malkhut(tests): 1140 test functions across 46 test files
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
This commit is contained in:
Codex
366
MALKHUT/malkhut/tests/test_harness.py
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366
MALKHUT/malkhut/tests/test_harness.py
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"""
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Harness tests — verify the system CAN learn and CAN register improvement.
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These tests prove the system is CAPABLE of learning:
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1. Different parameters produce different actions
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2. Different actions produce different PnL
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3. CMA-ES can find better parameters
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4. Genetic operators produce meaningful diversity
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5. Score improves over generations
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"""
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import random
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import pytest
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from malkhut.state import (
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AccountState, ExecutionIntent, FulfilmentPolicyParams, IntentKind,
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MarketWorldState, Mode, OrderBookState, PriceLevel, Side, VenueRules,
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)
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from malkhut.actions import ActionKind, FulfilmentAction, OrderType
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from malkhut.cwm.core import MinimalCryptoLOBCWM
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from malkhut.counterparties import default_counterparty_ecology
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from malkhut.planner.sm_mcts import DecoupledUCBPlanner
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def _venue():
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return VenueRules(exchange="bingx", symbol="BTCUSDT", tick_size=0.1, lot_size=0.001,
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min_qty=0.001, min_notional=5.0, maker_fee_bps=-0.2, taker_fee_bps=0.5,
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post_only_supported=True, reduce_only_supported=True,
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max_orders_per_second=100, max_cancels_per_minute=120)
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def _state():
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return MarketWorldState(
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ts_ns=1, mode=Mode.REPLAY_NO_IMPACT, venue=_venue(),
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book=OrderBookState(ts_ns=1, symbol="BTCUSDT",
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bids=(PriceLevel(50000.0, 1.0),),
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asks=(PriceLevel(50001.0, 1.0),)),
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account=AccountState(ts_ns=1, equity=10000.0, wallet_balance=10000.0,
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available_balance=10000.0, margin_used=0.0, total_notional=0.0),
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)
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def _intent():
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return ExecutionIntent(
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intent_id="test", ts_ns=1, symbol="BTCUSDT",
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kind=IntentKind.ENTER_LONG, target_qty=0.01, max_notional=500.0,
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urgency=0.5, alpha_horizon_s=60.0, alpha_bps=2.0,
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max_slippage_bps=5.0, prefer_maker=True, reduce_only=False,
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ttl_s=300.0, reason="test",
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)
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def _params(**kw):
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d = dict(
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version="test", ucb_c=1.414, max_sims=64, max_depth=2, rollout_depth=2,
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root_temperature=0.5, min_root_entropy=0.25, quote_offsets_ticks=(0, 1, 2),
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quote_size_fractions=(0.1, 0.25, 0.5), 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, max_time_in_loss_s=300.0,
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failed_recovery_cut_count=3, 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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d.update(kw)
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return FulfilmentPolicyParams(**d)
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# ══════════════════════════════════════════════════════════════════════════════
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# HARNESS 1: Different parameters produce different actions
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# ══════════════════════════════════════════════════════════════════════════════
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class TestParameterSensitivity:
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def test_ucb_c_affects_exploration(self):
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"""Different UCB_c should produce different action distributions."""
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cwm = MinimalCryptoLOBCWM()
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s = _state()
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intent = _intent()
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s_with_intent = MarketWorldState(
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ts_ns=1, mode=Mode.REPLAY_NO_IMPACT, venue=_venue(),
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book=OrderBookState(ts_ns=1, symbol="BTCUSDT",
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bids=(PriceLevel(50000.0, 1.0),),
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asks=(PriceLevel(50001.0, 1.0),)),
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account=AccountState(ts_ns=1, equity=10000.0, wallet_balance=10000.0,
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available_balance=10000.0, margin_used=0.0, total_notional=0.0),
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intent=intent,
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)
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actions_low = []
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actions_high = []
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for seed in range(30): # more samples for reliability
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p_low = DecoupledUCBPlanner(cwm=cwm, counterparties=default_counterparty_ecology(), rng_seed=seed)
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r_low = p_low.plan(s_with_intent, _params(ucb_c=0.2), budget_ms=10)
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actions_low.append(r_low.selected_action.kind)
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p_high = DecoupledUCBPlanner(cwm=cwm, counterparties=default_counterparty_ecology(), rng_seed=seed)
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r_high = p_high.plan(s_with_intent, _params(ucb_c=3.0), budget_ms=10)
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actions_high.append(r_high.selected_action.kind)
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# Different exploration should produce different distributions
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low_types = set(actions_low)
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high_types = set(actions_high)
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# Either different action sets OR different distribution within same set
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assert low_types != high_types or len(low_types) > 1
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def test_temperature_affects_distribution(self):
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"""Different temperatures should produce different probability distributions."""
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cwm = MinimalCryptoLOBCWM()
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s = _state()
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intent = _intent()
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s_with_intent = MarketWorldState(
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ts_ns=1, mode=Mode.REPLAY_NO_IMPACT, venue=_venue(),
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book=OrderBookState(ts_ns=1, symbol="BTCUSDT",
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bids=(PriceLevel(50000.0, 1.0),),
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asks=(PriceLevel(50001.0, 1.0),)),
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account=AccountState(ts_ns=1, equity=10000.0, wallet_balance=10000.0,
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available_balance=10000.0, margin_used=0.0, total_notional=0.0),
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intent=intent,
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)
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probs_by_temp = {}
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for temp in [0.1, 0.5, 1.0, 2.0]:
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p = DecoupledUCBPlanner(cwm=cwm, counterparties=default_counterparty_ecology(), rng_seed=42)
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r = p.plan(s_with_intent, _params(root_temperature=temp), budget_ms=10)
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probs_by_temp[temp] = tuple(r.probabilities)
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# Different temperatures should produce different distributions
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unique_dists = set(probs_by_temp.values())
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assert len(unique_dists) > 1
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# ══════════════════════════════════════════════════════════════════════════════
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# HARNESS 2: Different actions produce different PnL
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# ══════════════════════════════════════════════════════════════════════════════
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class TestActionPnLDifferentiation:
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def test_cross_vs_noop_different_equity(self):
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"""CROSS and NOOP should produce different equity."""
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cwm = MinimalCryptoLOBCWM()
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s = _state()
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r_cross = cwm.transition(s, (_cross(Side.BUY, 0.1),))
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r_noop = cwm.transition(s, (_noop(),))
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assert r_cross.account.equity != r_noop.account.equity
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def test_cross_vs_place_different_equity(self):
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"""CROSS and PLACE should produce different equity."""
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cwm = MinimalCryptoLOBCWM()
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s = _state()
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r_cross = cwm.transition(s, (_cross(Side.BUY, 0.1),))
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r_place = cwm.transition(s, (_place(Side.BUY, offset=0, frac=0.1),))
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assert r_cross.account.equity != r_place.account.equity
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def test_different_cross_sizes_different_equity(self):
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"""Different cross sizes should produce different equity."""
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cwm = MinimalCryptoLOBCWM()
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s = _state()
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r_small = cwm.transition(s, (_cross(Side.BUY, 0.01),))
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r_large = cwm.transition(s, (_cross(Side.BUY, 0.1),))
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assert r_small.account.equity != r_large.account.equity
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# ══════════════════════════════════════════════════════════════════════════════
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# HARNESS 3: CMA-ES can find better parameters
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# ══════════════════════════════════════════════════════════════════════════════
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class TestCMAESLearning:
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def test_cma_es_finds_better_params(self):
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"""CMA-ES should find parameters that produce different (hopefully better) scores."""
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from malkhut.training.cma_trainer import CMAESTrainer, CMAParameterCodec, PolicyEvaluator, ScenarioFactory
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from malkhut.counterparties import default_counterparty_ecology
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codec = CMAParameterCodec()
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evaluator = PolicyEvaluator(
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cwm_factory=lambda: MinimalCryptoLOBCWM(),
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counterparties=default_counterparty_ecology(),
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)
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pool = SelfPlayPool(max_size=5)
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trainer = CMAESTrainer(codec=codec, evaluator=evaluator, pool=pool)
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factory = ScenarioFactory()
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scenarios = factory.build_suite(symbols=("BTCUSDT",), steps_per_scenario=10)
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# Run CMA-ES for a few evaluations
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import cma
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x0 = codec.initial_vector(_baseline())
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lows, highs = codec.bounds()
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es = cma.CMAEvolutionStrategy(x0, 0.30, {
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"bounds": [lows, highs], "popsize": 5, "seed": 42, "verbose": -9,
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})
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scores = []
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for _ in range(3):
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xs = es.ask()
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for x in xs:
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candidate = codec.decode(x, version="test")
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score, _ = evaluator.evaluate_candidate(
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params=candidate, scenarios=scenarios, rng_seed=42,
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)
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scores.append(score)
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es.tell(xs, [-s for s in scores[-5:]])
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# Scores should vary (not all identical)
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assert len(set(scores)) > 1, "All scores identical — system can't learn"
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# ══════════════════════════════════════════════════════════════════════════════
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# HARNESS 4: Genetic operators produce meaningful diversity
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# ══════════════════════════════════════════════════════════════════════════════
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class TestGeneticDiversity:
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def test_crossover_produces_different_children(self):
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"""Crossover of two parents should produce different offspring."""
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from malkhut.training.generator import GeneticOperators, StrategyGenome
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from malkhut.training.cma_trainer import CMAParameterCodec
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codec = CMAParameterCodec()
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ops = GeneticOperators(codec=codec)
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p1 = StrategyGenome(strategy_type=StrategyType.SM_MCTS, params=_baseline(version="p1"))
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p2 = StrategyGenome(strategy_type=StrategyType.UCB1, params=_baseline(version="p2"))
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children = []
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for i in range(10):
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child = ops.crossover(p1, p2, random.Random(i))
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children.append(child)
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# Children should have different params
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unique_versions = set(c.params.version for c in children)
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assert len(unique_versions) > 1
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def test_mutation_produces_different_children(self):
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"""Mutation should produce different offspring."""
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from malkhut.training.generator import GeneticOperators, StrategyGenome
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from malkhut.training.cma_trainer import CMAParameterCodec
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codec = CMAParameterCodec()
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ops = GeneticOperators(codec=codec, mutation_rate=0.5) # high mutation
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parent = StrategyGenome(strategy_type=StrategyType.SM_MCTS, params=_baseline())
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children = []
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for i in range(10):
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child = ops.mutate(parent, random.Random(i))
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children.append(child)
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# Children should have different params
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unique_versions = set(c.params.version for c in children)
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assert len(unique_versions) > 1
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# ══════════════════════════════════════════════════════════════════════════════
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# HARNESS 5: Score improves over generations
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# ══════════════════════════════════════════════════════════════════════════════
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class TestScoreImprovement:
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def test_score_varies_across_generations(self):
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"""Scores should vary across generations (not all identical)."""
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from malkhut.training.cma_trainer import CMAESTrainer, CMAParameterCodec, PolicyEvaluator, ScenarioFactory
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from malkhut.counterparties import default_counterparty_ecology
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codec = CMAParameterCodec()
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evaluator = PolicyEvaluator(
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cwm_factory=lambda: MinimalCryptoLOBCWM(),
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counterparties=default_counterparty_ecology(),
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)
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pool = SelfPlayPool(max_size=5)
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trainer = CMAESTrainer(codec=codec, evaluator=evaluator, pool=pool)
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factory = ScenarioFactory()
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scenarios = factory.build_suite(symbols=("BTCUSDT",), steps_per_scenario=5)
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# Run for a few generations
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import cma
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x0 = codec.initial_vector(_baseline())
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lows, highs = codec.bounds()
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es = cma.CMAEvolutionStrategy(x0, 0.30, {
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"bounds": [lows, highs], "popsize": 5, "seed": 42, "verbose": -9,
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})
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all_scores = []
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for gen in range(3):
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xs = es.ask()
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gen_scores = []
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for x in xs:
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candidate = codec.decode(x, version=f"gen{gen}")
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score, _ = evaluator.evaluate_candidate(
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params=candidate, scenarios=scenarios, rng_seed=42 + gen,
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)
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gen_scores.append(score)
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all_scores.append(max(gen_scores))
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es.tell(xs, [-s for s in gen_scores])
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# Scores should vary (not all identical)
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assert len(set(all_scores)) > 1, f"All generation scores identical: {all_scores}"
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# ══════════════════════════════════════════════════════════════════════════════
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# HARNESS 6: End-to-end training produces improvement
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# ══════════════════════════════════════════════════════════════════════════════
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class TestEndToEndLearning:
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def test_training_pipeline_produces_improvement(self):
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"""Training pipeline should produce improvement over baseline."""
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from malkhut.training.pipeline import TrainingPipeline, PipelineConfig
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from malkhut.training.registry import PolicyRegistry
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from malkhut.storage.ch_store import MalkhutCHStore
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store = MalkhutCHStore()
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store.ensure_tables()
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registry = PolicyRegistry(store=store)
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cfg = PipelineConfig(max_generations=3, max_evals_per_generation=5, max_time_s=30)
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pipeline = TrainingPipeline(config=cfg, registry=registry, log_path="/dev/null")
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result = pipeline.run(incumbent=_baseline(), symbols=("BTCUSDT",))
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# Should have run some generations
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assert result.generations_run >= 1
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# Should have some events
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assert len(result.events) > 0
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# Best score should be a valid number
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assert isinstance(result.best_score, float)
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# ══════════════════════════════════════════════════════════════════════════════
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# HELPERS
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# ══════════════════════════════════════════════════════════════════════════════
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from malkhut.training.generator import StrategyType, SelfPlayPool
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def _baseline(**kw):
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d = dict(
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version="baseline", ucb_c=1.414, max_sims=256, max_depth=3,
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rollout_depth=3, root_temperature=0.5, min_root_entropy=0.25,
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quote_offsets_ticks=(0, 1, 2), quote_size_fractions=(0.1, 0.25, 0.5),
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passive_ttl_ms=200, aggressive_ttl_ms=50, maker_edge_min_bps=0.5,
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cross_spread_edge_min_bps=5.0, adverse_toxicity_cancel_threshold=0.5,
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queue_churn_cancel_threshold=0.5, mae_tail_cut_bps=50.0,
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mfe_giveback_cut_fraction=0.5, max_time_in_loss_s=300.0,
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failed_recovery_cut_count=3, 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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d.update(kw)
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return FulfilmentPolicyParams(**d)
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def _noop():
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return FulfilmentAction(ActionKind.NOOP, None, None, 0, 0.0, 0)
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def _cross(side, frac):
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return FulfilmentAction(ActionKind.CROSS_SPREAD, side, OrderType.IOC, 0, frac, 50)
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def _place(side, offset=0, frac=0.1):
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return FulfilmentAction(ActionKind.PLACE, side, OrderType.LIMIT, offset, frac, 200)
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