simon
fb6608193b
说明：本提交**不是本轮会话所做**，而是工作区里此前遗留的未提交改动。
为把历史分开，先单独提交它，再提交本轮会话的修改。
包含：
- Web 前端：web/index.html、web/app.js（统一 SPA，含回测/画像/Walk-forward 页面）
- 后端接口：web/server.py 路由、web/analysis.py（新增个股分析）
- 报告层：report/format.py（新增统一数字格式化 NumFmt）、
report/{backtest,profile,sensitivity,universe,walkforward}_report.py 接入 NumFmt、
report/renderer.py
- 股息率口径：factor/dividend_yield.py（毛刺消除 smooth_spikes）
- 筛选：universe/selector.py、universe/filters/dividend.py
- 绩效/敏感性：analysis/performance.py、analysis/sensitivity.py
- 部署：deploy/install-service.sh
- 测试：tests/test_format.py、tests/test_dividend_smoothing.py（新增）、
tests/test_web.py、tests/test_universe.py
提交时全量测试 403 项通过。
622 lines
24 KiB
Python
622 lines
24 KiB
Python
"""单位换算与筛选滤网测试。
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这里覆盖的都是**开发过程中真实踩到过**的坑，每个测试对应一次静默错误：
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1. ``total_mv`` 是万元而阈值配的是元 → 筛选结果为空（不报错）；
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2. 拿季报 ROE（年初至今累计）去比「5 年年均 ROE」→ 好公司全被误杀；
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3. 银行负债率天然 90%+ → 整个金融板块被误杀；
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4. 分红除权晚于 asof → 稳定分红公司被误判为「连续分红 0 年」。
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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 hdiv.data.units import (
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normalize_financial_panel,
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normalize_market_panel,
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pct_to_ratio,
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verify_market_units,
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vol_shou_to_shares,
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wan_to_shares,
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wan_to_yuan,
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)
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# ---------------------------------------------------------------------------
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# 单位换算
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# ---------------------------------------------------------------------------
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def test_wan_to_yuan() -> None:
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assert wan_to_yuan(pd.Series([1.0, 100.0])).tolist() == [1e4, 1e6]
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def test_wan_to_shares() -> None:
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assert wan_to_shares(pd.Series([125619.78])).iloc[0] == pytest.approx(1.2561978e9)
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def test_pct_to_ratio() -> None:
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assert pct_to_ratio(pd.Series([5.08])).iloc[0] == pytest.approx(0.0508)
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def test_vol_shou_to_shares() -> None:
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assert vol_shou_to_shares(pd.Series([100])).iloc[0] == 10000
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def test_normalize_market_panel_units() -> None:
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"""茅台 2024-06-28 实测值：总市值 1.84 万亿元，总股本 12.56 亿股。"""
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df = pd.DataFrame(
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{
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"symbol": ["600519.SH"],
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"close": [1467.39],
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"total_share": [125619.78], # 万股
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"total_mv": [184333208.97], # 万元
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"circ_mv": [184333208.97],
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"dv_ttm": [5.1720], # 百分数
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"turnover_rate": [0.25],
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}
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)
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out = normalize_market_panel(df)
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assert out["total_mv"].iloc[0] == pytest.approx(1.8433320897e12, rel=1e-6)
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assert out["total_share"].iloc[0] == pytest.approx(1.2561978e9, rel=1e-9)
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assert out["dv_ttm"].iloc[0] == pytest.approx(0.051720)
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assert out["_units"].iloc[0] == "yuan/shares/ratio"
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def test_normalize_market_panel_does_not_mutate_input() -> None:
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df = pd.DataFrame({"total_mv": [100.0], "total_share": [10.0]})
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before = df.copy()
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normalize_market_panel(df)
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pd.testing.assert_frame_equal(df, before)
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def test_normalize_financial_panel_converts_pct() -> None:
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df = pd.DataFrame({"roe": [15.13], "debt_to_assets": [90.23], "total_revenue": [1.78e11]})
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out = normalize_financial_panel(df)
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assert out["roe"].iloc[0] == pytest.approx(0.1513)
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assert out["debt_to_assets"].iloc[0] == pytest.approx(0.9023)
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assert out["total_revenue"].iloc[0] == 1.78e11, "金额列本就以元计，不得被换算"
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def test_verify_market_units_detects_correct_data() -> None:
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df = pd.DataFrame(
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{
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"close": [10.0, 20.0],
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"total_share": [1e9, 5e8],
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"total_mv": [1e10, 1e10],
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}
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)
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v = verify_market_units(df)
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assert v["checked"] == 2 and v["bad"] == 0
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assert v["median_ratio"] == pytest.approx(1.0)
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def test_identity_alone_cannot_detect_wan_vs_yuan() -> None:
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"""恒等式对「整体万元/元混淆」无效 —— 因为 元/股 × 万股 = 万元。
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这是一个容易误以为「有了恒等式检查就安全」的陷阱，必须显式记录：
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原始单位下比值仍然恰好是 1。
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"""
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raw = pd.DataFrame(
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{"close": [1467.39], "total_share": [125619.78], "total_mv": [184333208.97]}
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)
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v = verify_market_units(raw)
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assert v["identity_ok"] is True, "恒等式在原始单位下同样成立"
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assert v["unit_ok"] is False, "但绝对量级检查必须发现单位错误"
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assert v["detected_unit"] == "wan"
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def test_verify_market_units_accepts_normalized_yuan() -> None:
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norm = normalize_market_panel(
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pd.DataFrame(
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{"close": [1467.39], "total_share": [125619.78], "total_mv": [184333208.97]}
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)
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)
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v = verify_market_units(norm)
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assert v["unit_ok"] is True and v["identity_ok"] is True
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assert v["detected_unit"] == "yuan"
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def test_verify_market_units_detects_partial_conversion() -> None:
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"""只换算一个字段（常见疏漏）也必须被抓出来。"""
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df = pd.DataFrame(
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{"close": [10.0], "total_share": [1e9], "total_mv": [1e10 / 1e4]} # 市值漏换算
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)
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v = verify_market_units(df)
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assert v["bad"] == 1
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def test_verify_market_units_empty() -> None:
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assert verify_market_units(pd.DataFrame())["checked"] == 0
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# ---------------------------------------------------------------------------
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# 滤网：行业豁免
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# ---------------------------------------------------------------------------
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class _FakeRepo:
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def __init__(self, st: set[str] | None = None, susp: set[str] | None = None) -> None:
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self._st = st or set()
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self._susp = susp or set()
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def st_symbols(self, asof, include_delisting=True): # noqa: ANN001, ARG002
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return self._st
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def suspended_on(self, asof): # noqa: ANN001, ARG002
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return self._susp
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def _frame(**kwargs) -> pd.DataFrame:
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base = {
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"symbol": ["600036.SH", "601088.SH"],
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"name": ["招商银行", "中国神华"],
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"industry": ["银行", "煤炭开采"],
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"market": ["主板", "主板"],
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"exchange": ["SSE", "SSE"],
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"listed_years": [30.0, 17.0],
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"is_fresh": [True, True],
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"total_mv": [8.6e11, 8.8e11],
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"circ_mv": [7.0e11, 7.3e11],
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"avg_amount": [1e9, 8e8],
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"close": [34.19, 44.37],
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"debt_ratio": [0.902, 0.236],
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"total_hldr_eqy_exc_min_int": [3.5e11, 4.0e11],
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}
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base.update(kwargs)
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return pd.DataFrame(base)
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def test_risk_filter_exempts_banks_from_leverage() -> None:
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from hdiv.core.config import RiskFilterConfig
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from hdiv.universe.filters.risk import RiskFilter
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f = RiskFilter(RiskFilterConfig(max_debt_to_assets=0.80), exempt_leverage=["银行"])
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out = f.compute(_frame(), _FakeRepo(), date(2024, 6, 28))
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assert bool(out.passed.iloc[0]) is True, "银行必须豁免负债率上限"
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assert bool(out.passed.iloc[1]) is True, "低负债公司自然通过"
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assert out.values["600036.SH"]["debt_ratio_exempt"] is True
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def test_risk_filter_still_rejects_high_leverage_non_exempt() -> None:
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from hdiv.core.config import RiskFilterConfig
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from hdiv.universe.filters.risk import RiskFilter
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f = RiskFilter(RiskFilterConfig(max_debt_to_assets=0.80), exempt_leverage=["银行"])
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df = _frame(industry=["房地产", "煤炭开采"])
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out = f.compute(df, _FakeRepo(), date(2024, 6, 28))
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assert bool(out.passed.iloc[0]) is False
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assert "资产负债率" in out.reasons["600036.SH"]
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def test_quality_filter_exempts_banks_from_fcf_and_leverage() -> None:
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from hdiv.core.config import QualityFilterConfig
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from hdiv.universe.filters.quality import FinancialQualityFilter
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cfg = QualityFilterConfig(min_ocf_to_profit=0.60, max_debt_to_assets=0.80)
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f = FinancialQualityFilter(cfg, exempt_leverage=["银行"], exempt_fcf=["银行"])
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df = _frame(roe_avg=[0.1513, 0.1406], ocf_to_profit=[-0.03, 1.80])
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out = f.compute(df, _FakeRepo(), date(2024, 6, 28))
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assert bool(out.passed.iloc[0]) is True, "银行豁免 FCF 与负债率"
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def test_quality_filter_uses_annual_average_not_quarterly() -> None:
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"""季报 ROE 3.47% 不该被拿去比年均 8% 的阈值。"""
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from hdiv.core.config import QualityFilterConfig
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from hdiv.universe.filters.quality import FinancialQualityFilter
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cfg = QualityFilterConfig(min_roe_5y_avg=0.08, min_ocf_to_profit=None)
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f = FinancialQualityFilter(cfg)
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df = _frame(
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industry=["白酒", "煤炭开采"],
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roe=[0.0347, 0.0380], # 季报累计值（会被 avg 覆盖）
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roe_avg=[0.1513, 0.1406], # 年报 5 年平均
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)
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out = f.compute(df, _FakeRepo(), date(2024, 6, 28))
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assert bool(out.passed.iloc[0]) is True, "应使用 roe_avg 而非季报 roe"
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assert out.values["600036.SH"]["roe"] == pytest.approx(0.1513)
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def test_quality_filter_falls_back_to_latest_when_no_average() -> None:
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from hdiv.core.config import QualityFilterConfig
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from hdiv.universe.filters.quality import FinancialQualityFilter
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cfg = QualityFilterConfig(min_roe_5y_avg=0.08, min_ocf_to_profit=None)
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f = FinancialQualityFilter(cfg)
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df = _frame(industry=["白酒", "煤炭开采"], roe=[0.20, 0.03]) # 无 roe_avg 列
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out = f.compute(df, _FakeRepo(), date(2024, 6, 28))
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assert bool(out.passed.iloc[0]) is True
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assert bool(out.passed.iloc[1]) is False
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# ---------------------------------------------------------------------------
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# 分红连续性：一年宽限期
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# ---------------------------------------------------------------------------
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def _div(symbol: str, end_year: int, ex_year: int, dps: float = 1.0, month: int = 7) -> dict:
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return {
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"symbol": symbol,
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"end_date": date(end_year, 12, 31),
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"imp_ann_date": date(ex_year, month - 1 if month > 1 else 12, 1),
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"div_proc": "实施",
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"cash_div_tax": dps,
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"cash_div": dps,
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"stk_div": None,
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"base_share": 10000.0,
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"ex_date": date(ex_year, month, 15),
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}
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def test_continuity_within_target_year() -> None:
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"""FY2023 分红已在 2024-05 除权 → target=2023，连续 5 年。"""
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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recs = {2023: 2024, 2022: 2023, 2021: 2022, 2020: 2021, 2019: 2020}
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rows = [_div("600036.SH", fy, ex, month=5) for fy, ex in recs.items()]
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s = DividendFilter._stats(rows, target_year=2023, asof=date(2024, 6, 28),
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cfg=DividendFilterConfig())
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assert s["dividend_continuity_years"] == 5
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assert s["continuity_grace_used"] is False
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def test_continuity_grace_when_ex_date_lags() -> None:
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"""神华实测情形：FY2023 分红要 2024-07 才除权，asof=2024-06-28 时不可见。
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此时必须用一年宽限期从 FY2022 起算，而不是判定为「中断分红」。
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"""
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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rows = [_div("601088.SH", fy, fy + 1, month=7) for fy in (2022, 2021, 2020, 2019, 2018)]
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s = DividendFilter._stats(rows, target_year=2023, asof=date(2024, 6, 28),
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cfg=DividendFilterConfig())
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assert s["latest_dividend_year"] == 2022
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assert s["continuity_start_year"] == 2022
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assert s["continuity_grace_used"] is True
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assert s["dividend_continuity_years"] == 5, "不应因为除权晚而误判中断"
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def test_continuity_zero_when_genuinely_stopped() -> None:
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"""最近可见分红比应考核财年早两年以上 → 视为真的中断。"""
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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rows = [_div("000002.SZ", fy, fy + 1) for fy in (2019, 2018, 2017, 2016, 2015)]
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s = DividendFilter._stats(rows, target_year=2023, asof=date(2024, 6, 28),
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cfg=DividendFilterConfig())
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assert s["dividend_continuity_years"] == 0
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def test_continuity_breaks_on_gap() -> None:
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"""有断档：2022 有、2021 无 → 连续 1 年。"""
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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rows = [_div("X.SZ", fy, fy + 1) for fy in (2022, 2020, 2019)]
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s = DividendFilter._stats(rows, target_year=2023, asof=date(2024, 6, 28),
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cfg=DividendFilterConfig())
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assert s["dividend_continuity_years"] == 1
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def test_ttm_dps_only_counts_ex_date_in_window() -> None:
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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rows = [
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_div("X.SZ", 2023, 2024, dps=1.5, month=4), # 窗口内
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_div("X.SZ", 2022, 2023, dps=1.2, month=7), # 窗口内（>2023-06-28）
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_div("X.SZ", 2021, 2022, dps=1.0, month=7), # 窗口外
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]
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s = DividendFilter._stats(rows, target_year=2023, asof=date(2024, 6, 28),
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cfg=DividendFilterConfig())
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assert s["ttm_dps"] == pytest.approx(2.7)
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def test_shift_year_handles_leap_day() -> None:
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from hdiv.universe.filters.dividend import _shift_year
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assert _shift_year(date(2024, 2, 29), -1) == date(2023, 2, 28)
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assert _shift_year(date(2024, 6, 28), -1) == date(2023, 6, 28)
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def test_target_year_uses_latest_annual_report() -> None:
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"""最新年报为 FY2023 时，考核目标年应为 2023。"""
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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df = pd.DataFrame(
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{
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"symbol": ["A.SH", "B.SH"],
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"fin_end_date": [date(2024, 3, 31), date(2023, 12, 31)],
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}
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)
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t = DividendFilter._target_years(df, date(2024, 6, 28))
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# 能看到 2024Q1 报，说明 FY2023 年报必然已披露 → 目标年 2023
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assert t["A.SH"] == 2023, "有 2024Q1 报 → FY2023 年报已出"
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assert t["B.SH"] == 2023, "有 FY2023 年报 → 目标是 2023"
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df2 = pd.DataFrame({"symbol": ["C.SH"], "fin_end_date": [date(2023, 9, 30)]})
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assert DividendFilter._target_years(df2, date(2024, 6, 28))["C.SH"] == 2022, (
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"只看到 2023Q3 → FY2023 年报未出，退回到 2022"
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)
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def test_payout_ratio_uses_base_share() -> None:
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"""总现金分红 = 每股分红 × 基准股本（万股→股）。"""
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from hdiv.core.config import DividendFilterConfig
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from hdiv.universe.filters.dividend import DividendFilter
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rows = [
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{
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"symbol": "X.SH", "end_date": date(2023, 12, 31), "ex_date": date(2024, 6, 1),
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"div_proc": "实施", "cash_div_tax": 2.0, "base_share": 10000.0, # 1 亿股
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}
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]
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row = pd.Series({"n_income_attr_p": 4e8, "free_cashflow": 8e8})
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out = DividendFilter._payout_and_cover(rows, row, date(2024, 6, 28),
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DividendFilterConfig())
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assert out["total_cash_dividend"] == pytest.approx(2.0 * 10000.0 * 1e4)
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assert out["payout_ratio"] == pytest.approx(0.5)
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# 总现金分红 = 2.0 元 × 10000 万股 × 1e4 = 2e8 元；FCF 8e8 → 覆盖 4 倍
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assert out["fcf_dividend_cover"] == pytest.approx(4.0)
|
||
|
||
|
||
def test_fcf_fallback_when_tushare_missing() -> None:
|
||
from hdiv.core.config import DividendFilterConfig
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
rows = [{
|
||
"symbol": "X.SH", "end_date": date(2023, 12, 31), "ex_date": date(2024, 6, 1),
|
||
"div_proc": "实施", "cash_div_tax": 1.0, "base_share": 1000.0,
|
||
}]
|
||
row = pd.Series({
|
||
"n_income_attr_p": 5e6, "free_cashflow": None,
|
||
"n_cashflow_act": 1e7, "c_pay_dist_dpcp_int_exp": 2e6,
|
||
})
|
||
out = DividendFilter._payout_and_cover(rows, row, date(2024, 6, 28),
|
||
DividendFilterConfig())
|
||
assert out["fcf_source"] == "ocf_minus_dist"
|
||
assert out["free_cashflow"] == pytest.approx(8e6)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 滤网结果索引契约（曾经把 DataFrame 索引当成 symbol 用的真实 bug）
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def test_filter_outcome_index_is_dataframe_index_not_symbol() -> None:
|
||
"""滤网的 passed 索引必须与传入 DataFrame 的索引一致。
|
||
|
||
选择器据此用 ``live.at[i, "symbol"]`` 映射；
|
||
若误把索引当 symbol，会导致「全部淘汰」（本项目开发中确实发生过）。
|
||
"""
|
||
from hdiv.core.config import MarketFilterConfig
|
||
from hdiv.universe.filters.market import MarketFilter
|
||
|
||
df = _frame()
|
||
df.index = [10, 20] # 非默认索引
|
||
f = MarketFilter(MarketFilterConfig(min_market_cap=1e10))
|
||
out = f.compute(df, _FakeRepo(), date(2024, 6, 28))
|
||
assert list(out.passed.index) == [10, 20]
|
||
assert set(out.passed.values) <= {True, False}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 数据缺失策略（安全边际策略的关键取舍）
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def _div_cfg(**kw):
|
||
from hdiv.core.config import DividendFilterConfig
|
||
|
||
base = {
|
||
"min_dividend_yield": None,
|
||
"min_continuous_years": 0,
|
||
"min_dividend_years_in_window": 0,
|
||
"max_payout_ratio": None,
|
||
"require_positive_fcf": True,
|
||
"min_fcf_dividend_cover": None,
|
||
}
|
||
base.update(kw)
|
||
return DividendFilterConfig(**base)
|
||
|
||
|
||
def test_missing_fcf_passes_by_default() -> None:
|
||
"""默认宽松：数据缺失放行，避免因未同步而误杀。"""
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
cfg = _div_cfg(on_missing_data="pass")
|
||
stats = {"free_cashflow": None, "dividend_continuity_years": 0,
|
||
"dividend_years_in_window": 0, "dividend_yield": 0.05}
|
||
assert DividendFilter._reject_reason(stats, cfg) is None
|
||
|
||
|
||
def test_missing_fcf_rejected_when_strict() -> None:
|
||
"""严格模式：无法验证现金流即淘汰 —— 忠于「安全边际」的策略逻辑。"""
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
cfg = _div_cfg(on_missing_data="reject")
|
||
stats = {"free_cashflow": None, "dividend_continuity_years": 0,
|
||
"dividend_years_in_window": 0, "dividend_yield": 0.05}
|
||
why = DividendFilter._reject_reason(stats, cfg)
|
||
assert why is not None and "缺失" in why
|
||
|
||
|
||
def test_negative_fcf_rejected_in_both_modes() -> None:
|
||
"""FCF 为负时两种模式都必须淘汰 —— 宽松不等于放行已知风险。"""
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
stats = {"free_cashflow": -1e8, "dividend_continuity_years": 0,
|
||
"dividend_years_in_window": 0, "dividend_yield": 0.05}
|
||
for mode in ("pass", "reject"):
|
||
why = DividendFilter._reject_reason(stats, _div_cfg(on_missing_data=mode))
|
||
assert why is not None and "负" in why, f"{mode} 模式必须拒绝负 FCF"
|
||
|
||
|
||
def test_missing_coverage_strict_only() -> None:
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
stats = {"free_cashflow": 1e8, "fcf_dividend_cover": None,
|
||
"dividend_continuity_years": 0, "dividend_years_in_window": 0,
|
||
"dividend_yield": 0.05}
|
||
assert DividendFilter._reject_reason(
|
||
stats, _div_cfg(min_fcf_dividend_cover=1.0, on_missing_data="pass")
|
||
) is None
|
||
assert DividendFilter._reject_reason(
|
||
stats, _div_cfg(min_fcf_dividend_cover=1.0, on_missing_data="reject")
|
||
) is not None
|
||
|
||
|
||
def test_on_missing_data_invalid_value_rejected() -> None:
|
||
from hdiv.core.errors import SchemaValidationError
|
||
|
||
with pytest.raises(Exception) as ei:
|
||
_div_cfg(on_missing_data="maybe")
|
||
assert "on_missing_data" in str(ei.value) or "Input should be" in str(ei.value)
|
||
del SchemaValidationError
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 分红支付率必须与分红**同财年**（真实踩到的错误）
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def test_payout_uses_same_fiscal_year_not_latest_quarter() -> None:
|
||
"""回归：曾用「FY2023 分红 ÷ 2024Q1 净利润」算出 230.9% 的荒谬支付率。
|
||
|
||
正确口径下美的集团 FY2023 为：分红 207.8 亿 ÷ 净利 337.2 亿 = 61.63%。
|
||
"""
|
||
from hdiv.core.config import DividendFilterConfig
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
recs = [{
|
||
"symbol": "000333.SZ", "end_date": date(2023, 12, 31),
|
||
"ex_date": date(2024, 5, 15), "div_proc": "实施",
|
||
"cash_div_tax": 3.0, "base_share": 692675.9241,
|
||
}]
|
||
# 同财年（FY2023）财务
|
||
fy_row = pd.Series({
|
||
"n_income_attr_p": 3.372e10, "free_cashflow": 7.16e10,
|
||
})
|
||
out = DividendFilter._payout_and_cover(
|
||
recs, fy_row, date(2024, 6, 28), DividendFilterConfig()
|
||
)
|
||
assert out["financial_year"] == 2023
|
||
assert out["payout_ratio"] == pytest.approx(207.8 / 337.2, abs=0.01)
|
||
assert out["payout_basis"] == "same_fiscal_year"
|
||
|
||
|
||
def test_payout_not_computed_without_same_year_row() -> None:
|
||
"""缺少同财年财务时必须返回「不可得」，而不是退回到最新季报。"""
|
||
from hdiv.core.config import DividendFilterConfig
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
recs = [{
|
||
"symbol": "X.SZ", "end_date": date(2023, 12, 31),
|
||
"ex_date": date(2024, 5, 15), "div_proc": "实施",
|
||
"cash_div_tax": 1.0, "base_share": 10000.0,
|
||
}]
|
||
out = DividendFilter._payout_and_cover(
|
||
recs, None, date(2024, 6, 28), DividendFilterConfig()
|
||
)
|
||
assert out["payout_ratio"] is None
|
||
assert out["fcf_dividend_cover"] is None
|
||
assert out["payout_basis"] == "missing_same_year_financials"
|
||
|
||
|
||
def test_total_cash_dividend_uses_base_share() -> None:
|
||
"""base_share 是**万股**，漏乘 1e4 会把支付率缩小一万倍。"""
|
||
from hdiv.core.config import DividendFilterConfig
|
||
from hdiv.universe.filters.dividend import DividendFilter
|
||
|
||
recs = [{
|
||
"symbol": "X.SZ", "end_date": date(2023, 12, 31),
|
||
"ex_date": date(2024, 5, 15), "div_proc": "实施",
|
||
"cash_div_tax": 2.0, "base_share": 10000.0, # 1 亿股
|
||
}]
|
||
row = pd.Series({"n_income_attr_p": 4e8, "free_cashflow": 8e8})
|
||
out = DividendFilter._payout_and_cover(
|
||
recs, row, date(2024, 6, 28), DividendFilterConfig()
|
||
)
|
||
assert out["total_cash_dividend"] == pytest.approx(2.0 * 10000.0 * 1e4)
|
||
assert out["payout_ratio"] == pytest.approx(0.5)
|
||
|
||
|
||
def test_dividend_records_include_base_share() -> None:
|
||
"""回归：repo.dividend_records 曾漏选 base_share，
|
||
|
||
导致 payout_ratio 与 fcf_dividend_cover 在全库范围内静默为 NULL，
|
||
进而使 max_payout_ratio / min_fcf_dividend_cover 两个筛选条件从未生效。
|
||
"""
|
||
import inspect
|
||
|
||
from hdiv.data.repo import Repo
|
||
|
||
src = inspect.getsource(Repo.dividend_records)
|
||
assert "base_share" in src, "dividend_records 必须选出 base_share"
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 重跑覆盖同一条记录
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def test_universe_run_id_is_deterministic() -> None:
|
||
"""回归：run_id 不得含时间戳，否则同参数重跑会不断累积重复记录。
|
||
|
||
早期实现把 datetime.now() 编进指纹，同一 asof 最多累积了 11 条内容相同的记录。
|
||
现在的语义是「同一份配置 + 同一时点 → 同一个 run_id → 重跑原地覆盖」。
|
||
"""
|
||
import inspect
|
||
|
||
from hdiv.universe import selector
|
||
|
||
src = inspect.getsource(selector.UniverseSelector)
|
||
i = src.find("run_id = stable_id(")
|
||
assert i != -1, "未找到 run_id 生成处"
|
||
# 取到该语句结束的分号行（不能用第一个 ')'，那会截断在 config_hash(self.config) 里）
|
||
end = src.find("\n )", i)
|
||
assert end != -1, "未找到 run_id 语句结尾"
|
||
block = src[i:end]
|
||
assert "datetime.now" not in block, f"run_id 指纹仍含时间戳：{block}"
|
||
for must in ("config_hash", "effective", "self.config.name"):
|
||
assert must in block, f"run_id 指纹缺少 {must}：{block}"
|
||
|
||
|
||
@pytest.mark.db
|
||
def test_universe_rerun_overwrites_same_record() -> None:
|
||
"""同参数重跑不新增记录，且成员行数等于候选数（无重复堆积）。"""
|
||
from hdiv.core.config import load_config
|
||
from hdiv.data import db
|
||
from hdiv.data.sync.base import stable_id
|
||
|
||
db.load_dotenv_once()
|
||
cfg = load_config("datasource")
|
||
df = db.read_sql(
|
||
"SELECT r.run_id, r.candidate_count, r.asof_date, r.config_hash, r.name, "
|
||
" COUNT(m.id) AS member_rows "
|
||
"FROM hd_universe_run r LEFT JOIN hd_universe_member m ON m.run_id = r.run_id "
|
||
"GROUP BY r.run_id HAVING member_rows > 0 "
|
||
"ORDER BY r.created_at DESC LIMIT 5",
|
||
cfg=cfg,
|
||
)
|
||
if df.empty:
|
||
pytest.skip("没有筛选记录")
|
||
checked = 0
|
||
for _, r in df.iterrows():
|
||
expect = stable_id("universe", r["name"], str(r["asof_date"]), r["config_hash"])
|
||
if r["run_id"] != expect:
|
||
continue # 确定化之前的历史记录，跳过
|
||
checked += 1
|
||
assert int(r["member_rows"]) == int(r["candidate_count"]), (
|
||
f"run_id={r['run_id'][:10]} 成员行数 {r['member_rows']} "
|
||
f"应等于候选数 {r['candidate_count']}（出现重复堆积）"
|
||
)
|
||
assert checked > 0, "未找到确定化之后生成的筛选记录，无法验证"
|