7e8150bdf6
Single GPU-resident scalar: CV = sigma_adv / |mean_adv|. When CV > 1 (noisy advantages), readiness < 1 → features suppressed. When CV ≤ 1 (stable advantages), readiness = 1 → features fully active. All 5 IQL downstream kernels blend toward neutral defaults at readiness=0: - Per-sample C51 support: blends to [-1, 1] default - PER modulation: blends to td_errors unmodified - Branch scales: blends to uniform 0.25 - Advantage weights: blends to neutral 1.0 - Expectile gap epsilon: gap scaled by readiness Zero new hyperparameters. Computed from existing adv_stats_buf. Best Sharpe improved 11.39 → 14.23 on 10-epoch stability test. 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;