arXiv Open Access 2023

Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment

Saizhuo Wang Hang Yuan Leon Zhou Lionel M. Ni Heung-Yeung Shum +1 lainnya
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Abstrak

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.

Topik & Kata Kunci

Penulis (6)

S

Saizhuo Wang

H

Hang Yuan

L

Leon Zhou

L

Lionel M. Ni

H

Heung-Yeung Shum

J

Jian Guo

Format Sitasi

Wang, S., Yuan, H., Zhou, L., Ni, L.M., Shum, H., Guo, J. (2023). Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment. https://arxiv.org/abs/2308.00016

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Tahun Terbit
2023
Bahasa
en
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arXiv
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Open Access ✓