HB Capital employs autonomous machine learning research using an autoresearch framework inspired by Andrej Karpathy's approach. AI agents autonomously iterate on reinforcement learning models for cryptocurrency trading.
The system runs continuous experiments exploring model architectures, hyperparameters, and training strategies. Each experiment is evaluated on out-of-sample data across multiple market regimes to ensure robustness.
Key principles: fixed evaluation budget for fair comparison, single metric optimization (out-of-sample performance), autonomous iteration without human intervention, and continuous frontier tracking.
Based on the autoresearch framework by Andrej Karpathy. Learn more at github.com/karpathy/autoresearch