Simon
23972e7063
汇总三轮未提交的开发（每轮均在本机 MariaDB + 真实浏览器上验证）：
1) 股息率案例（全市场股息率最高 n 只，默认 20，每 m 月择股）
- 新增日频估值表 daily_basic + 迁移；股息率因子（dv_ratio / dividend_yield / TTM）
- 名称历史表 stock_name_history：剔除 ST 按**择股日当时名称**判定，消除
「曾高股息后 ST」的股息陷阱（实测 3.70pp 偏差）
- 区间择股/调仓双周期（m 择股 / y 调仓）、指数成分与白名单、停牌近似剔除
- 复权因子口径核对（4,164,742 行、缺失 0.0%）、收盘价成交与涨跌停拦单
- 案例实测：2020-01-01~2026-09-04 总收益 +24.86%（年化 3.52%、回撤 -28.58%）
2) 策略库与前端统一
- strategy 表 + CRUD/PUT 原地更新 + `describe_strategy` 按 spec 真实推导
「一句话说明 + 计算公式 + 执行步骤 + 注意事项」（与引擎实执行规则同源）
- 任何出现股票代码处都成对显示名称且可点击进个股页
- 全站图表基座统一 TradingView Lightweight Charts（ECharts 依赖、
锁文件、组件与文档标注一并清除），买卖点标记只落在真实交易日上
3) 回测存档完整化（可往复查看）
- 同步端点（POST /api/backtests、/api/factor-tests）此前完全不落库 → 现在同样归档，
归档 id 经响应头 X-Experiment-Id 返回（不破坏 response_model）
- data_version 首次真实写入（数据快照指纹：最新交易日 + 各表规模）
- 个股收益曲线默认**全量保存**（此前硬截断 60 只）；超出体积预算才裁剪，
并写 archive_meta（机器可读）+ unimplemented（人可读）如实标注
- 列表 kind/q 过滤 + X-Total-Count（此前 limit=50 静默截断）、DELETE 归档
- 只读归档页 /experiments/{id}（Server Component，SSR 直出**选股条件**与
**交易执行依据**）；结果视图按 kind 分发（backtest/factor_test/selection），
非回测归档不套用回测口径
- 新增 CLI：prune_experiments（保留策略，默认 dry-run）、
restore_experiment_from_job（从 Job 副本按原 id 重建被删的历史归档，默认 dry-run）
门禁：pytest 388 passed、ruff All checks passed、tsc 0 错误、图表单测 7 passed、
next build 成功、契约脚本 verify_strategy_workspace 59/59（含按 kind 逐类验证归档页）。
175 lines
6.9 KiB
Python
175 lines
6.9 KiB
Python
"""复权折算（v3 §20.5）测试：SQL 侧按 adjust_factor 折算价格列。
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覆盖：
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- hfq：price × factor（后复权）
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- qfq：price × factor / 该股最新 factor（前复权，归一）
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- volume/amount 不折算
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- 因子缺失按 1.0 兜底，并由 count_price_adjust_gaps 如实统计
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- hfq 与 qfq 的**收益率序列完全一致**（仅差一个常数倍），这是复权口径自洽性的关键断言
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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 pytest
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from app.domain.entities.market import AdjustFactor, DailyBar
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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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)
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from sqlalchemy import create_engine
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from sqlalchemy.orm import Session
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_D = [date(2024, 6, 3), date(2024, 6, 4), date(2024, 6, 5)]
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def _bar(day: date, close: str, symbol: str = "600519.SH") -> DailyBar:
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return DailyBar(
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symbol=symbol,
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trade_date=day,
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source="tushare",
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adjust="none",
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open=Decimal(close),
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high=Decimal(close),
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low=Decimal(close),
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close=Decimal(close),
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volume=Decimal("1000"),
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amount=Decimal("100000"),
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)
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def _factor(day: date, factor: str, symbol: str = "600519.SH") -> AdjustFactor:
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return AdjustFactor(symbol=symbol, trade_date=day, factor=Decimal(factor), source="tushare")
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@pytest.fixture
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def session(tmp_path) -> Session:
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engine = create_engine(f"sqlite:///{tmp_path / 'adjprice.db'}", future=True)
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Base.metadata.create_all(engine)
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with Session(engine) as s:
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repo = SqlAlchemyDailyBarRepository(s)
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# 100 → 除权前 1.0；110 → 之后因子 1.1（模拟一次分红/送股）
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repo.upsert_many([_bar(_D[0], "100"), _bar(_D[1], "110"), _bar(_D[2], "121")])
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s.add_all(
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[
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_factor_model(_D[0], "1.0"),
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_factor_model(_D[1], "1.1"),
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_factor_model(_D[2], "1.1"),
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]
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)
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s.commit()
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yield s
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def _factor_model(day: date, factor: str, symbol: str = "600519.SH"):
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from app.infrastructure.persistence.sqlalchemy.models.market import AdjustFactorModel
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return AdjustFactorModel(symbol=symbol, trade_date=day, factor=Decimal(factor))
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def _stream(session, price_adjust: str, columns=("close",)) -> dict[date, tuple]:
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repo = SqlAlchemyDailyBarRepository(session)
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rows = list(
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repo.stream_range_many_columns(
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["600519.SH"], _D[0], _D[2], list(columns),
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adjust="none", price_adjust=price_adjust,
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)
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)
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return {date.fromisoformat(r[1]): r[2:] for r in rows}
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class TestAdjustedPrices:
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def test_hfq_multiplies_by_factor(self, session) -> None:
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out = _stream(session, "hfq")
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assert out[_D[0]] == (100.0,) # 100 × 1.0
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assert out[_D[1]] == pytest.approx((121.0,)) # 110 × 1.1
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assert out[_D[2]] == pytest.approx((133.1,)) # 121 × 1.1
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def test_qfq_normalizes_by_latest_factor(self, session) -> None:
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out = _stream(session, "qfq")
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assert out[_D[0]] == pytest.approx((100 / 1.1,)) # 100 × 1.0 / 1.1
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assert out[_D[1]] == pytest.approx((110.0,)) # 110 × 1.1 / 1.1
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assert out[_D[2]] == pytest.approx((121.0,))
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def test_volume_and_amount_not_adjusted(self, session) -> None:
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out = _stream(session, "hfq", columns=("close", "volume", "amount"))
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close, volume, amount = out[_D[2]]
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assert close == pytest.approx(133.1)
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assert volume == 1000.0 and amount == 100000.0 # 不随复权缩放
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def test_none_is_raw(self, session) -> None:
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out = _stream(session, "none")
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assert out[_D[0]] == (100.0,) and out[_D[2]] == (121.0,)
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def test_hfq_and_qfq_give_identical_returns(self, session) -> None:
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hfq, qfq = _stream(session, "hfq"), _stream(session, "qfq")
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for a, b in zip(_D, _D[1:], strict=False):
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r_hfq = hfq[b][0] / hfq[a][0]
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r_qfq = qfq[b][0] / qfq[a][0]
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assert r_hfq == pytest.approx(r_qfq, rel=1e-12)
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def test_invalid_price_adjust_rejected(self, session) -> None:
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repo = SqlAlchemyDailyBarRepository(session)
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with pytest.raises(ValueError, match="复权"):
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list(
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repo.stream_range_many_columns(
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["600519.SH"], _D[0], _D[2], ["close"], price_adjust="bad"
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)
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)
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class TestAdjustGapReporting:
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def test_missing_factor_falls_back_and_is_reported(self, session) -> None:
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repo = SqlAlchemyDailyBarRepository(session)
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# 追加一行无因子的行情（模拟 25 只蓝筹 2020-2022 缺因子的情形）
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repo.upsert_many([_bar(date(2024, 6, 6), "200")])
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session.commit()
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out = dict(
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(date.fromisoformat(r[1]), r[2])
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for r in repo.stream_range_many_columns(
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["600519.SH"], _D[0], date(2024, 6, 6), ["close"], price_adjust="hfq"
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)
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)
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assert out[date(2024, 6, 6)] == 200.0 # 缺因子 → 系数 1.0（未折算）
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total, missing = repo.count_price_adjust_gaps(
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["600519.SH"], _D[0], date(2024, 6, 6)
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)
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assert total == 4 and missing == 1
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def test_no_gap_when_all_covered(self, session) -> None:
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repo = SqlAlchemyDailyBarRepository(session)
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total, missing = repo.count_price_adjust_gaps(["600519.SH"], _D[0], _D[2])
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assert total == 3 and missing == 0
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def test_empty_symbols(self, session) -> None:
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repo = SqlAlchemyDailyBarRepository(session)
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assert repo.count_price_adjust_gaps([], _D[0], _D[2]) == (0, 0)
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class TestQfqMissingFactor:
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"""qfq 缺因子行不得被错误缩放。
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回归用例：`coalesce(factor,1)/latest` 会把缺口行缩放到 `1/latest`
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（latest=5 → 121/5=24.2，凭空 −80% 单日跌幅），正确写法必须把 COALESCE
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放在最外层，使缺口行保持原始价（口径：缺失因子按 1.0 兜底、不折算）。
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"""
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def test_qfq_gap_row_is_not_rescaled(self, session) -> None:
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from app.infrastructure.persistence.sqlalchemy.models.market import AdjustFactorModel
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session.query(AdjustFactorModel).delete() # 清掉 fixture 的 1.0/1.1/1.1
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session.add_all([_factor_model(_D[0], "5.0"), _factor_model(_D[1], "5.0")])
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session.commit() # 第 3 天无因子 → 缺口
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out = _stream(session, "qfq")
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# 有因子的行：price × factor / max(factor=5.0) → 等于原始价
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assert out[_D[0]] == pytest.approx((100.0,))
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assert out[_D[1]] == pytest.approx((110.0,))
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# 缺口行：保持原始价（若被错误归一则为 121/5 = 24.2）
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assert out[_D[2]] == pytest.approx((121.0,))
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repo = SqlAlchemyDailyBarRepository(session)
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total, missing = repo.count_price_adjust_gaps(["600519.SH"], _D[0], _D[2])
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assert (total, missing) == (3, 1)
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