ba97a6e819
Sharpe/Sortino were annualized with √252 (daily trading assumption). For intraday strategies with hundreds of trades per eval window, this inflated magnitudes ~10x, causing Sharpe=-1.4 while Omega=10.9 on the same return series — mathematically contradictory. Fix: scale by √N where N = actual number of returns in the series. This gives the Sharpe of the evaluation window, not a synthetic annual. - evaluation/metrics.rs: Sharpe, Sortino, Calmar all fixed - trainer/metrics.rs: val_loss Sharpe (compute_validation_loss) - ppo.rs: epoch Sharpe proxy 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;