e910f6dfa8
The metrics CUDA kernel already iterated over actions_history for trade counting but never counted buy/sell/hold distribution. evaluate_gpu() hardcoded all three to 0.0, making calculate_hft_activity_score_wave10() return a constant -5.0 for every trial — PSO searched blind for 25% of the objective space. Kernel: 3 new shared-memory reduction arrays (s_buys/s_sells/s_holds), action counting in existing per-step loop (0,1→sell, 2→hold, 3,4→buy), output expanded from 10→13 floats/window. Zero extra kernel launches, zero extra GPU→CPU transfers (piggybacks on existing memcpy_dtoh). Shared memory: 25 KB (was 22 KB) — well within H100 228 KB/SM limit. Co-Authored-By: Claude Opus 4.6 <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;