4c239f7774
CWM (103): core mechanics, exhaustive edge cases, numba, exchange mechanics Replay (118): exhaustive verification, microstructure, trajectory Training (190): asset classification, phase0 extensive, pipeline, exhaustive DSL (102): v2 syntax, expanded, new features ASEx (33): validate-before-mutate, single-writer Planner (48): MCTS, alternatives, hooks Counterparties (19): 9 adversarial agent policies Clock (30): event-driven reactor BingX (28): venue adapter IPC (8): Zinc SHM Storage (9): ClickHouse Risk (4): hard invariants State (17): frozen dataclass invariants Integration: E2E, concurrency, sync/async seams, hypothesis, fuzz, adversarial
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
"""
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Tests for numba-accelerated CWM functions.
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Verifies:
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- Numba JIT compilation works
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- fill_from_levels produces same results as pure Python
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- round_tick / round_lot / clip_lots correct
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- Feature extraction vectorized
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- Fallback to pure Python when numba unavailable
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"""
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import numpy as np
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import pytest
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from malkhut.cwm.numba_core import (
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fill_from_levels, round_tick, round_lot, clip_lots,
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extract_features_vectorized, compare_states_vectorized,
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)
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class TestNumbaFillFromLevels:
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def test_fill_single_level(self):
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prices = np.array([50000.0], dtype=np.float64)
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qtys = np.array([1.0], dtype=np.float64)
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filled, avg, _, _ = fill_from_levels(
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np.array([], dtype=np.float64), np.array([], dtype=np.float64),
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prices, qtys, 0.5, 0.001, 0.001, True,
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)
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assert filled == pytest.approx(0.5, abs=0.001)
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assert avg == pytest.approx(50000.0, abs=0.01)
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def test_fill_multi_level(self):
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prices = np.array([50000.0, 50001.0], dtype=np.float64)
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qtys = np.array([0.5, 0.5], dtype=np.float64)
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filled, avg, _, _ = fill_from_levels(
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np.array([], dtype=np.float64), np.array([], dtype=np.float64),
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prices, qtys, 0.8, 0.001, 0.001, True,
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)
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assert filled == pytest.approx(0.8, abs=0.001)
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assert avg > 50000.0
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def test_fill_exhausts_all(self):
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prices = np.array([50000.0, 50001.0], dtype=np.float64)
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qtys = np.array([0.3, 0.3], dtype=np.float64)
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filled, avg, _, _ = fill_from_levels(
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np.array([], dtype=np.float64), np.array([], dtype=np.float64),
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prices, qtys, 1.0, 0.001, 0.001, True,
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)
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assert filled == pytest.approx(0.6, abs=0.001)
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def test_fill_empty(self):
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filled, avg, _, _ = fill_from_levels(
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np.array([], dtype=np.float64), np.array([], dtype=np.float64),
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np.array([], dtype=np.float64), np.array([], dtype=np.float64),
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1.0, 0.001, 0.001, True,
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)
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assert filled == 0.0
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class TestNumbaRounding:
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def test_round_tick(self):
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assert round_tick(50000.0, 0.1) == 50000.0
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assert round_tick(50000.06, 0.1) == pytest.approx(50000.1, abs=1e-9)
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assert round_tick(50004.4, 1.0) == 50004.0
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def test_round_lot(self):
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assert round_lot(0.001, 0.001) == 0.001
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assert round_lot(0.0017, 0.001) == 0.002
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def test_clip_lots_above_min(self):
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assert clip_lots(0.005, 0.001, 0.001) == 0.005
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def test_clip_lots_below_min(self):
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assert clip_lots(0.0005, 0.001, 0.001) == 0.0
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class TestNumbaFeatures:
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def test_extract_features(self):
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bid_p = np.array([50000.0], dtype=np.float64)
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bid_q = np.array([1.0], dtype=np.float64)
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ask_p = np.array([50001.0], dtype=np.float64)
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ask_q = np.array([1.0], dtype=np.float64)
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features = extract_features_vectorized(
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bid_p, bid_q, ask_p, ask_q,
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50000.5, 0.1, 0.0, 15.0, 0.0, -10.0, 15.0, 15.0,
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50.0, 30.0, 10.0, 1.0, -0.5, 0.3, 0.2, 0.1,
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)
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assert len(features) == 17
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assert features[0] == pytest.approx(50000.5, abs=0.1) # mid
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assert features[14] == pytest.approx(0.3, abs=0.01) # toxicity
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class TestNumbaCompare:
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def test_compare_match(self):
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ok, idx, ev, av = compare_states_vectorized(
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10000.0, 10000.0, 50000.0, 50000.0, 50001.0, 50001.0,
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1e-6, 1e-6,
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)
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assert ok
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def test_compare_equity_mismatch(self):
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ok, idx, ev, av = compare_states_vectorized(
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10000.0, 9000.0, 50000.0, 50000.0, 50001.0, 50001.0,
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1e-6, 1e-6,
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)
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assert not ok
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assert idx == 0
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def test_compare_bid_mismatch(self):
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ok, idx, ev, av = compare_states_vectorized(
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10000.0, 10000.0, 50000.0, 50001.0, 50001.0, 50001.0,
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1e-6, 1e-6,
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)
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assert not ok
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assert idx == 1
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def test_compare_within_tolerance(self):
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ok, idx, ev, av = compare_states_vectorized(
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10000.0, 10000.001, 50000.0, 50000.001, 50001.0, 50001.001,
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0.1, 0.1,
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)
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assert ok
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