ebc7144434
Smoke train-8zwtf @ 465abc7e9 validated that the AUX_HIDDEN_DIM 32→128
uplift fixed the aux head:
- aux_dir_acc: 0.28 (anti-predictive) → 0.70 (correctly predicting
majority-down direction at H=60 bars)
- pred_tanh: +0.66 (UP-biased, wrong) → -0.52 (DOWN-biased, correct)
- val WR: 0.4345 (baseline preserved, no destabilization)
With aux head now informative, re-activate Phase 3 priors:
- aux_w_prior_init: W = [-0.5, 0, +0.5, 0]
- state_reset_registry dispatch: scale_beta = 0.5
When state_121 < 0 (aux predicts down, typical):
- atom_shift[Short] = -0.5 × neg = POSITIVE → encourages Short ✓
- atom_shift[Long] = +0.5 × neg = NEGATIVE → discourages Long ✓
Next smoke is decisive H6 Phase 3 test: does mechanism move WR above
0.4345 baseline with the now-informative aux head?
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;