0718dabd4c
16 new features computed directly in the state_gather CUDA kernel from market data already on GPU. No CPU feature engineering. Lookback windows: 5, 15, 60, 240 bars (≈5min, 15min, 1hr, 4hr) Features per window: - Return over N bars (directional bias) - Volatility (high-low range / close) - Volume trend (current / N-bar average) - Momentum (position within N-bar range [0=bottom, 1=top]) State dim: 66 raw (72 aligned) without OFI, 74 raw (80 aligned) with. The model now sees price action at 4 timescales simultaneously. 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;