39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
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from __future__ import annotations
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import pytest
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torch = pytest.importorskip("torch")
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import json
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from gpu_feature_parity_contract_v1_1 import deterministic_adversarial_ohlcv # noqa: E402
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from gpu_feature_engine_v1_2 import supertrend_trace # noqa: E402
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from gpu_feature_parity_contract_v1_2 import cpu_supertrend_trace # noqa: E402
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from gpu_supertrend_v1_2_diagnostic import diagnose # noqa: E402
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def test_period_10_semantic_diagnostic_has_serializable_cpu_gpu_trace_statistics():
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ohlcv = deterministic_adversarial_ohlcv(length=96, seed=11)
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report = diagnose(ohlcv, device=torch.device("cpu"))
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assert json.dumps(report, allow_nan=False)
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assert [record["multiplier"] for record in report["records"]] == [2.0, 3.0, 4.0]
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for record in report["records"]:
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assert set(record["numeric"]) == {"output", "true_range", "atr", "basic_upper", "basic_lower", "upper", "lower"}
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assert record["numeric"]["atr"]["first_divergence"] is None
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assert record["direction"]["exact"]
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assert record["transitions"]["direction_transition"]["exact"]
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assert all(item["exact"] for item in record["branch_predicates"].values())
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def test_cpu_trace_exposes_the_complete_gpu_trace_surface():
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ohlcv = deterministic_adversarial_ohlcv(length=96, seed=11)
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cpu = cpu_supertrend_trace(ohlcv["close"], ohlcv["high"], ohlcv["low"], 10, 3.0)
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gpu = supertrend_trace(
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torch.as_tensor(ohlcv["close"], dtype=torch.float64),
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torch.as_tensor(ohlcv["high"], dtype=torch.float64),
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torch.as_tensor(ohlcv["low"], dtype=torch.float64),
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10,
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3.0,
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)
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assert set(cpu) == set(gpu)
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