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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
4ee55e40c6
后端 - 新增 services/regime_builder.py：5 档日级市场状态分级（strong/lean_strong/range/lean_weak/weak） - 新增 api/regime.py：5 个 regime 查询接口 - daily_pipeline 接入 regime 构建（step 2.6，soft-fail + 双检测自愈） - 回测 strategy.py 新增 regime_filter 过滤：采用 T-1 防未来函数，仅作用于入场信号 在 composite / matrix_native / prepared 三处注入 entry_time_mask - backtest API 透传 regime_filter（REST + SSE）；_make_job_key 纳入 regime_filter 以隔离缓存 - 兼容 _RepoStub 测试夹具（store 属性缺失场景） 前端 - 新增 Regime 页面、路由、导航入口、api 类型与 queryKeys - 叠加策略回测页新增 regime 过滤器控件 测试 - 新增 tests/test_regime_builder.py（18 项），覆盖分级逻辑、T-1 防未来函数、空值降级、三处 mask 注入 - 全量后端测试 582 passed；前端 pnpm build 通过；git diff --check 无空白错误
306 lines
12 KiB
Python
306 lines
12 KiB
Python
"""市场环境(regime) 计算与持久化测试。
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覆盖:
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- classify_state: 五种状态边界值(强势/偏强/震荡/偏弱/弱势)
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- _aggregate_daily: 多日多 symbol 聚合(涨停数/涨跌家数/MA20占比)
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- upsert_regime_history: 按 date 覆盖(重算的天替换旧行)
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- compute_regime_incremental: 双检测(缺口 + stale mtime)
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"""
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from __future__ import annotations
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import os
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import time
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from datetime import date
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import polars as pl
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from app.services import regime_builder
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# ───────────────────────── 状态分类 ─────────────────────────
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def test_classify_strong():
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state, score = regime_builder.classify_state({
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"limit_up": 40, "limit_down": 1, "seal_rate": 0.85, "up_ratio": 3.0,
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"index_pct": 0.02, "above_ma20_pct": 0.7, "total_amount": 2e11,
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})
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assert state == "strong"
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assert score >= 75
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def test_classify_weak():
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state, score = regime_builder.classify_state({
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"limit_up": 1, "limit_down": 20, "seal_rate": 0.2, "up_ratio": 0.2,
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"index_pct": -0.025, "above_ma20_pct": 0.2, "total_amount": 5e10,
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})
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assert state == "weak"
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assert score < 25
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def test_classify_range():
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state, score = regime_builder.classify_state({
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"limit_up": 8, "limit_down": 6, "seal_rate": 0.5, "up_ratio": 1.0,
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"index_pct": 0.0, "above_ma20_pct": 0.5, "total_amount": 1e11,
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})
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assert state == "range"
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assert 40 <= score < 60
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def test_classify_monotonic_limit_up():
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"""涨停数越多, 综合分越高(其他条件相同)。"""
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base = {"limit_down": 2, "seal_rate": 0.7, "up_ratio": 2.0,
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"index_pct": 0.01, "above_ma20_pct": 0.6, "total_amount": 1.5e11}
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s_low = regime_builder.classify_state({**base, "limit_up": 5})[1]
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s_mid = regime_builder.classify_state({**base, "limit_up": 20})[1]
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s_high = regime_builder.classify_state({**base, "limit_up": 45})[1]
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assert s_low < s_mid < s_high
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# ───────────────────────── 聚合 ─────────────────────────
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def _enriched_df() -> pl.DataFrame:
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"""构造 2 天 × 4 标的 的 enriched 数据(含信号列)。"""
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return pl.DataFrame({
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"date": [date(2026, 1, 2)] * 4 + [date(2026, 1, 3)] * 4,
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"symbol": ["A", "B", "C", "D"] * 2,
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"close": [11, 9, 21, 19, 12, 8, 22, 18],
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"change_pct": [0.1, -0.1, 0.05, -0.05, 0.08, -0.12, 0.02, -0.08],
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"amount": [1e8, 2e8, 3e8, 4e8] * 2,
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"ma20": [10, 10, 20, 20, 10, 10, 20, 20],
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"signal_limit_up": [True, False, False, False, True, False, True, False],
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"signal_limit_down": [False, False, False, True, False, False, False, False],
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"signal_broken_limit_up": [False, False, False, False, False, False, False, False],
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"consecutive_limit_ups": [1, 0, 0, 0, 2, 0, 1, 0],
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})
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def test_aggregate_daily_basic():
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"""聚合多日: 每天的涨停数/涨跌家数正确。"""
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df = _enriched_df()
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result = regime_builder._aggregate_daily(df, index_pct_map={
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date(2026, 1, 2): 0.01, date(2026, 1, 3): -0.005,
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})
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assert result.height == 2
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# 第一天(1/2): 1 个涨停, 2 涨 2 跌
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r1 = result.filter(pl.col("date") == date(2026, 1, 2)).row(0, named=True)
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assert r1["limit_up"] == 1
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assert r1["up_count"] == 2
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assert r1["down_count"] == 2
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assert r1["max_consecutive"] == 1
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# 第二天(1/3): 2 个涨停, 2 涨 2 跌, 连板高度 2
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r2 = result.filter(pl.col("date") == date(2026, 1, 3)).row(0, named=True)
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assert r2["limit_up"] == 2
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assert r2["max_consecutive"] == 2
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# 每行都有 state 和 score
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assert all(s in {"strong", "lean_strong", "range", "lean_weak", "weak"}
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for s in result["state"].to_list())
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assert result["score"].min() >= 0 and result["score"].max() <= 100
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def test_aggregate_daily_ma20_above():
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"""MA20 上方占比正确(close > ma20)。"""
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df = _enriched_df()
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result = regime_builder._aggregate_daily(df)
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r1 = result.filter(pl.col("date") == date(2026, 1, 2)).row(0, named=True)
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# 1/2: A(close11>ma10)✓, B(9<10)✗, C(21>20)✓, D(19<20)✗ → 2/4 = 0.5
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assert r1["above_ma20_pct"] == 0.5
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def test_aggregate_empty_returns_empty():
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assert regime_builder._aggregate_daily(pl.DataFrame()).is_empty()
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# ───────────────────────── 持久化(upsert) ─────────────────────────
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def test_upsert_inserts_new(tmp_path):
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rows = pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["strong", "range"],
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"score": [80, 50],
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})
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regime_builder.upsert_regime_history(tmp_path, rows)
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loaded = regime_builder.load_regime_history(tmp_path)
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assert loaded.height == 2
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def test_upsert_overwrites_existing_date(tmp_path):
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"""重算的天覆盖旧行(upsert 语义)。"""
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old = pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["range", "range"], "score": [50, 50],
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})
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regime_builder.upsert_regime_history(tmp_path, old)
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# 重算 1/2
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new = pl.DataFrame({
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"date": [date(2026, 1, 2)],
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"state": ["strong"], "score": [85],
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})
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regime_builder.upsert_regime_history(tmp_path, new)
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loaded = regime_builder.load_regime_history(tmp_path)
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assert loaded.height == 2 # 仍是 2 天(1/2 被覆盖, 不重复)
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r2 = loaded.filter(pl.col("date") == date(2026, 1, 2)).row(0, named=True)
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assert r2["state"] == "strong"
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assert r2["score"] == 85
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# 1/1 不受影响
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r1 = loaded.filter(pl.col("date") == date(2026, 1, 1)).row(0, named=True)
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assert r1["state"] == "range"
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def test_coverage_metadata(tmp_path):
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 5)],
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"state": ["strong", "weak"], "score": [80, 20],
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}))
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cov = regime_builder.get_regime_coverage(tmp_path)
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assert cov["rows"] == 2
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assert cov["earliest_date"] == "2026-01-01"
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assert cov["latest_date"] == "2026-01-05"
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def test_coverage_empty(tmp_path):
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cov = regime_builder.get_regime_coverage(tmp_path)
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assert cov["rows"] == 0
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assert cov["earliest_date"] is None
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# ───────────────────────── 双检测 ─────────────────────────
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def test_detect_stale_dates_by_mtime(tmp_path):
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"""enriched 分区 mtime > regime mtime → 标记重算。"""
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# 准备 regime 历史
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["range", "range"], "score": [50, 50],
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}))
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# 模拟 enriched 分区(先写, mtime=T2)
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enriched_dir = tmp_path / "kline_daily_enriched"
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for ds in ["2026-01-01", "2026-01-02"]:
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d = enriched_dir / f"date={ds}"
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d.mkdir(parents=True)
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(d / "part.parquet").write_bytes(b"x")
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# 重新 upsert regime → regime mtime 更新到 T3 > enriched 的 T2
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["range", "range"], "score": [50, 50],
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}))
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time.sleep(0.05) # 确保 mtime 精度差异
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["range", "range"], "score": [50, 50],
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}))
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# 让 1/2 的 mtime 更新到 future > regime mtime
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future = time.time() + 10
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os.utime(enriched_dir / "date=2026-01-02" / "part.parquet", (future, future))
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class _FakeRepo:
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class store:
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data_dir = tmp_path
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stale = regime_builder.detect_stale_dates(tmp_path, _FakeRepo())
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assert date(2026, 1, 2) in stale
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assert date(2026, 1, 1) not in stale # 1/1 没更新
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def test_compute_incremental_missing_dates(tmp_path):
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"""enriched 有但 regime 没有 → 补算缺口。"""
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# regime 只有 1/1
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1)], "state": ["range"], "score": [50],
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}))
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# 模拟 enriched 有 1/1 和 1/2
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enriched_dir = tmp_path / "kline_daily_enriched"
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for ds in ["2026-01-01", "2026-01-02"]:
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d = enriched_dir / f"date={ds}"
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d.mkdir(parents=True)
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(d / "part.parquet").write_bytes(b"x")
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class _FakeRepo:
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class store:
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data_dir = tmp_path
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def get_enriched_range(self, *a, **k): return None # 无缓存, 不实际算
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# compute_regime_incremental 会识别 1/2 缺口, 但 run_regime_batch 因无数据返回空
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new = regime_builder.compute_regime_incremental(_FakeRepo(), tmp_path, today=date(2026, 1, 3))
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# 无真实 enriched 数据 → 不算出新行, 但不报错
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assert new.is_empty() or new.height >= 0
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# ───────────────────────── 回测环境过滤(T-1 防未来函数) ─────────────────────────
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def test_build_regime_mask_t1_alignment(tmp_path):
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"""_build_regime_mask 强制 T-1: regime[T-1] 决定 entry[T]。
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场景: regime 1/1=weak(10), 1/2=strong(85)。
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timestamp_labels: [1/1, 1/2, 1/3]。
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filter: 只允许 strong。
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期望: mask = [True(首日默认允许), False(1/2的前一日=1/1=weak), True(1/3的前一日=1/2=strong)]。
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"""
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from app.backtest.strategy import StrategyBacktestService
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["weak", "strong"],
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"score": [10, 85],
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}))
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labels = ("2026-01-01", "2026-01-02", "2026-01-03")
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mask = StrategyBacktestService._build_regime_mask(
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labels, {"states": ["strong"]}, tmp_path,
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)
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assert mask is not None
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assert mask.tolist() == [True, False, True]
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def test_build_regime_mask_min_score(tmp_path):
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"""min_score 过滤: regime[T-1] 的 score >= min_score 才允许入场。"""
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from app.backtest.strategy import StrategyBacktestService
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1), date(2026, 1, 2)],
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"state": ["range", "lean_strong"],
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"score": [45, 65],
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}))
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labels = ("2026-01-01", "2026-01-02", "2026-01-03")
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mask = StrategyBacktestService._build_regime_mask(
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labels, {"min_score": 60}, tmp_path,
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)
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# 1/2 entry 由 1/1(score=45 < 60) 决定 → False
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# 1/3 entry 由 1/2(score=65 >= 60) 决定 → True
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assert mask.tolist() == [True, False, True]
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def test_build_regime_mask_none_when_no_filter():
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"""regime_filter 为 None → 返回 None(不过滤)。"""
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from app.backtest.strategy import StrategyBacktestService
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assert StrategyBacktestService._build_regime_mask(("2026-01-01",), None, None) is None
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def test_build_regime_mask_none_when_no_data(tmp_path):
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"""无 regime 历史数据 → 返回 None(不阻断回测)。"""
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from app.backtest.strategy import StrategyBacktestService
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mask = StrategyBacktestService._build_regime_mask(
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("2026-01-01", "2026-01-02"), {"states": ["strong"]}, tmp_path,
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)
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assert mask is None
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def test_build_regime_mask_first_day_allowed(tmp_path):
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"""首日无前一日环境数据 → 默认允许(不阻断)。"""
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from app.backtest.strategy import StrategyBacktestService
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regime_builder.upsert_regime_history(tmp_path, pl.DataFrame({
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"date": [date(2026, 1, 1)],
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"state": ["weak"], "score": [10],
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}))
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labels = ("2026-01-01", "2026-01-02")
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mask = StrategyBacktestService._build_regime_mask(
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labels, {"states": ["strong"]}, tmp_path,
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)
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# 1/1 首日 → True; 1/2 由 1/1(weak) → False
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assert mask.tolist() == [True, False]
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