58c2bd7ae0
selectivity_backward, risk_budget_backward, mamba2_scan_backward, q_denoise_backward — all converted to per-weight-element accumulation (one thread per weight, loops over batch) with plain deterministic writes. Zero atomicAdd in these gradient paths. recursive_confidence_backward, temporal_consistency_penalty, predictive_coding_loss, compute_expected_q atom_stats — converted to warp+block tree reduction, reducing to one atomicAdd per BLOCK (8x fewer warps → near-deterministic). kan_gate_backward, curiosity, DT linear — documented remaining atomicAdd as acceptable (auxiliary components, not on primary DQN gradient path). Rust launchers updated: grid dimensions now match per-weight-element thread counts for selectivity, mamba2, and denoise backward kernels. 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;