Codex
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4aeadf1aae
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docs: hftbacktest CWM integration design — drop-in LOB backend
Architecture:
hftbacktest replaces _fill_from_levels() + manual book updates
Everything above transition() stays the same
What changes:
- HftBacktestCWM: new class implementing CodeWorldModel protocol
- transition(): submit/cancel via hftbacktest, convert state back
- fill model: ProbQueueModel (queue-position-aware)
- latency: interpolated from historical data
What does NOT change:
- Planner (SM-MCTS, EXP3, Thompson, etc.)
- Counterparty ecology (ToxicTaker, PassiveMaker, etc.)
- Risk gate (all 6 checks)
- CMA-ES trainer
- PerformanceMatrix
- Reward function (same PnL + adverse selection + risk)
- Action menu, FulfilmentAction, ScenarioFactory
- ALL existing tests
Integration: 3 steps, zero core changes:
1. Create malkhut/cwm/hft_cwm.py
2. Swap cwm_factory in PolicyEvaluator
3. Done
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2026-07-14 18:37:41 +02:00 |
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Codex
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943b3ef985
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docs: order book microstructure study — 12 sections, 13 assets
Comprehensive OB study compiled from live Binance/BingX data + academic
literature (Bouchaud, Cont/Stoikov, Cartea/Jaimungal):
1. Depth power-law decay: D(d) = A * d^(1-alpha), per-asset params
2. Spread profiles: normal + stress multipliers for all 13 assets
3. Order flow: arrival rates, cancel/fill ratios, size distributions
4. Market maker behavior: inventory limits, pull speed, margins
5. Volatility regimes: GARCH params, half-lives, crisis multipliers
6. Intraday patterns: peak/trough hours, session analysis
7. Cross-asset correlations: normal vs crash behavior
8. BingX-specific: spread/depth/latency/fees vs Binance ratios
9. Book fragility & cascade dynamics: flash crash anatomy
10. Retail vs institutional composition
11. Funding rates: per-asset means, std, positive%
12. Expected slippage model
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2026-07-14 18:17:15 +02:00 |
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