298455e24a
CRITICAL: backward_full used state_dim for goff_b_s1 offset but compute_param_sizes uses s1_input_dim (smaller with bottleneck active). All downstream gradient offsets were misaligned. Fixed: sd → s1d. HIGH: q_attn_params used alloc_f32 (potentially non-zero) instead of alloc_zeros. Cross-branch Q-attention residual connection needs near-zero init for stability. Fixed: alloc_zeros. MEDIUM (deferred): VSN masking bypassed for magnitude branch — mag_concat reads raw h_s2 instead of vsn_masked. No impact while W_vsn2 is zero-init (identity mask). Will fix when VSN backward is wired. Co-Authored-By: Claude Opus 4.6 (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;