交易日
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def _spec(**kw) -> ResearchSpec:
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base = dict(
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type="backtest",
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universe={"exclude_st": False, "min_listing_days": 0},
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factors=[{"name": "momentum_60", "weight": 1.0}],
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selection={"top_n": 2},
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rebalance="monthly",
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period=(date(2024, 5, 1), date(2024, 12, 31)),
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)
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base.update(kw)
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return ResearchSpec(**base)
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class TestSelectionBacktestConsistency:
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def test_rebalance_selection_equals_backtest_positions(self, daily_df) -> None:
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result = LocalEngine().run_backtest(daily_df, _spec())
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stocks = [
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Stock(symbol=s, name=f"测试{i}", list_date=date(1999, 1, 1)) for i, s in enumerate(_SYMS)
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]
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svc = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(daily_df))
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# 回测每个调仓日的实际持仓 → 与 select(as_of=该日) 的 TopN 候选一致
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by_date: dict[date, set[str]] = {}
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for p in result.positions:
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by_date.setdefault(p.date, set()).add(p.symbol)
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assert len(by_date) >= 5 # 月调仓多个时点
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for d, held in sorted(by_date.items()):
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res = svc.select(
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SelectionQuery(
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universe=_spec().universe,
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factors=[{"name": "momentum_60", "weight": 1.0}],
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top_n=2,
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as_of=d,
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)
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)
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picked = {c.symbol for c in res.candidates}
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assert picked == held, (
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f"as_of={d}: 选股 {sorted(picked)} ≠ 回测持仓 {sorted(held)}"
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)
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def test_rank_order_consistent(self, daily_df) -> None:
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"""排序方向也一致：select 返回顺序 == 回测 score 排序（通过持仓逐日验证序）。"""
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spec = _spec()
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result = LocalEngine().run_backtest(daily_df, spec)
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stocks = [
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Stock(symbol=s, name=f"测试{i}", list_date=date(1999, 1, 1)) for i, s in enumerate(_SYMS)
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]
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svc = SelectionService(_MemStockRepo(stocks), _MemDailyRepo(daily_df))
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by_date: dict[date, list[str]] = {}
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for p in result.positions:
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by_date.setdefault(p.date, []).append(p.symbol)
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# 只验证任一日的一致性集合（顺序由 TopK 权重决定，与评分排序一一对应）
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d, held = next(iter(by_date.items()))
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res = svc.select(
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SelectionQuery(
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universe=spec.universe,
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factors=[{"name": "momentum_60", "weight": 1.0}],
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top_n=len(held),
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as_of=d,
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)
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)
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assert [c.symbol for c in res.candidates] == sorted(
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held, key=lambda s: res.candidates[[x.symbol for x in res.candidates].index(s)].score,
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reverse=True,
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)
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