mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
synced 2026-09-12 22:34:18 +08:00
shy3130
697c27bb02
- 市场环境: 新增情绪周期6阶段(冰点/启动/主升/高潮/退潮/修复, 连板梯队驱动, EMA平滑+2日确认+弱档否决, 平均段长9.7天)与概念/行业主线排名(涨停梯队聚合, 可配置宽基/风格标签过滤); 市场环境页重构, regime 透明加列, 与5档state并存 - 挖掘: 因子与策略挖掘全链路(API/worker/进程锁/候选库/前端工作台/文档), 周度调度默认关闭且永不自动发布 - 回测: 财务快照因子(点时口径), 批量回测预计算共享下期收益, 信号路径矩阵列依赖展开修复(consecutive_limit_ups 缺列报错) - 数据/性能: enriched 生成与预热治理, 重任务限流, 行情/K线缓存复用, 时区修复 - 测试: 后端全量 914 通过; GUI 黑盒验证截图存证 gui-test-screenshots/
392 lines
13 KiB
Python
392 lines
13 KiB
Python
from __future__ import annotations
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from datetime import date, timedelta
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from types import SimpleNamespace
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import polars as pl
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import pytest
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from app.backtest.factor import (
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FactorBacktestService,
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FactorBatchConfig,
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FactorBatchItem,
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FactorConfig,
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FactorResult,
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)
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from app.backtest.regime_alignment import (
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align_regime_t_minus_one,
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build_regime_filter_mask,
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)
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class _Engine:
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def __init__(self, panel: pl.DataFrame, data_dir=None) -> None:
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self.panel = panel
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self.calls = 0
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self.repo = (
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SimpleNamespace(store=SimpleNamespace(data_dir=data_dir))
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if data_dir is not None
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else None
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)
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def load_panel(self, symbols, start, end, columns, asset_type):
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self.calls += 1
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selected = [column for column in columns if column in self.panel.columns]
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return self.panel.select(selected)
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def _config(
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factor_name: str = "turnover_rate",
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**overrides,
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) -> FactorConfig:
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values = {
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"factor_name": factor_name,
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"symbols": None,
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"start": date(2026, 1, 5),
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"end": date(2026, 1, 12),
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"n_groups": 2,
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"rebalance": "daily",
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"fees_pct": 0.0,
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"slippage_bps": 0.0,
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}
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values.update(overrides)
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return FactorConfig(**values)
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def _batch_config(**overrides) -> FactorBatchConfig:
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values = {
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"factor_names": ["turnover_rate"],
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"symbols": None,
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"start": date(2026, 1, 5),
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"end": date(2026, 1, 12),
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"n_groups": 2,
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"rebalance": "daily",
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"fees_pct": 0.0,
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"slippage_bps": 0.0,
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}
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values.update(overrides)
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return FactorBatchConfig(**values)
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def _daily_panel(days: int = 8, symbols: int = 4) -> pl.DataFrame:
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rows = []
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start = date(2026, 1, 5)
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for day in range(days):
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for index in range(symbols):
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rows.append({
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"symbol": f"S{index}",
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"date": start + timedelta(days=day),
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"open": 10.0 + index,
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"high": 10.5 + index,
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"low": 9.5 + index,
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"close": (10.0 + index) * (1.0 + (index - 1) * day * 0.01),
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"volume": 1_000.0,
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"amount": 10_000.0,
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"turnover_rate": float(index + 1),
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})
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return pl.DataFrame(rows)
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def test_single_and_batch_use_same_full_price_axis_with_internal_factor_null():
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panel = _daily_panel(days=5, symbols=3).with_columns(
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pl.when((pl.col("symbol") == "S2") & (pl.col("date") == date(2026, 1, 6)))
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.then(None)
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.otherwise(pl.col("turnover_rate"))
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.alias("turnover_rate")
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)
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engine = _Engine(panel)
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service = FactorBacktestService(engine)
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single = service.run(_config(end=date(2026, 1, 9)))
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batch = service.run_batch(_batch_config(end=date(2026, 1, 9)))
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assert single.error is None
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assert batch.results[0].error is None
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assert single.ic_mean == batch.results[0].ic_mean
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assert single.ir == batch.results[0].ir
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assert single.long_short_stats["total_return"] == batch.results[0].long_short_return
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def test_daily_forward_returns_join_exact_global_trading_dates_on_suspension():
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dates = [date(2026, 1, 5) + timedelta(days=offset) for offset in range(6)]
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rows = [
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{"symbol": "B", "date": current, "close": 20.0 + index}
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for index, current in enumerate(dates)
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]
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rows.extend(
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{"symbol": "A", "date": current, "close": 10.0 + index}
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for index, current in enumerate(dates)
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if index != 3
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)
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panel = pl.DataFrame(rows)
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prepared = FactorBacktestService._attach_shared_next_return(
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panel,
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_batch_config(end=dates[-1]),
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)
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first = prepared.filter(
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(pl.col("symbol") == "A") & (pl.col("date") == dates[0])
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).row(0, named=True)
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second = prepared.filter(
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(pl.col("symbol") == "A") & (pl.col("date") == dates[1])
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).row(0, named=True)
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assert first["_forward_return_1d"] == pytest.approx(11.0 / 10.0 - 1.0)
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assert first["_forward_return_3d"] is None
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assert first["_forward_return_5d"] == pytest.approx(15.0 / 10.0 - 1.0)
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assert second["_forward_return_3d"] == pytest.approx(14.0 / 11.0 - 1.0)
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def test_service_forward_axis_uses_market_partitions_when_selected_universe_has_gap(
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tmp_path,
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):
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dates = [date(2026, 1, 5) + timedelta(days=offset) for offset in range(3)]
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panel = pl.DataFrame({
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"symbol": ["A", "A"],
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"date": [dates[0], dates[2]],
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"open": [10.0, 12.0],
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"high": [10.0, 12.0],
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"low": [10.0, 12.0],
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"close": [10.0, 12.0],
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"volume": [1_000.0, 1_000.0],
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"amount": [10_000.0, 12_000.0],
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"turnover_rate": [1.0, 2.0],
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})
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for current in dates:
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partition = tmp_path / "kline_daily_enriched" / f"date={current.isoformat()}"
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partition.mkdir(parents=True)
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pl.DataFrame({"date": [current]}).write_parquet(partition / "part.parquet")
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service = FactorBacktestService(_Engine(panel, tmp_path))
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config = _batch_config(start=dates[0], end=dates[-1])
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loaded = service._load_factor_panel(config, ["turnover_rate"])
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prepared = service._attach_shared_next_return(
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loaded,
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config,
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trading_dates=service._global_trading_dates(config),
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)
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first = prepared.filter(pl.col("date") == dates[0]).row(0, named=True)
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assert first["_forward_return_1d"] is None
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assert first["_forward_return_3d"] is None
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def test_tie_aware_groups_do_not_split_constant_factor_by_symbol_order():
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panel = pl.DataFrame({
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"symbol": ["C", "A", "D", "B"],
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"date": [date(2026, 1, 5)] * 4,
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"factor": [7.0] * 4,
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})
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first = FactorBacktestService._add_groups(panel, "factor", 5).sort("symbol")
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second = FactorBacktestService._add_groups(
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panel.reverse(), "factor", 5
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).sort("symbol")
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assert first["_group"].n_unique() == 1
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assert first["_group"].to_list() == second["_group"].to_list()
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assert first["_factor_strength"].to_list() == second["_factor_strength"].to_list()
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def test_weekly_uses_first_actual_trading_day_not_monday():
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panel = pl.DataFrame({
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"symbol": ["A", "A", "A", "A"],
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"date": [
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date(2026, 1, 6),
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date(2026, 1, 7),
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date(2026, 1, 13),
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date(2026, 1, 14),
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],
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"close": [10.0, 11.0, 12.0, 13.0],
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})
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result = FactorBacktestService._calc_period_return(panel, "weekly")
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assert result.filter(pl.col("date") == date(2026, 1, 6))["_next_return"][0] == pytest.approx(0.2)
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assert result.filter(pl.col("date") == date(2026, 1, 7))["_next_return"][0] is None
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def test_monthly_uses_first_actual_trading_day():
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panel = pl.DataFrame({
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"symbol": ["A", "A", "A", "A"],
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"date": [
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date(2026, 1, 6),
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date(2026, 1, 7),
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date(2026, 2, 3),
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date(2026, 2, 4),
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],
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"close": [10.0, 11.0, 12.0, 13.0],
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})
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result = FactorBacktestService._calc_period_return(panel, "monthly")
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assert result.filter(pl.col("date") == date(2026, 1, 6))["_next_return"][0] == pytest.approx(0.2)
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assert result.filter(pl.col("date") == date(2026, 1, 7))["_next_return"][0] is None
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def test_factor_weight_and_decomposed_costs_change_group_results():
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panel = _daily_panel(days=2, symbols=4).with_columns(
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pl.when(pl.col("date") == date(2026, 1, 6))
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.then(
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pl.when(pl.col("symbol") == "S0").then(9.0)
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.when(pl.col("symbol") == "S1").then(10.0)
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.when(pl.col("symbol") == "S2").then(14.3)
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.otherwise(16.9)
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)
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.otherwise(pl.col("close"))
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.alias("close")
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)
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service = FactorBacktestService(_Engine(panel))
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equal = service.run(_config(end=date(2026, 1, 6), weight="equal"))
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weighted = service.run(_config(end=date(2026, 1, 6), weight="factor_weight"))
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costly = service.run(_config(
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end=date(2026, 1, 6),
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weight="factor_weight",
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fees_pct=0.009,
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commission_pct=0.001,
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stamp_tax_pct=0.002,
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slippage_bps=10.0,
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))
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equal_q2 = next(item for item in equal.group_stats if item["label"] == "Q2")
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weighted_q2 = next(item for item in weighted.group_stats if item["label"] == "Q2")
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costly_q2 = next(item for item in costly.group_stats if item["label"] == "Q2")
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assert weighted_q2["total_return"] != equal_q2["total_return"]
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assert weighted_q2["total_return"] - costly_q2["total_return"] == pytest.approx(0.006)
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assert costly.long_short_stats["total_return"] < weighted.long_short_stats["total_return"]
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assert costly.config["commission_pct"] == 0.001
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assert costly.config["stamp_tax_pct"] == 0.002
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def test_factor_v2_metrics_and_defaults_are_backward_compatible():
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default_result = FactorResult(run_id="r", config={})
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default_item = FactorBatchItem(factor_name="f", label="F", group="G")
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assert default_result.methodology_version == "factor_v2"
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assert default_result.yearly_ic == []
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assert default_result.ic_decay == []
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assert default_result.regime_stats == []
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assert default_item.methodology_version == "factor_v2"
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assert default_item.yearly_ic == []
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result = FactorBacktestService(_Engine(_daily_panel())).run(_config())
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assert result.methodology_version == "factor_v2"
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assert result.coverage == 1.0
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assert result.turnover is not None
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assert result.long_short_sharpe is not None
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assert [item["horizon"] for item in result.ic_decay] == [1, 3, 5]
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assert result.yearly_ic[0]["year"] == 2026
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assert result.long_short_stats["portfolio_type"] == "theoretical_factor_spread"
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assert result.long_short_stats["executable_short"] is False
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def test_factor_regime_stats_accept_injected_t_minus_one_mapping(tmp_path):
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panel = _daily_panel(days=5)
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market_dates = [date(2026, 1, 2), *panel["date"].unique().sort().to_list()]
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for current in market_dates:
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partition = tmp_path / "kline_daily_enriched" / f"date={current.isoformat()}"
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partition.mkdir(parents=True)
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pl.DataFrame({"date": [current]}).write_parquet(partition / "part.parquet")
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regimes = {
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date(2026, 1, 2): {"state": "range", "score": 50},
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date(2026, 1, 5): {"state": "weak", "score": 20},
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date(2026, 1, 6): {"state": "strong", "score": 80},
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date(2026, 1, 7): {"state": "strong", "score": 85},
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date(2026, 1, 8): {"state": "range", "score": 50},
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}
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result = FactorBacktestService(_Engine(panel, tmp_path)).run(
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_config(end=date(2026, 1, 9)),
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regime_by_date=regimes,
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)
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assert result.error is None
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assert {item["state"] for item in result.regime_stats} == {"range", "strong", "weak"}
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def test_factor_regime_stats_reject_missing_actual_market_predecessor(tmp_path):
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panel = _daily_panel(days=3)
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market_dates = [date(2026, 1, 2), *panel["date"].unique().sort().to_list()]
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for current in market_dates:
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partition = tmp_path / "kline_daily_enriched" / f"date={current.isoformat()}"
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partition.mkdir(parents=True)
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pl.DataFrame({"date": [current]}).write_parquet(partition / "part.parquet")
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regimes = {
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date(2026, 1, 1): {"state": "range", "score": 50},
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date(2026, 1, 5): {"state": "weak", "score": 20},
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date(2026, 1, 6): {"state": "strong", "score": 80},
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}
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with pytest.raises(ValueError, match="2026-01-02"):
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FactorBacktestService(_Engine(panel, tmp_path)).run(
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_config(end=date(2026, 1, 7)),
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regime_by_date=regimes,
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)
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def test_align_regime_t_minus_one_is_pure_and_fail_closed_in_required_range():
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labels = ("2026-01-05", "2026-01-06", "2026-01-07")
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regimes = {
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"2026-01-05": ("weak", 20),
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"2026-01-06": {"state": "strong", "score": 80},
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}
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aligned = align_regime_t_minus_one(
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labels,
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regimes,
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required_start=date(2026, 1, 6),
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required_end=date(2026, 1, 7),
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)
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mask = build_regime_filter_mask(
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labels,
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{"states": ["strong"], "min_score": 60},
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regimes,
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required_start=date(2026, 1, 6),
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required_end=date(2026, 1, 7),
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)
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assert aligned == [None, ("weak", 20.0), ("strong", 80.0)]
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assert mask is not None
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assert mask.tolist() == [True, False, True]
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with pytest.raises(ValueError, match="正式首日"):
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align_regime_t_minus_one(
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labels,
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regimes,
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required_start=date(2026, 1, 5),
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required_end=date(2026, 1, 7),
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)
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lean_regimes = {
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"2026-01-05": ("lean_strong", 60),
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"2026-01-06": ("range", 50),
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}
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strong_only_mask = build_regime_filter_mask(
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labels,
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{"states": ["strong"]},
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lean_regimes,
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required_start=date(2026, 1, 6),
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required_end=date(2026, 1, 7),
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)
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assert strong_only_mask is not None
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assert strong_only_mask.tolist() == [True, False, False]
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aggregated_mask = build_regime_filter_mask(
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labels,
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{"states": ["strong", "lean_strong"]},
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lean_regimes,
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required_start=date(2026, 1, 6),
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required_end=date(2026, 1, 7),
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)
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assert aggregated_mask is not None
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assert aggregated_mask.tolist() == [True, True, False]
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with pytest.raises(ValueError, match="缺少前一交易日环境"):
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align_regime_t_minus_one(
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labels,
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{"2026-01-05": ("weak", 20)},
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required_start=date(2026, 1, 6),
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required_end=date(2026, 1, 7),
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)
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