Codex
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be0e1468da
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malkhut(perf): parallel episode eval — 9x single-eval speedup, zero fidelity loss
- parallel_eval.py: ProcessPoolExecutor-based episode runner. Each worker
gets its own CWM + planner instance. Zero shared state = embarrassingly
parallel. Deterministic: same seed → same result.
- PolicyEvaluator.evaluate_candidate: new workers parameter (0=sequential,
>1=parallel). Backward compatible: default workers=0.
- 16 new tests: determinism, pickling, result validity, cross-validation
between sequential and parallel paths, backward compatibility.
- README: training performance table with speedup measurements.
Speedup results (3 assets × 30 scenarios = 90 scenarios):
Sequential: 3.3s per eval (1.0x)
2 workers: 1.3s per eval (2.6x)
4 workers: 0.6s per eval (5.9x)
8 workers: 0.4s per eval (9.1x)
CMA-ES 48 evals: 125s → 85s (1.5x training speedup)
Note: CWM numba hot path was already wired (_HAS_NUMBA=True, 5.3µs/transition).
Bottleneck is MCTS planner (96% of eval time), not CWM.
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2026-07-11 20:19:32 +02:00 |
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Codex
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dd86174107
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malkhut(T8): cognition pipeline + regime expansion + prod tooling
Cognition pipeline (cognition.py): rate-limited, 8 sources, dedup, perm-run.
Regime expansion (regime_expansion.py): 200+ regimes from 4x4x4x4 dimensions.
News sources (news_sources.py): 12 industry-standard sources with ranking.
Monitor (monitor.py): metrics, health scoring, alerts, JSONL logging.
Cognition launcher (cognition_launcher.py): standalone long-run service.
Continuous pipeline (continuous_pipeline.py): forever-loop training runner.
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2026-07-11 10:39:03 +02:00 |
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Codex
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ef2f8e8827
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malkhut(T6): training core — CMA-ES trainer, registry, pipeline, selector
CMA-ES trainer (cma_trainer.py): self-play pool, bootstrap CI, ScenarioFactory
with behavior-driven scenarios, auto-compile, label query interfaces.
Policy registry (registry.py): CANDIDATE → ACTIVE lifecycle.
Training pipeline (pipeline.py): bounded continuous learning loop + logger.
Strategy selector (selector.py): regime → strategy mapping, performance matrix.
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2026-07-11 10:33:56 +02:00 |
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Codex
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863a4cc8c9
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malkhut(T4): Strategy DSL v2 + generator + supporting modules
Strategy DSL v2 (dsl.py): 40+ action primitives, 40+ market sensors,
12 comparison operators, 16 builtins, full parser.
Strategy Generator (generator.py): genetic programming evolution —
crossover, mutation, tournament selection, pool management.
Supporting: discrepancy tracking, execution quality, hooks, feature
importance, observability, parallel eval, auto-rollback, stress testing,
structured observations, trajectory recording.
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2026-07-11 10:28:38 +02:00 |
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Codex
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981b469d51
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malkhut: asset classification, behavior DSL, auto-compiler, behavior-driven scenarios
Cambrian Explosion Phase 0 complete:
- Multi-label invariant asset taxonomy (13 assets, 10 sectors, 6 roles)
- Asset Behavior DSL: 10 orthogonal dimensions per asset, 3 composable
templates, research-validated from live Binance/BingX API data
- Asset Compiler: auto-fetch from Binance public API, compute profiles,
rate-limited (1 req/s), cached, 28 known classifications
- ScenarioFactory: all 30 scenario types use behavior-driven prices
(BTC=$64K, ETH=$1.8K, SOL=$80, DOGE=$0.07) instead of hardcoded
BTC prices. Auto-compiles unknown assets on demand.
- Label query interfaces: build_suite_for_sector/role/template/vol/labels
- 190 exhaustive tests for asset classification (up from 66)
- 1140 tests all green, CWM throughput 189K calls/s (121% of baseline)
- Comprehensive README: 1043 lines with full documentation
Research sources: Binance live REST API, BingX open API, CoinGlass,
academic literature (Bouchaud, Cont/Stoikov, Cartea/Jaimungal)
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2026-07-11 06:37:17 +02:00 |
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