08eaef4a4f
The GpuBacktestConfig hardcoded max_position=1.0 with a 2× leverage cap, reducing effective position to 0.2026 contracts on ES at $35K capital. Training uses max_position_absolute from PSO params (1.0-4.0 contracts) with no leverage cap — a 5.4× mismatch that makes transaction costs overwhelm any alpha in walk-forward evaluation (0% win rate, -688% return). Fix: Pass max_position_absolute through evaluate_gpu() and disable leverage cap (max_leverage=0) to match training conditions. Same fix applied to evaluate_baseline.rs via --max-position CLI arg. 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;