05aa19ffe9
Removed #[ignore] from tests that have local infrastructure: - 3 data_loader tests: auto-detect test_data/real/databento/ via workspace - 3 memory_profiler tests: nvidia-smi at /usr/bin/nvidia-smi - 4 benchmark tests (TFT, Mamba2, DQN, PPO): GPU + DBN data available - 1 inference test: model loading (slow but should run) - 3 DQN performance smoke tests: GPU available PPO benchmark: fixed data_path to test_data/real/databento/6E.FUT Sequential: added vars_mut() accessor 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;