7c34003f03
Performance: - Cache cuBLAS descriptors: pre-create matmul_desc + layouts + algo at init. requestedAlgoCount=3 for better algorithm selection. Zero per-GEMM overhead. - Per-branch workspace: 4 × 32MB separate workspace buffers for multi-stream branch dispatch. Eliminates workspace contention on parallel execution. Training stability: - Remove hardcoded shrink-perturb that fired every epoch on short runs (3-5 epochs). With epochs=5, interval = epochs/4 = 1 → fired every epoch, destroying epoch 1 learned weights. This caused Sharpe to collapse from +0.60 to -0.29 after epoch 1. - Phase 3 shrink-perturb now uses config values (was hardcoded alpha=0.9, sigma=0.01). - The config-defined shrink_perturb_interval=20 now controls all shrink-perturb timing. 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;