Compares the 51-feature TLOBFeatureExtractor (crates/ml-supervised/src/tlob/
features.rs + mbp10_feature_extractor.rs) against Foxhunt's 20-slot OFI
vector persisted in .fxcache.
Findings: of TLOB's 51 slots, 23 are placeholders (sine waves / hardcoded
constants), 12 duplicate existing OFI or 42-dim market features, and only
~10 carry genuinely novel information. Meanwhile the existing
MicrostructureState struct in ml-features already computes 10 features
(realized variance, Hawkes intensity, weighted book pressure, spread
dynamics, aggression ratio, queue-depletion asymmetry, order-count flux,
intra-bar momentum, regime score, OFI trajectory) that are dropped before
reaching fxcache — these dominate the TLOB novel candidates and require
zero new math.
Phase B plan (drafted inline): bump OFI_DIM 20→28, bump FXCACHE_VERSION
4→5, prioritize persisting the 10 Foxhunt-internal MicrostructureState
features before any TLOB-derived additions, sweep all hard-coded 18/20
constants across CUDA kernels + gpu_dqn_trainer.rs, and rely on the
ensure-fxcache Argo step for cache regeneration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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