1634dec042
Two fixes: 1. Width: half = max(10*q_gap, 3*q_std, 0.1) instead of fixed 3*q_std+1.0. With q_gap=0.06 and 51 atoms, gives ~5 atoms of action resolution (was 1.5 atoms with the 1.0 floor). No more catastrophic -40 dips from atom underresolution. 2. Baseline: uses q_std_ema (proportional to natural Q oscillation) instead of fixed 0.01. Normal Q-oscillation (±q_std) no longer triggers aggressive alpha — only abnormal shifts do. 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;