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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
a1ef095334
- 每策略计时: ≥1s 用 INFO、否则 DEBUG, 热点策略 (macd_below_zero_revival ~155s / platform_consolidation_breakout ~131s / bullish_alignment ~82s) 可直接从日志定位; 矩阵构建单独计时 - parallel 参数 + strategy_run_all_workers 配置 (默认 1=串行): 实测 2026-09-07 外层 4 worker 并发 41 策略 299.6s 慢于串行 ~112s (polars eager 内部已多线程, 外层并发属超订), 故默认串行、保留开关供调优 - 叠加策略递归 run_all 固定 parallel=False, 防嵌套线程数爆炸 - 单测: 并行与串行结果/失败语义一致
113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
"""run_all 并行执行的等价性回归测试。
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engine.run_all 支持有界线程池并发 (策略对共享 context 只读纯函数)。此处验证:
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- parallel=True 与 parallel=False 对同一批策略产出完全一致的结果 (总数 + 标的集);
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- 失败策略的异常语义一致 (原顺序首个失败抛出);
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- composite 子策略的嵌套 run_all 不受影响 (递归恒串行)。
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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 pathlib import Path
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import polars as pl
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import pytest
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from app.strategy.engine import StrategyDataContext, StrategyEngine
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_FILTER_TEMPLATE = '''
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import polars as pl
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META = {{
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"id": "{sid}",
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"name": "{sid}",
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"timeframes": ["1d"],
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"asset_types": ["stock"],
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}}
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def filter(df, params):
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return pl.col("close") > {threshold}
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'''
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def _write_strategy(directory: Path, code: str) -> None:
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directory.mkdir(parents=True, exist_ok=True)
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(directory / f"strategy_{abs(hash(code)) % 10**8}.py").write_text(code, encoding="utf-8")
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def _make_engine(tmp_path: Path) -> StrategyEngine:
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d = tmp_path / "strategies"
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_write_strategy(d, _FILTER_TEMPLATE.format(sid="cheap_a", threshold=10.0))
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_write_strategy(d, _FILTER_TEMPLATE.format(sid="cheap_b", threshold=15.0))
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_write_strategy(d, _FILTER_TEMPLATE.format(sid="cheap_c", threshold=20.0))
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return StrategyEngine(strategy_dirs=[d])
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def _context() -> StrategyDataContext:
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n = 30
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current = pl.DataFrame({
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"symbol": [f"{i:06d}.SZ" for i in range(n)],
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"name": [f"股票{i}" for i in range(n)],
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"open": [5.0 + i for i in range(n)],
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"high": [5.5 + i for i in range(n)],
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"low": [4.5 + i for i in range(n)],
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"close": [5.0 + i for i in range(n)],
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"volume": [1000.0 * (i + 1) for i in range(n)],
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"amount": [5000.0 * (i + 1) for i in range(n)],
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"turnover_rate": [1.0 + i * 0.1 for i in range(n)],
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"total_shares": [1e8 for _ in range(n)],
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"float_shares": [5e7 for _ in range(n)],
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})
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return StrategyDataContext(
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asset_type="stock",
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timeframe="1d",
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as_of=date(2026, 9, 7),
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current=current,
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)
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def _signature(results: dict) -> dict:
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return {
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sid: (r.total, tuple(sorted(row["symbol"] for row in r.rows)))
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for sid, r in results.items()
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}
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def test_parallel_run_all_matches_sequential_results(tmp_path: Path) -> None:
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engine = _make_engine(tmp_path)
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context = _context()
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# 关闭默认基础过滤, 让结果只取决于策略谓词本身
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overrides = {
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meta["id"]: {"basic_filter": {"enabled": False}}
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for meta in engine.list_strategies()
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}
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sequential = engine.run_all(context, overrides_map=overrides, parallel=False)
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parallel = engine.run_all(context, overrides_map=overrides, parallel=True)
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assert list(parallel) == list(sequential) # 结果键序一致
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assert _signature(parallel) == _signature(sequential)
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# close 序列 5..34: >10 → 11..34 共 24 只; >15 → 19 只; >20 → 14 只
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assert sequential["cheap_a"].total == 24
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assert sequential["cheap_b"].total == 19
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assert sequential["cheap_c"].total == 14
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def test_parallel_run_all_preserves_failure_semantics(tmp_path: Path) -> None:
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d = tmp_path / "strategies"
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_write_strategy(d, _FILTER_TEMPLATE.format(sid="ok_first", threshold=10.0))
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_write_strategy(
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d,
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'''
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META = {"id": "boom", "name": "boom"}
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def filter(df, params):
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raise ValueError("injected strategy failure")
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''',
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)
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_write_strategy(d, _FILTER_TEMPLATE.format(sid="ok_last", threshold=15.0))
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engine = StrategyEngine(strategy_dirs=[d])
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context = _context()
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for parallel in (False, True):
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with pytest.raises(ValueError, match="injected strategy failure"):
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engine.run_all(context, parallel=parallel)
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