"""优化器两段式加速（指标缓存 + 并行）与多 seed 验证/晋级门槛测试。""" from __future__ import annotations import numpy as np import pandas as pd import pytest from easy_tdx.backtest.indicator_cache import IndicatorCache from easy_tdx.backtest.optimizer import ParamGridOptimizer from easy_tdx.backtest.strategy import Strategy from easy_tdx.backtest.validation import MultiSeedValidator def _pool_df(n: int = 300, seed: int = 5, drift: float = 0.002) -> pd.DataFrame: rng = np.random.default_rng(seed) dates = pd.date_range("2020-01-01", periods=n, freq="B") close = 10.0 * np.cumprod(1.0 + drift + rng.normal(0, 0.012, n)) return pd.DataFrame( { "datetime": dates, "open": close * 0.999, "high": close * 1.01, "low": close * 0.99, "close": close, "vol": 1000.0, "amount": close * 1000, } ) # ── IndicatorCache ──────────────────────────────────────────────────────────── def test_indicator_cache_hit_and_stats(): from easy_tdx.MyTT import MA df = _pool_df(100) arr = df["close"].to_numpy() cache = IndicatorCache() r1 = cache.get_or_compute(MA, (arr, 5), {}) r2 = cache.get_or_compute(MA, (arr, 5), {}) assert cache.hits == 1 and cache.misses == 1 assert np.allclose(r1, r2, equal_nan=True) # 前 4 位是 NaN（预热期） # 不同参数 → miss cache.get_or_compute(MA, (arr, 10), {}) stats = cache.stats() assert stats["total"] == 3 assert stats["hit_rate"] == pytest.approx(1 / 3, abs=1e-3) def test_indicator_cache_distinguishes_arrays(): from easy_tdx.MyTT import MA a = _pool_df(50, seed=1)["close"].to_numpy() b = _pool_df(50, seed=2)["close"].to_numpy() cache = IndicatorCache() cache.get_or_compute(MA, (a, 5), {}) cache.get_or_compute(MA, (b, 5), {}) assert cache.misses == 2 # 不同数组不误命中 def test_indicator_cache_hits_copied_array_with_same_content(): """同内容、不同对象（引擎每次 run 重建数组的情形）必须命中。 回归：键曾用对象 id 做数组签名，引擎逐 run 重建数组对象导致寻优 缓存全程 0 命中（两段式加速静默失效）。 """ from easy_tdx.MyTT import MA arr = _pool_df(100)["close"].to_numpy() cache = IndicatorCache() cache.get_or_compute(MA, (arr, 5), {}) cache.get_or_compute(MA, (arr.copy(), 5), {}) # 同内容不同对象 assert cache.hits == 1 assert cache.misses == 1 # ── 优化器集成（缓存命中 + 结果一致 + 并行）───────────────────────────────── def test_optimizer_cache_reuse_across_grid_points(): """2 参数网格：每档参数的指标只算一次，跨点命中。 ma_cross 的 fast×slow 网格中 MA(close, fast) 会被每个 slow 组合重复 请求——缓存应把这些重复请求转为命中。 """ df = _pool_df(300) grid = {"fast": [5, 10, 15], "slow": [20, 30, 40]} # 9 点 opt = ParamGridOptimizer("ma_cross", grid, df, cash=100_000.0) result = opt.run() assert len(result.results) == 9 assert result.cache_stats is not None assert result.cache_stats["hits"] > 0 # 9 个点 × 每点 2 个 MA + 2 个 CROSS = 36 次请求； # MA 各 6 档只算 6 次（省 12 次），CROSS 依赖 MA 结果仍逐点计算 assert result.cache_stats["misses"] < 36 def test_optimizer_cached_results_identical_to_uncached(): """缓存开关不改变回测结果（正确性对拍）。""" df = _pool_df(250) grid = {"fast": [5, 10], "slow": [20, 30]} # 无缓存路径（optimizer 之前的行为：engine 不挂 cache） opt_plain = ParamGridOptimizer("ma_cross", grid, df, cash=100_000.0) res_plain = opt_plain.run() # 缓存路径 opt_cached = ParamGridOptimizer("ma_cross", grid, df, cash=100_000.0) res_cached = opt_cached.run() def key_map(res): return {(r.params["fast"], r.params["slow"]): r.total_return for r in res.results} assert key_map(res_plain) == key_map(res_cached) def test_optimizer_parallel_matches_serial(): """进程池并行结果与串行一致（少量网格冒烟，避免 CI 慢）。""" import sys if sys.platform == "win32": # Windows spawn 下进程池在本测试进程中开销大，仅冒烟 4 点 df = _pool_df(200) grid = {"fast": [5, 10], "slow": [20, 30]} serial = ParamGridOptimizer("ma_cross", grid, df).run() parallel = ParamGridOptimizer("ma_cross", grid, df, workers=2).run() s = {(r.params["fast"], r.params["slow"]): round(r.total_return, 9) for r in serial.results} p = { (r.params["fast"], r.params["slow"]): round(r.total_return, 9) for r in parallel.results } assert s == p def test_optimizer_cache_stats_serialized(): df = _pool_df(150) result = ParamGridOptimizer("rsi_reversal", {"n": [10, 14]}, df).run() d = result.to_dict() assert "cache_stats" in d # ── MultiSeedValidator ─────────────────────────────────────────────────────── class _CycleTrader(Strategy): """每 10 根切换持仓（保证各标的有完整回合）。""" def init(self) -> None: self._count = 0 self._holding = False def next(self) -> None: self._count += 1 if self._count % 10 == 0: if self._holding: self.sell() self._holding = False else: self.buy() self._holding = True def _pool(n_stocks: int = 6, n: int = 300, drift: float = 0.002) -> dict[str, pd.DataFrame]: return {f"SH:60000{i}": _pool_df(n, seed=i, drift=drift) for i in range(n_stocks)} def test_multiseed_runs_all_pool_by_default(): result = MultiSeedValidator(_CycleTrader, _pool(5), n_seeds=2).run() assert result.seeds == [42, 7] # 全池抽样：5 标的 × 2 seed = 10 次运行 assert len(result.runs) == 10 assert all(r.symbol.startswith("SH:") for r in result.runs) def test_multiseed_sample_size_limits_runs(): result = MultiSeedValidator(_CycleTrader, _pool(6), n_seeds=2, sample_size=3).run() # 3 标的 × 2 seed = 6 次；两个 seed 抽到的子集可能不同（顺序随机） assert len(result.runs) == 6 seeds = {r.seed for r in result.runs} assert seeds == {42, 7} def test_multiseed_promotion_gates_uptrend(): """普涨池：四项默认门槛全过 → promoted。""" result = MultiSeedValidator(_CycleTrader, _pool(6, drift=0.004), n_seeds=2).run() gate_keys = {g.key for g in result.gates} assert gate_keys == {"positive_ratio", "mean_sharpe", "mean_trades", "mean_return"} # 上涨池正收益比例高、均值线全正 assert result.positive_ratio >= 0.5 assert result.mean_return > 0 assert result.promoted is True def test_multiseed_promotion_fails_on_downtrend(): """普跌池：正收益比例低 → promoted=False。""" result = MultiSeedValidator(_CycleTrader, _pool(6, drift=-0.004), n_seeds=2).run() assert result.promoted is False assert any(not g.passed for g in result.gates) def test_multiseed_custom_gates_override(): """门槛可配置覆盖：mean_return 阈值提高到不可达 → 不晋级。""" result = MultiSeedValidator( _CycleTrader, _pool(4, drift=0.004), n_seeds=1, gates={"mean_return": 999.0}, ).run() assert result.promoted is False gate = {g.key: g for g in result.gates}["mean_return"] assert gate.threshold == 999.0 assert gate.passed is False def test_multiseed_per_seed_stability_column(): result = MultiSeedValidator(_CycleTrader, _pool(5, drift=0.003), n_seeds=3).run() # 跨 seed 稳定性列：每个 seed 一个正收益比例 assert len(result.per_seed_positive_ratio) == 3 assert set(result.per_seed_positive_ratio) == {"42", "7", "2024"} def test_multiseed_serializable(): import json d = MultiSeedValidator(_CycleTrader, _pool(3), n_seeds=1).run().to_dict() json.dumps(d) assert {"seeds", "runs", "positive_ratio", "gates", "promoted"} <= set(d) def test_multiseed_empty_pool_raises(): with pytest.raises(ValueError, match="不能为空"): MultiSeedValidator(_CycleTrader, {})