5c8dc537f0
All search space bounds now derived from HardwareBudget methods: - batch: budget.max_batch_size() - hidden_dim: budget.max_hidden_dim_base_full() - dueling/branch: proportional to max_hidden (50%/25%) - buffer: 15% VRAM / 120 bytes per entry - atoms: 5% VRAM / per-atom tensor cost - accum: proportional to VRAM / 10GB Removed small_gpu/large_gpu boolean tiers entirely. A 24GB GPU now gets bounds between 4GB and 80GB values, not arbitrarily bucketed. Phase Fast fixed bounds (lo==hi) still skipped — never modified. 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;