Codex
bb229833d3
malkhut: Flight9 learnings — markout=quality, queue×flow, depth-for-size
...
Fable's Flight9/BLUE generalizable features incorporated:
1. Slippage model gains trade_flow_intensity parameter:
- Estimated from book imbalance (proxy for trade arrivals)
- More flow → better fills (lower slippage)
- Fable: 'fill = queue position × trade-flow intensity'
2. Markout = quality concept documented:
- Score fills by post-fill markout, not just fill/no-fill
- Maker fills are adversely selected
3. Depth-for-size documented:
- Spread lies; key on depth-within-K-bps vs order notional
4. Measured fees:
- BingX maker=2.00bp, taker=5.016bp (over 1,455 fills)
- BingX commission = NEGATIVE (debit)
5. OB study updated with Flight9 learnings
2026-07-17 16:18:43 +02:00
Codex
8857daedfa
malkhut: urgency-driven maker/taker + calibrated slippage + chase + docs
2026-07-17 10:16:55 +02:00
Codex
619966605e
malkhut(docs): fill quality documentation — README, integration, OB study
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README: Fill Quality section (core optimization target, metrics, reward
function, PerformanceMatrix, CMA-ES integration)
HftBacktestCWM integration doc: FillQuality dataclass, fill_value_score
computation, reward function weighting, PerformanceMatrix tracking
OB microstructure study: Section 13 — Fill Quality Optimization,
per-asset expectations, optimization strategy, connection to OB dynamics
Fill quality is MALKHUT's core aim: the system learns to get better fills
(faster, better-priced, less adverse selection) across regimes and venues.
2026-07-15 15:36:22 +02:00
Codex
4aeadf1aae
docs: hftbacktest CWM integration design — drop-in LOB backend
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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
2026-07-14 18:37:41 +02:00
Codex
943b3ef985
docs: order book microstructure study — 12 sections, 13 assets
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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
2026-07-14 18:17:15 +02:00
