b2eb1b7741
Four improvements to the training pipeline: 1. Asymmetric spread gradient: challenger action (adjacent to taken) gets pushed UP at half strength instead of DOWN. Creates a two-horse race instead of single-action monopoly. 2. Per-branch spread scaling: spread_grad *= branch_scale. Direction branch (high impact) gets more spread than urgency (low impact). 3. Distributional variance position sizing (Layer 4): Var[Q] = E[Z²] - E[Z]² computed in compute_expected_q. Position scaled by 1/(1+sqrt(Var[Q_taken])). High uncertainty → smaller position. Kelly criterion from C51 atoms. 4. Reward std guard: skip rank normalization when observed_reward_std ≈ 0 (epoch 0). Prevents zeroing all rewards before std is observed. 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;