3bd339b5a3
Root cause: backtest windows of 300K bars produced ±billions% returns via multiplicative compounding, and sqrt(252) annualization was wrong for 1-minute bars. Fixes: - Window size capped to 10K bars (~25 trading days), evenly distributed across the full validation set (was clustered in first 6%) - Annualization: configurable bars_per_day field in GpuBacktestConfig (default 390.0 for 1-min), produces sqrt(98280) ≈ 313.5 - tanh normalization recalibrated: Sharpe/5, Sortino/8 (was /2, /3) - CVaR threshold scaled to per-bar: 0.003 with slope 1400 (was 0.05/200) - VaR/CVaR strided sampling covers full window (was first 4096 only) - financials.rs + ab_testing.rs: sqrt(252) → sqrt(98280) for consistency 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;