caa01070c8
Atom warm-start: bitonic sort rewards → quantile positions → write to atom_positions_buf as initialization. Existing SGD optimizer refines. Atoms start where reward mass actually is instead of uniform [-50,+50]. Robust PopArt: median/IQR normalization from sorted rewards replaces Welford mean/var. More robust for bimodal distribution (many ±0.1 micro-rewards + few ±5.0 trade exits). Conditional: popart_robust=true. Both reuse the same bitonic sort (~14ms per epoch, amortized). gather_quantiles kernel extracts positions. extract_median_iqr reads Q25/median/Q75 from sorted buffer. 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;