from __future__ import annotations from datetime import date, timedelta import polars as pl from app.backtest.optimizer import OptimizeConfig from app.backtest.strategy import StrategyBacktestConfig from app.backtest.walkforward import WalkForwardConfig from app.backtest.worker import make_worker_task, run_worker_task def _write_worker_strategy(data_dir) -> None: strategy_dir = data_dir / "strategies" / "custom" strategy_dir.mkdir(parents=True) (strategy_dir / "worker_always_entry.py").write_text( """import numpy as np from app.backtest.matrix import make_signal_matrix META = { "id": "worker_always_entry", "name": "worker", "asset_types": ["stock"], "timeframes": ["1d"], "params": [ {"id": "gate", "type": "int", "default": 1, "min": 1, "max": 2, "step": 1}, ], "scoring": {}, "required_features": ["open", "high", "low", "close", "volume"], } EXECUTION_BACKEND = "matrix_native" ENTRY_SIGNALS = [] EXIT_SIGNALS = [] STOP_LOSS = None MAX_HOLD_DAYS = 1 class AlwaysEntry: def required_fields(self): return frozenset({"open", "high", "low", "close", "volume"}) def required_warmup_bars(self, params): return 1 def compute_signals(self, market, params): return make_signal_matrix( market.shape, entry=np.ones(market.shape, dtype=np.uint8), ) MATRIX_STRATEGY = AlwaysEntry() """, encoding="utf-8", ) def _write_market_data(data_dir, start: date, days: int = 3) -> None: rows = [] for offset in range(days): close = 10.0 + offset current = start + timedelta(days=offset) rows.append({ "symbol": "600000.SH", "date": current, "open": close, "high": close, "low": close, "close": close, "volume": 1000.0, "amount": close * 100000.0, "raw_close": close, "raw_high": close, "raw_low": close, "turnover_rate": 1.0, "consecutive_limit_ups": 0, "consecutive_limit_downs": 0, }) partition = data_dir / "kline_daily_enriched" / f"date={current.isoformat()}" partition.mkdir(parents=True) pl.DataFrame([rows[-1]]).write_parquet(partition / "part.parquet") instruments_dir = data_dir / "instruments" instruments_dir.mkdir(parents=True) pl.DataFrame({ "symbol": ["600000.SH"], "name": ["浦发银行"], "total_shares": [1_000_000_000.0], "float_shares": [1_000_000_000.0], }).write_parquet(instruments_dir / "part.parquet") def test_spawn_worker_returns_compact_result_and_memory_metrics(tmp_path): start = date(2024, 1, 1) data_dir = tmp_path / "data" _write_worker_strategy(data_dir) _write_market_data(data_dir, start) config = StrategyBacktestConfig( strategy_id="worker_always_entry", symbols=["600000.SH"], start=start, end=start + timedelta(days=2), overrides={"basic_filter": {"enabled": False}}, matching="close_t", fees_pct=0, slippage_bps=0, max_positions=1, ) result = run_worker_task(make_worker_task("backtest", data_dir, config)) assert result["error"] is None assert result["stats"]["execution_backend"] == "matrix_native" worker = result["stats"]["worker"] assert worker["peak_rss_bytes"] > 0 assert worker["serialized_result_bytes"] > 0 assert worker["worker_exitcode"] == 0 assert worker["parent_rss_after_worker_exit_bytes"] > 0 def test_spawn_optimizer_reuses_one_matrix_and_exits(tmp_path): start = date(2024, 1, 1) data_dir = tmp_path / "data" _write_worker_strategy(data_dir) _write_market_data(data_dir, start) config = OptimizeConfig( strategy_id="worker_always_entry", symbols=["600000.SH"], start=start, end=start + timedelta(days=2), param_grid={"gate": [1, 2]}, objective="total_return", max_workers=4, overrides={"basic_filter": {"enabled": False}}, backtest_kwargs={ "matching": "close_t", "fees_pct": 0, "slippage_bps": 0, "max_positions": 1, }, ) result = run_worker_task(make_worker_task("optimize", data_dir, config)) assert result["n_completed"] == 2 assert result["effective_workers"] == 1 assert result["shared_market_data"] is True assert result["shared_market_data_bytes"] > 0 assert result["best_backtest"]["equity_curve"] assert result["best_backtest"]["trades"] assert "mc_maxdd_p50" in result["best_backtest"]["stats"] assert result["matrix_compute_cache"]["released"] is True assert result["performance"]["trial_peak_rss_bytes"] > 0 assert result["performance"]["best_backtest_peak_rss_bytes"] > 0 assert result["performance"]["trials_per_second"] > 0 assert result["worker"]["worker_exitcode"] == 0 def test_spawn_walkforward_reuses_shared_matrix_across_folds(tmp_path): start = date(2024, 1, 1) data_dir = tmp_path / "data" _write_worker_strategy(data_dir) _write_market_data(data_dir, start, days=8) config = WalkForwardConfig( strategy_id="worker_always_entry", symbols=["600000.SH"], start=start, end=start + timedelta(days=7), param_grid={"gate": [1, 2]}, objective="total_return", train_days=2, test_days=1, step_days=2, overrides={"basic_filter": {"enabled": False}}, backtest_kwargs={ "matching": "close_t", "fees_pct": 0, "slippage_bps": 0, "max_positions": 1, }, ) result = run_worker_task(make_worker_task("walkforward", data_dir, config)) assert result["n_folds"] == 2 assert result["shared_market_data"] is True assert result["shared_market_data_bytes"] > 0 assert all(fold["oos_stats"]["shared_market_data"] for fold in result["folds"]) assert result["worker"]["worker_exitcode"] == 0 def test_spawn_walkforward_skips_folds_before_available_matrix_data(tmp_path): configured_start = date(2024, 1, 1) market_start = configured_start + timedelta(days=4) data_dir = tmp_path / "data" _write_worker_strategy(data_dir) _write_market_data(data_dir, market_start, days=8) config = WalkForwardConfig( strategy_id="worker_always_entry", symbols=["600000.SH"], start=configured_start, end=configured_start + timedelta(days=11), param_grid={"gate": [1, 2]}, objective="total_return", train_days=2, test_days=1, step_days=2, overrides={"basic_filter": {"enabled": False}}, backtest_kwargs={ "matching": "close_t", "fees_pct": 0, "slippage_bps": 0, "max_positions": 1, }, ) result = run_worker_task(make_worker_task("walkforward", data_dir, config)) assert result["n_planned_folds"] == 4 assert result["n_skipped"] == 1 assert result["skipped"][0]["reason"] == "训练区间无可用行情数据" assert result["n_folds"] == 3 assert result["worker"]["worker_exitcode"] == 0