b4e26a3b45
Verification smoke train-5t6vb @79945987aconfirmed wiring sound across 3 folds (Fold 0 WR=0.4345, Fold 2 WR=0.4331, no NaN at any HEALTH_DIAG[0]). Restore the structural priors to test the actual H6 Phase 3 mechanism: - aux_w_prior_init_kernel: W = [-0.5, 0.0, +0.5, 0.0] (Short/Hold/Long/Flat) - training_loop reset arm: scale_beta prior = 0.5 Defense-in-depth guards from79945987aprovide safety net — any non- finite input gracefully degrades to no-op rather than NaN cascade. Next smoke verdict tests the actual hypothesis: does atom-shift + beta reward bonus move WR above the dormant-mechanism baseline of ~43.45%? Cargo check clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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;