malkhut(docs + bench): comprehensive update + smoke test script
README updated with: - Vectorized UCB selection (7.7x speedup, 1.13µs/selection) - Batch MCTS kernel (numba-accelerated) - Fast scalar + advantage scoring modes - Updated performance benchmarks (1186 tests, 390 scenarios, 3043 score/min) - Advantage scorer module in package structure smoke_1h.py: standalone training script for extended runs. Total session: 19 commits, 1186 tests, all green. All implementations: parallel eval (7x), vectorized reward (numba), vectorized UCB (7.7x), fast scalar scoring, advantage mode, DuckDB store (sub-µs reads), asset compiler, behavior DSL, multi-exchange support, three-layer identifiers.
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@@ -144,6 +144,7 @@ MALKHUT/
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│ │ ├── parallel_eval.py # ProcessPoolExecutor episode runner
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│ │ ├── ray_eval.py # Ray-based eval (industrial alternative)
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│ │ ├── vbt_analysis.py # Post-sim metrics: Sharpe, Sortino, VaR
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│ │ ├── advantage_scorer.py # Advantage estimation for offline analysis
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│ │ ├── cognition.py # Rate-limited market regime research
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│ │ ├── regime_expansion.py # 200+ regimes from dimension combinations
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│ │ ├── news_sources.py # 12 industry-standard news sources
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@@ -402,14 +403,14 @@ simple doctrinal tick-exits (C11) ship first via T19 step 3; MALKHUT supersedes
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## DEVELOPMENT STATUS (2026-07-13)
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**1178 test functions. 50 test files. All green. 0 failures. 0 regressions.**
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**1186 test functions. 50 test files. All green. 0 failures. 0 regressions.**
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### Completed subsystems
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| Subsystem | Module | Tests | Status |
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|-----------|--------|-------|--------|
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| **State Model** | `state.py` | 17 | 42 frozen dataclasses, immutable |
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| **CWM** | `cwm/core.py` + `cwm/numba_core.py` | 103 | Exchange mechanics + numba JIT (5.3µs/transition) + vectorized reward |
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| **CWM** | `cwm/core.py` + `cwm/numba_core.py` | 103 | Exchange mechanics + numba JIT (5.3µs/transition) + vectorized reward + vectorized UCB + batch MCTS kernel |
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| **Replay Verification** | `cwm/replay_verify.py` | 65 | Deep comparison, binary search, trajectory recording |
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| **Planner** | `planner/sm_mcts.py` | 11 | Decoupled UCB/UCT, ≤25ms budget |
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| **Action Menu** | `planner/action_menu.py` | (in planner) | Compact action space construction |
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@@ -424,6 +425,7 @@ simple doctrinal tick-exits (C11) ship first via T19 step 3; MALKHUT supersedes
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| **CMA-ES Training** | `training/cma_trainer.py` | 65 | Behavior-driven, auto-compile, parallel workers, 7x speedup |
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| **Parallel Eval** | `training/parallel_eval.py` | 16 | ProcessPoolExecutor, 7x CMA-ES speedup |
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| **Ray Eval** | `training/ray_eval.py` | 5 | Ray-based eval (available, slower for ≤1K scenarios) |
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| **Scoring Modes** | `cma_trainer.py` + `advantage_scorer.py` | 8 | Fast scalar (CMA loop) + advantage (offline analysis) |
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| **VBT Analysis** | `training/vbt_analysis.py` | 8 | Post-sim trade metrics: Sharpe, Sortino, VaR, cross-asset |
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| **Policy Registry** | `training/registry.py` | 14 | CANDIDATE → ACTIVE lifecycle |
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| **Training Pipeline** | `training/pipeline.py` | 21 | Bounded continuous learning loop |
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@@ -451,17 +453,18 @@ simple doctrinal tick-exits (C11) ship first via T19 step 3; MALKHUT supersedes
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| Metric | Value |
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|--------|-------|
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| CWM transition | 5.3 µs/call (numba JIT) |
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| CWM throughput | 189K calls/sec |
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| CWM 100-step episode | 0.64 ms |
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| CWM reward (numba vectorized) | ~0.3µs (was 2µs with dict) |
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| CWM throughput | 189K calls/sec (numba JIT) |
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| CWM per-call latency | 5.3 µs |
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| CWM reward (numba vectorized) | ~0.3µs |
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| UCB selection (numba vectorized) | 1.13µs (was 8.7µs, 7.7x speedup) |
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| Numba fill speedup | 1.8x (batch 100) |
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| DuckDB asset reads | 0.2µs (in-memory) |
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| Scenario generation | 390 scenarios in 0.8s |
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| CMA-ES parallel (8 workers) | 7× speedup, 87% efficiency |
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| Best CMA-ES score (48 evals) | 2,594 (parallel) vs 1,727 (sequential) |
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| Score/min (8 workers) | 1,718 (was 164 sequential) |
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| Peak RAM | 146 MB |
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| Best CMA-ES score (48 evals) | 8,628 (fast scalar, 100.4 bps PnL) |
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| Score/min (8 workers) | 3,043 |
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| Episode throughput (parallel) | 16ms/ep |
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| Episode throughput (sequential) | 23ms/ep |
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### Bugs found and fixed (22 total)
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97
MALKHUT/smoke_1h.py
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97
MALKHUT/smoke_1h.py
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#!/usr/bin/env python3
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"""MALKHUT 1h+ Training Smoke — standalone script for background execution."""
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import time, os, sys, json
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LOG = "/mnt/dolphinng5_predict/MALKHUT/smoke_1h_run.log"
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BUDGET = 192
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WORKERS = min(os.cpu_count() or 4, 8)
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with open(LOG, "w") as f:
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f.write(f"MALKHUT 1H+ TRAINING SMOKE\n")
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f.write(f"Start: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
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f.write(f"Config: budget={BUDGET} workers={WORKERS} pop=12\n")
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f.write(f"Assets: BTC/ETH/SOL (3 × 30 scenarios = 90)\n\n")
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f.flush()
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print(f"Starting 1h+ smoke: budget={BUDGET} workers={WORKERS}", flush=True)
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from malkhut.training.cma_trainer import (
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ScenarioFactory, PolicyEvaluator, CMAESTrainer, CMAParameterCodec, SelfPlayPool
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)
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from malkhut.cwm.core import MinimalCryptoLOBCWM
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from malkhut.state import FulfilmentPolicyParams
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import cma as cma_lib
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factory = ScenarioFactory()
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suite = factory.build_suite(symbols=('BTCUSDT', 'ETHUSDT', 'SOLUSDT'), steps_per_scenario=5)
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evaluator = PolicyEvaluator(cwm_factory=MinimalCryptoLOBCWM)
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codec = CMAParameterCodec()
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pool = SelfPlayPool()
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params = FulfilmentPolicyParams(
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version='baseline', 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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x0 = codec.initial_vector(params)
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lows, highs = codec.bounds()
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es = cma_lib.CMAEvolutionStrategy(x0, sigma0=0.30,
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inopts={"bounds": [lows, highs], "popsize": 12, "seed": 42, "verbose": -9})
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eval_count, gen_best, gen_pnl = 0, [], []
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t0 = time.time()
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next_log = 120
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with open(LOG, "a") as f:
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while not es.stop() and eval_count < BUDGET:
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xs = es.ask()
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losses, gs, gp = [], [], []
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for x in xs:
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if eval_count >= BUDGET:
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break
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cand = codec.decode(x, version=f"e{eval_count}")
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score, results = evaluator.evaluate_candidate(
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params=cand, scenarios=suite, rng_seed=eval_count,
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planner_type='sm_mcts', workers=WORKERS)
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pnl = sum(r.pnl_bps for r in results) / max(len(results), 1)
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losses.append(-score); gs.append(score); gp.append(pnl)
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eval_count += 1
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if gs:
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es.tell(xs[:len(losses)], losses)
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gen_best.append(max(gs))
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gen_pnl.append(sum(gp)/len(gp))
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elapsed = time.time() - t0
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if elapsed >= next_log:
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msg = (f"[{elapsed:.0f}s] Gen {len(gen_best)} | "
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f"{eval_count}/{BUDGET} evals | best={max(gen_best):,.0f} | "
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f"pnl={gen_pnl[-1]:.1f}bps | {eval_count/elapsed:.2f}e/s")
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f.write(msg + "\n"); f.flush()
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print(msg, flush=True)
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next_log += 120
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total = time.time() - t0
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summary = (f"\n{'='*60}\nCOMPLETE\n"
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f" Duration: {total:.0f}s ({total/60:.1f}min)\n"
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f" Evals: {eval_count}/{BUDGET}\n"
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f" Rate: {eval_count/total:.2f} eval/s\n"
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f" Best score: {max(gen_best):,.0f} (gen {gen_best.index(max(gen_best))+1}/{len(gen_best)})\n"
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f" Final gen PnL: {gen_pnl[-1]:.1f}bps\n"
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f" Score curve (last 8): {gen_best[-8:]}\n"
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f" PnL curve (last 8): {[f'{p:.0f}' for p in gen_pnl[-8:]]}\n"
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f"{'='*60}\n")
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f.write(summary); f.flush()
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print(summary, flush=True)
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