3d04c24eaf
unique_actions was hardcoded to 5 in the GPU backtest path, making the diversity penalty (0.8 max weight) always return 0.0 — another dead objective component. CPU backtest path correctly computed it via HashSet. Added 5-bit bitmask OR-reduction: each thread sets bit(action_id) during the existing per-step loop, then a single OR-reduction across the block produces the union. __popc() gives unique count in one PTX instruction. Memory efficiency: reuses s_sorted shared memory (sequential staging — OR-reduction completes before bitonic sort overwrites it). No extra shared memory arrays needed. Output expanded from 13→14 floats/window. Combined with the previous commit, this restores gradient signal for 100% of the multi-objective function: composite (60%), HFT activity (25%), stability (15%), and diversity penalty (soft signal). 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;