Simon
861a4051ca
- Job kind=selection：executor 分支用 SelectionService（spec=SelectionQuery），结果 落 result_json、自动归档 Experiment（summary：as_of + 选出 N）；/api/jobs 与 /api/experiments 的 result 解码支持 SelectionResult - POST /api/selections/jobs：提交 SelectionQuery 为异步 Job（BackgroundTasks/subprocess） - Web 选股页新增「异步（全市场）」按钮：提交 Job → waitJob 轮询结果（解决同步 60s+） - tests/test_selection_job.py：executor 执行归档（SelectionResult/Experiment kind）、 API 提交→轮询→结果与实验列表；全量 pytest + tsc 通过
165 lines
6.5 KiB
Python
165 lines
6.5 KiB
Python
"""M9-6 Bar Replay 测试：线性重放与回测调仓意图一致、范围约束、未来函数。"""
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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.api import deps
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from app.application.services.replay_service import ReplayService
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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.domain.entities.signal import SignalRules
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from app.infrastructure.persistence.sqlalchemy.base import Base
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from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
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SqlAlchemyDailyBarRepository,
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SqlAlchemyStockRepository,
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)
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from app.main import app
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from app.quant.engine import LocalEngine
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from fastapi.testclient import TestClient
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from sqlalchemy import create_engine
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from sqlalchemy.orm import sessionmaker
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from conftest_quant import bars_dataframe_to_daily_bars, synthetic_daily
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_SYMS = ["600000.SH", "600001.SH", "600002.SH", "600003.SH", "600004.SH"]
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def _query(symbols=_SYMS, as_of=None) -> SelectionQuery:
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return SelectionQuery(
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universe={"exclude_st": False, "min_listing_days": 0, "symbols": list(symbols)},
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factors=[{"name": "momentum_60", "weight": 1.0}],
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top_n=2,
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as_of=as_of,
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)
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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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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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@pytest.fixture()
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def df() -> pd.DataFrame:
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return synthetic_daily({s: 0.006 - 0.0015 * i for i, s in enumerate(_SYMS)}, n=320)
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class TestReplayConsistency:
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def test_replay_matches_backtest_selection(self, df) -> None:
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stocks = [Stock(symbol=s, name=f"测试{i}", list_date=date(1999, 1, 1))
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for i, s in enumerate(_SYMS)]
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svc = ReplayService(_MemStockRepo(stocks), _MemDailyRepo(df))
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start, end = date(2024, 5, 1), date(2024, 8, 31)
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res = svc.replay(_query(), SignalRules(), start, end, top_n=2)
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spec = ResearchSpec(
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type="backtest",
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universe={"exclude_st": False, "min_listing_days": 0, "symbols": _SYMS},
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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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bt = LocalEngine().run_backtest(df, spec)
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by_date: dict[date, set[str]] = {}
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for p in bt.selection_history:
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if start <= p.date <= end:
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by_date.setdefault(p.date, set()).add(p.symbol)
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assert res.days and by_date
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replay_by_date = {d.as_of: {t.symbol for t in d.top} for d in res.days}
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for d, syms in by_date.items():
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assert replay_by_date[d] == syms, f"as_of={d} 重放 {replay_by_date.get(d)} ≠ 回测 {syms}"
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def test_bounds(self, df) -> None:
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stocks = [Stock(symbol=s, name=f"测试{i}", list_date=date(1999, 1, 1))
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for i, s in enumerate(_SYMS)]
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svc = ReplayService(_MemStockRepo(stocks), _MemDailyRepo(df))
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with pytest.raises(ValueError):
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svc.replay(_query(symbols=[]), SignalRules(), date(2024, 5, 1), date(2024, 8, 31))
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with pytest.raises(ValueError):
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svc.replay(_query(), SignalRules(), date(2024, 1, 1), date(2024, 12, 31)) # >90 交易日
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def test_future_does_not_leak_into_early_frames(self, df) -> None:
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"""把 B 股在区间后段设为暴涨：前段重放帧不应出现 B（as_of 截断）。"""
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dates = sorted(df["trade_date"].unique())
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mid = dates[len(dates) // 2]
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b = _SYMS[1]
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for d in dates:
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if d > mid:
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mask = (df["symbol"] == b) & (df["trade_date"] == d)
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df.loc[mask, "close"] = df.loc[mask, "close"] * 1.03 # 后段持续暴涨
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stocks = [Stock(symbol=s, name=f"测试{i}", list_date=date(1999, 1, 1))
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for i, s in enumerate(_SYMS)]
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svc = ReplayService(_MemStockRepo(stocks), _MemDailyRepo(df))
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res = svc.replay(_query(), SignalRules(), date(2024, 2, 1), date(2024, 4, 30), top_n=2)
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assert res.days
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for d in res.days[: 5]: # 早段帧
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assert all(t.symbol != b for t in d.top)
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class TestReplayApi:
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@pytest.fixture()
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def client(self, tmp_path, df):
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engine = create_engine(f"sqlite:///{tmp_path / 'replay.db'}", future=True)
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Base.metadata.create_all(engine)
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Session = sessionmaker(bind=engine, expire_on_commit=False)
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with Session() as session:
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SqlAlchemyStockRepository(session).upsert_many(
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[Stock(symbol=s, name=f"测试{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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SqlAlchemyDailyBarRepository(session).upsert_many(bars_dataframe_to_daily_bars(df))
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session.commit()
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def _override():
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with Session() as s:
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yield s
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app.dependency_overrides[deps.get_session] = _override
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with TestClient(app) as c:
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yield c
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app.dependency_overrides.clear()
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def test_post_replay(self, client) -> None:
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body = {
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"query": {
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"universe": {"exclude_st": False, "min_listing_days": 0, "symbols": _SYMS},
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"factors": [{"name": "momentum_60", "weight": 1}],
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"top_n": 2,
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},
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"rules": {"buy_rank_threshold": 1, "max_output_rank": 3},
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"start": "2024-05-01",
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"end": "2024-06-15",
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"top_n": 2,
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}
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resp = client.post("/api/replays", json=body)
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assert resp.status_code == 200
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data = resp.json()
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assert len(data["days"]) > 10
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assert data["days"][0]["top"] and data["days"][0]["counts"]["sell"] >= 0
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# 无白名单 → 400
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bad = {**body, "query": {**body["query"], "universe": {"exclude_st": False, "min_listing_days": 0}}}
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assert client.post("/api/replays", json=bad).status_code == 400
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