Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
This addresses the limitations of traditional alpha mining methods for quantitative investors, though it appears incremental as it builds on existing AI and prompt engineering techniques.
The paper tackles the problem of mining new alphas (trading signals) in quantitative investment by introducing a human-AI interactive paradigm using large language models, resulting in a system that outputs creative and effective alphas as demonstrated in experiments.
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.