df55c60945
Replace pure time-sequential expanding-window splits with stratified splits that adjust fold boundaries to balance Trending/Ranging/Volatile proportions. Slides boundaries up to 25% of validation window when deviation exceeds 10pp from global average. Strictly temporal — no data shuffling, only boundary adjustments. This directly addresses the R²=1.0 finding: IS→OOS Sharpe gap was entirely regime-driven because folds had wildly different regime mixes. Stratification ensures each fold sees similar market conditions. 7 unit tests for regime classification, distribution, deviation, and fold generation with both uniform and imbalanced data. 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;