danielmjrr
160ed2f948
Memory-mapped tickstores, a nogil numba replay kernel validated trade-for-trade against an independent reference implementation, purged walk-forward CV, an append-only FDR registry, tick-exact labels, and online changepoint detection. 129 tests, 4 runnable examples on synthetic data, CI for linux/macos.
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
"""Sweeps must fan out without scrambling which result belongs to which config."""
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import time
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import numpy as np
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import pytest
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import tickreplay as tr
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def crossover_case():
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time_ns, bid, ask = tr.synth.generate_ticks(days=3, ticks_per_day=8000, seed=5)
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store = tr.TickStore(time_ns, bid, ask)
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bars = store.bars("M5")
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trend = np.sign(bars["close"].rolling(5).mean() - bars["close"].rolling(20).mean())
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signal = trend.fillna(0).to_numpy().astype("int8")
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signal[signal == np.roll(signal, 1)] = 0 # act on crosses only
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return store, bars, signal
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def test_sweep_exits_returns_one_row_per_config_matching_a_direct_replay():
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store, bars, signal = crossover_case()
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base = {"max_hold": "1h", "cost": 0.09}
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grid = [{"sl": sl, "tp": tp} for sl in (1.0, 3.0) for tp in (2.0, 6.0)]
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df = tr.sweep_exits(store, bars, signal, grid, base=base)
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assert len(df) == len(grid)
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assert df["n_trades"].min() > 0
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assert df["total_net"].nunique() == len(grid), "grid must actually discriminate"
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for row, overrides in zip(df.to_dict("records"), grid):
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assert (row["sl"], row["tp"]) == (overrides["sl"], overrides["tp"])
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expected = tr.replay(store, bars, signal,
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tr.Config(**base, **overrides)).summary()
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assert set(expected) <= set(row)
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for key, value in expected.items():
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assert row[key] == pytest.approx(value), f"{overrides}: {key}"
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def test_sweep_exits_applies_base_under_the_grid():
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store, bars, signal = crossover_case()
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grid = [{"sl": 2.0}]
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with_hold = tr.sweep_exits(store, bars, signal, grid, base={"max_hold": "15m"})
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without_hold = tr.sweep_exits(store, bars, signal, grid)
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assert with_hold.loc[0, "n_trades"] != without_hold.loc[0, "n_trades"]
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def test_run_sweep_preserves_config_to_result_pairing():
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# descending delays make workers finish in roughly reverse submission order
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configs = [{"idx": i, "delay": (7 - i) * 0.02} for i in range(8)]
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def evaluate(config):
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time.sleep(config["delay"])
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return {"echo": config["idx"] * 10}
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df = tr.run_sweep(evaluate, configs, workers=4)
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assert list(df["idx"]) == [c["idx"] for c in configs]
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assert list(df["echo"]) == [c["idx"] * 10 for c in configs]
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def test_run_sweep_runs_configs_concurrently():
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def evaluate(config):
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time.sleep(0.1)
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return {"idx_seen": config["idx"]}
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configs = [{"idx": i} for i in range(8)]
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started = time.perf_counter()
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df = tr.run_sweep(evaluate, configs, workers=8)
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elapsed = time.perf_counter() - started
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assert list(df["idx_seen"]) == list(range(8))
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assert elapsed < 0.4, f"8 x 100ms across 8 workers took {elapsed:.2f}s -- serialized?"
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