f2938b19e8
- Replace Silverman's bandwidth (h = 1.06σn^(-1/5)) with Scott's rule (h = 0.7σn^(-1/(d+4))) for tighter kernels in high-D parameter spaces - Add best-trial injection: always evaluate EI at best known point plus 5 small perturbations (±5%), preventing optimizer from forgetting peaks - Scale n_candidates dynamically: max(256, 8*n_dims) instead of fixed 100 - Reduce gamma from 0.25 to 0.15 when trials < 50 for tighter exploitation - Wire model_name through PSO/TPE paths for per-trial Prometheus metrics Co-Authored-By: Claude Opus 4.6 <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;