Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
Saizhuo Wang Hang Yuan Leon Zhou Lionel M. Ni Heung-Yeung Shum Jian Guo
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.