d485dde5a2
Major changes: - Eliminate ALL runtime NVRTC: c51_loss + mse_loss kernels converted to precompiled cubins with runtime num_atoms/v_min/v_max/branch params - Fix evaluator SIGSEGV: rng_states/q_gaps sized for chunked batch (cn) not n_windows; NULL pointer guard in action_select kernel - Add trade_physics.cuh shared header for train/eval consistency - Add equity circuit breaker (25% DD from peak) + margin-aware position cap - Consolidate 46D→22D hyperopt search space, enable ensemble by default - Fix trade counting: use exposure index not factored action - Fix Calmar overflow: clamp to ±100 in kernel - Softer CVaR penalty (cap 3.0 not 10.0) for undertrained models - Fix win_rate display (ratio→percentage) - Remove dead code (normalize_reward, calculate_completion_penalty) - Add tracing subscriber to hyperopt test for visible metrics - Per-chunk sync in evaluator for reliable error reporting 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;