Files
foxhunt/crates/ml
jgrusewski 0c539e6156 feat(v8): wire 5 dead CUDA kernels — PopArt, pretrain, TD(λ), difficulty, hindsight
All 5 kernels now have Rust load + launch methods:
- popart_normalize_kernel: running mean/var + in-place normalization
- exposure_pretrain_step: supervised direction pre-training with backward GEMM
- td_lambda_kernel: loaded from nstep cubin (launch via config flag)
- compute_difficulty_scores: per-bar curriculum difficulty
- hindsight_relabel_kernel: optimal exit relabeling

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 01:23:28 +02:00
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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 aggregation
  • hyperopt — PSO-based hyperparameter optimization with per-model adapters
  • trainers — unified training loops (DQN, PPO, supervised)
  • inferenceInferenceAdapter trait for prediction
  • checkpoint — model checkpointing and restoration
  • evaluation — walk-forward evaluation pipeline

Usage

use ml::dqn::DQN;
use ml::ppo::PpoTrainer;