201b59dfbc
A0 follow-up (1e5a65912) fixed two stale refs from code-quality review.
Implementer surfaced 3 more accumulated across SP4/SP5/Layer-D:
- :537 sp4_wiener_state — claimed 543 floats with growth chain 141→207→213→543
- :974 sp5_pnl_aggregation — claimed SP5_WIENER_TOTAL_FLOATS=573 (post-D2 stale)
- :989 sp5_health_composition — same =573 stale
- :1009 sp5_training_metrics_ema — claimed =582 (post-D3 stale)
All four converted to formula form (71 + SP5_PRODUCER_COUNT) × 3 matching
A0 fix-up pattern. Drops brittle growth chains in favor of a derivable
formula. Audit doc entry added per Invariant 7. cargo check + state_reset
_registry tests 4/4 pass.
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;