EvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research

Automated alpha factor discovery is critical for modern quantitative research. While large language models (LLMs) have increasingly been integrated into quantitative workflows, existing agent-based approaches remain highly susceptible to severe backtest overfitting. To address this challenge, we propose **EvoQuantLab**, a training-free, dual-loop self-evolving framework for robust factor discovery. Instead of relying entirely on LLM generation, **EvoQuantLab** deploys an inner genetic-programming (GP) loop that dramatically improves token efficiency while evolving typed factor expressions under structural and diversity constraints. An outer diagnosis loop then systematically audits evidence across cycles to drive protocol-level improvements. Furthermore, our evaluation pipeline applies multi-metric validation to rigorously filter out spurious factors before deployment. Extensive out-of-sample experiments across both daily and hourly frequencies—covering equities and cryptocurrency—demonstrate the robustness of **EvoQuantLab**. Specifically, on daily CSI 500 and S&P 500 tasks, **EvoQuantLab** achieves leading annualized returns of 18.34% and 21.30%, consistently outperforming existing agent baselines. On high-turnover hourly cryptocurrency data, **EvoQuantLab** delivers a 37.91% annualized return while effectively avoiding execution collapse.