sentiment-engine

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Codex 97a770da65 malkhut: online EWMA self-calibrating slippage model

Flight7 model underestimates by 80% in CWM dynamic book:
  raw predicted: 0.034 bps, actual: 0.180 bps
  Constant error across 22K episodes — no feedback loop.

Root cause: Flight7 calibrated on real BingX taker fills, but CWM's
synthetic dynamic book has different fill characteristics.

Fix: SlippageSelfCalibrator with EWMA feedback loop.
  After each fill: error = actual - predicted (clipped to +/-20 bps)
  EWMA smooths per-symbol errors (alpha=0.2)
  Next prediction = raw_model + EWMA_correction
  Bounded output: 0-50 bps absolute

Convergence (300 eps across 8 assets):
  ETH: 9% error (from 80%)
  SOL: 3.5%
  DOGE: 6.7%
  LINK: 5.7%
  ADA: 9.7%
  BTC: 48.6% (low fill count, converging)
  AVAX: 28.6% (low fill count)
  UNI: 52.5% (low fill count, early outlier)

Truthfulness guarantees:
  - Correction is observable (CALIBRATOR.correction(symbol))
  - Resets between runs (no hidden state)
  - Only uses observed fills, no assumptions
  - Error clipping prevents outlier domination
  - Absolute bounds prevent runaway

2026-07-20 15:17:16 +02:00

__init__.py

malkhut(T2): Code World Model — deterministic exchange simulator

2026-07-11 10:23:44 +02:00

adverse_selection.py

malkhut(T2): Code World Model — deterministic exchange simulator

2026-07-11 10:23:44 +02:00

core.py

malkhut: online EWMA self-calibrating slippage model

2026-07-20 15:17:16 +02:00

correlation.py

malkhut(T2): Code World Model — deterministic exchange simulator