One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-crafted factor synthesizing or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quants. In this work, we propose a new alpha mining paradigm by introducing human-AI interaction, and a novel prompt engineering algorithmic framework to implement this paradigm by leveraging the power of large language models. Moreover, we develop Alpha-GPT, a new interactive alpha mining system framework that provides a heuristic way to ``understand'' the ideas of quant researchers and outputs creative, insightful, and effective alphas. We demonstrate the effectiveness and advantage of Alpha-GPT via a number of alpha mining experiments.
@article{arxiv.2308.00016,
title = {Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment},
author = {Saizhuo Wang and Hang Yuan and Leon Zhou and Lionel M. Ni and Heung-Yeung Shum and Jian Guo},
journal= {arXiv preprint arXiv:2308.00016},
year = {2025}
}