787ee7b86c
After Path C confirmed aux head's 28% accuracy at H=60 is the H6 Phase 3 bottleneck (not the mechanism itself), enlarge aux capacity: - AUX_HIDDEN_DIM: 32 -> 256 (8x, matches input dim, removes bottleneck) - AUX_TRUNK_H2: 128 -> 256 (2x, uniform trunk width) Architecture changes: - Aux head: 256 -> Linear -> 256 -> ELU -> Linear -> 2 (was 256->32->2) - Aux trunk: 256 -> 256 -> 256 -> 256 (was 256 -> 256 -> 128 -> 256) - +160K params total (mostly aux_nb_w1 + aux_rg_w1: [256, 256] each) Side effects: - Checkpoint fingerprint change (intentional) - Thread utilisation improves: AUX_BLOCK=256 threads x H=256 = 1:1 (vs 8:1 at H=32 — most threads idle previously) Phase 3 mechanism stays DORMANT (W=0, beta=0) for this validation smoke. Verdict criteria: aux_dir_acc improves from 0.28 toward 0.50+ with the larger capacity. If yes, re-activate Phase 3 priors. If no, the bottleneck is signal/horizon, not capacity. Cargo build clean (full nvcc rebuild). 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;