657d7b49ec
Two optimizations: 1. Dual CUDA graph: graph_forward_mse skips C51 loss/grad/SAXPY blend during MSE warmup (c51_alpha=0). Eliminates 7 kernel invocations from the graph replay. replay_forward() routes by alpha automatically. 2. NaN-safe dqn_scale_f32_kernel: writes 0.0 directly when alpha==0 instead of multiplying (IEEE 754: 0*NaN=NaN). Defense-in-depth for the C51 gradient buffer which can contain NaN from random logits. Fold 3 epoch: 5.08s → 4.92s (under 5s target). 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;