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    <abstract>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 synthesis or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quant researchers. 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. In particular, we evaluated Alpha-GPT’s performance in the WorldQuant International Quant Championship, where it demonstrated results comparable to those of top-performing human participants, ranking among top-10 over 41000 teams worldwide. These findings suggest Alpha-GPT’s significant potential in generating highly effective alphas that may surpass human capabilities in quantitative investment strategies.</abstract>
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%0 Conference Proceedings
%T Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
%A Wang, Saizhuo
%A Yuan, Hang
%A Zhou, Leon
%A Ni, Lionel
%A Shum, Heung-Yeung
%A Guo, Jian
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F wang-etal-2025-alpha
%X 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 synthesis or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quant researchers. 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. In particular, we evaluated Alpha-GPT’s performance in the WorldQuant International Quant Championship, where it demonstrated results comparable to those of top-performing human participants, ranking among top-10 over 41000 teams worldwide. These findings suggest Alpha-GPT’s significant potential in generating highly effective alphas that may surpass human capabilities in quantitative investment strategies.
%U https://aclanthology.org/2025.emnlp-demos.14/
%P 196-206

Markdown (Informal)

[Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment](https://aclanthology.org/2025.emnlp-demos.14/) (Wang et al., EMNLP 2025)

• Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment (Wang et al., EMNLP 2025)

ACL

• Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel Ni, Heung-Yeung Shum, and Jian Guo. 2025. Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 196–206, Suzhou, China. Association for Computational Linguistics.