aa272666a1
Replace raw PnL reward in experience_env_step with GPU-native composite: - DSR (Moody & Saffell 2001) with pre-update A/B formulation - Z-scored normalized PnL with running EMA - Drawdown penalty with peak_equity guard - Idle penalty (replaces hold_reward) - Regime-adaptive scaling from ADX/CUSUM features - Asymmetric loss scaling (prospect theory) - Position-time decay (stale position rent) - Transaction cost (unchanged) PORTFOLIO_STRIDE=12 (was 3). portfolio_sim_kernel stride-8 unchanged. All division-by-zero guards per spec. ~25 FLOPs overhead (<5%). 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;