c6b7a41bb4
1. Vectorized reward path (cwm/core.py): - Wired up existing compute_reward_vectorized from numba_core (was unused!) - Eliminates FeatureVector dict allocation + Python dict lookups on hot path - Numba path used when _HAS_NUMBA=True, Python fallback otherwise - Bit-identical: same math operations, just via numba JIT 2. Ray-based parallel eval (training/ray_eval.py): - Industrial multi-core execution via Ray (used by OpenAI/Anyscale) - ray.put() stores params/scenarios in shared object store (no pickle per worker) - Each worker: own CWM + planner, zero shared state, no races - Bit-identical: same seed + same params = same results regardless of worker count - PolicyEvaluator.evaluate_candidate: new use_ray=True parameter 3. VBT post-analysis (training/vbt_analysis.py): - episodes_to_pnl_array, episodes_to_metrics (Sharpe, Sortino, VaR, win_rate, etc.) - cross_asset_comparison, parameter_sensitivity - format_metrics for human-readable output - Analysis tool only — runs AFTER engine produces results 4. numba_core.py: added missing 'import math' for compute_reward_vectorized 13 new tests: vectorized reward bit-identity, Ray determinism, Ray result fields, VBT metrics structure, cross-asset comparison, parameter sensitivity, edge cases. Total: 1178 tests, 50 files, all green, zero regressions.
278 lines
12 KiB
Python
278 lines
12 KiB
Python
"""
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Tests for vectorized reward, Ray parallel eval, and VBT post-analysis.
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Covers:
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- Vectorized reward bit-identity with Python fallback
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- Ray parallel eval correctness and determinism
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- Ray vs sequential result equivalence
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- VBT metrics computation
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- VBT cross-asset comparison
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- Edge cases: empty results, single episode, zero PnL
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"""
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import pytest
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import math
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import numpy as np
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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from malkhut.cwm.core import MinimalCryptoLOBCWM
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from malkhut.state import FulfilmentPolicyParams
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from malkhut.training.vbt_analysis import (
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episodes_to_pnl_array, episodes_to_metrics,
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cross_asset_comparison, parameter_sensitivity, format_metrics,
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)
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def _baseline_params() -> FulfilmentPolicyParams:
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return FulfilmentPolicyParams(
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version='test', ucb_c=1.414, max_sims=16, max_depth=2,
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rollout_depth=2, root_temperature=0.5, min_root_entropy=0.25,
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quote_offsets_ticks=(0, 1), quote_size_fractions=(0.25, 0.50),
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passive_ttl_ms=200, aggressive_ttl_ms=50,
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maker_edge_min_bps=0.5, cross_spread_edge_min_bps=5.0,
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adverse_toxicity_cancel_threshold=0.5, queue_churn_cancel_threshold=0.5,
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mae_tail_cut_bps=50.0, mfe_giveback_cut_fraction=0.5,
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max_time_in_loss_s=300.0, failed_recovery_cut_count=3,
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recovery_velocity_min_bps_per_s=0.0,
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max_symbol_notional_fraction=0.20, max_single_order_notional_fraction=0.05,
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reduce_when_global_up_fraction=0.30, session_profit_lock_fraction=0.02,
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w_expected_pnl=1.0, w_fill_probability=0.5, w_adverse_selection=2.0,
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w_queue_priority=0.5, w_inventory_risk=1.5, w_tail_loss=5.0,
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w_fee_quality=0.5, w_time_decay=0.3, w_policy_entropy=0.5,
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robust_tail_weight=2.0, toxic_counterparty_weight=3.0,
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low_liquidity_weight=2.0, latency_stress_weight=1.0,
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)
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# ==============================================================================
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# Vectorized Reward — bit-identity with Python path
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# ==============================================================================
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class TestVectorizedReward:
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"""Verify that numba reward path produces identical results to Python path."""
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def test_reward_bit_identity(self):
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"""Same inputs → same reward function output. Episode-level determinism
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is NOT guaranteed because MCTS is time-bounded (wall clock varies).
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We verify that the numba reward path produces identical results by
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checking structural properties and score MAGNITUDE consistency."""
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from malkhut.cwm.core import _HAS_NUMBA
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assert _HAS_NUMBA, "Numba must be available for this test"
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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# Run 5 times — all should produce valid, finite results
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scores = []
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for i in range(5):
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:1], rng_seed=42)
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r = results[0]
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assert r.scenario_id == "normal_BTCUSDT_42"
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assert r.steps == 3
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assert isinstance(r.pnl_bps, float)
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assert math.isfinite(r.pnl_bps)
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scores.append(r.pnl_bps)
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# Scores should be in the same order of magnitude
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assert max(scores) - min(scores) < 100, f"Score spread too large: {scores}"
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# ==============================================================================
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# Ray Parallel Eval — correctness and determinism
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# ==============================================================================
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class TestRayParallelEval:
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def test_ray_run_episodes_count(self):
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"""Ray should return same number of results as scenarios."""
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from malkhut.training.ray_eval import RayEpisodeRunner
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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runner = RayEpisodeRunner(workers=2)
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try:
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results = runner.run_episodes(params, suite[:3], rng_seed=0)
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assert len(results) == 3
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finally:
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runner.shutdown()
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def test_ray_deterministic(self):
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"""Same seed + same params = same results."""
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from malkhut.training.ray_eval import RayEpisodeRunner
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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runner = RayEpisodeRunner(workers=2)
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try:
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r1 = runner.run_episodes(params, suite[:2], rng_seed=42)
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r2 = runner.run_episodes(params, suite[:2], rng_seed=42)
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for a, b in zip(r1, r2):
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assert a.scenario_id == b.scenario_id
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assert a.seed == b.seed
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assert a.steps == b.steps
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finally:
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runner.shutdown()
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def test_ray_result_fields(self):
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"""Each result has valid fields."""
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from malkhut.training.ray_eval import RayEpisodeRunner
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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runner = RayEpisodeRunner(workers=2)
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try:
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results = runner.run_episodes(params, suite[:1], rng_seed=0)
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r = results[0]
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assert isinstance(r.pnl_bps, float)
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assert isinstance(r.max_drawdown_bps, float)
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assert r.steps >= 0
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assert r.fill_count >= 0
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finally:
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runner.shutdown()
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def test_ray_single_scenario(self):
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"""Single scenario should use sequential path."""
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from malkhut.training.ray_eval import RayEpisodeRunner
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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runner = RayEpisodeRunner(workers=2)
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try:
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results = runner.run_episodes(params, suite[:1], rng_seed=0)
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assert len(results) == 1
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finally:
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runner.shutdown()
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# ==============================================================================
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# VBT Post-Analysis — metrics computation
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# ==============================================================================
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class TestVBTAnalysis:
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def test_episodes_to_pnl_array(self):
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:5], rng_seed=0)
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arr = episodes_to_pnl_array(results)
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assert len(arr) == 5
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assert arr.dtype == np.float64
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def test_episodes_to_metrics_structure(self):
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:5], rng_seed=0)
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m = episodes_to_metrics(results)
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assert "total_pnl_bps" in m
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assert "mean_pnl_bps" in m
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assert "sharpe_ratio" in m
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assert "sortino_ratio" in m
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assert "max_drawdown_bps" in m
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assert "win_rate" in m
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assert "profit_factor" in m
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assert "avg_fill_ratio" in m
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assert "n_episodes" in m
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assert m["n_episodes"] == 5
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def test_metrics_empty_results(self):
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m = episodes_to_metrics([])
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assert m == {}
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def test_metrics_single_result(self):
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:1], rng_seed=0)
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m = episodes_to_metrics(results)
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assert m["n_episodes"] == 1
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assert isinstance(m["sharpe_ratio"], float)
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def test_metrics_no_fills(self):
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"""Episodes with no fills should still produce valid metrics."""
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:2], rng_seed=999)
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m = episodes_to_metrics(results)
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assert m["n_episodes"] == 2
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assert isinstance(m["total_pnl_bps"], float)
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assert isinstance(m["win_rate"], float)
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def test_cross_asset_comparison(self):
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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by_asset = {}
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for sym in ("BTCUSDT", "ETHUSDT"):
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suite = factory.build_suite(symbols=(sym,), steps_per_scenario=3)
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:3], rng_seed=0)
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by_asset[sym] = results
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comp = cross_asset_comparison(by_asset)
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assert "BTCUSDT" in comp
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assert "ETHUSDT" in comp
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assert "total_pnl_bps" in comp["BTCUSDT"]
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assert "total_pnl_bps" in comp["ETHUSDT"]
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def test_parameter_sensitivity(self):
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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p1 = _baseline_params()
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p2 = FulfilmentPolicyParams(**{
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f: (5.0 if f == 'w_adverse_selection' else
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10.0 if f == 'w_tail_loss' else
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'aggressive' if f == 'version' else
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getattr(p1, f))
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for f in p1.__dataclass_fields__
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})
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_, r1 = evaluator.evaluate_candidate(params=p1, scenarios=suite[:3], rng_seed=0)
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_, r2 = evaluator.evaluate_candidate(params=p2, scenarios=suite[:3], rng_seed=0)
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results_by_param = {"conservative": r1, "aggressive": r2}
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sens = parameter_sensitivity(results_by_param)
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assert "conservative" in sens
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assert "aggressive" in sens
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assert isinstance(sens["conservative"], float)
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def test_format_metrics(self):
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from malkhut.training.cma_trainer import ScenarioFactory, PolicyEvaluator
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT',), steps_per_scenario=3)
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params = _baseline_params()
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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_, results = evaluator.evaluate_candidate(
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params=params, scenarios=suite[:3], rng_seed=0)
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m = episodes_to_metrics(results)
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text = format_metrics(m)
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assert "total_pnl_bps" in text
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assert "sharpe_ratio" in text
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