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Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment

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  • Hang Yuan
  • Saizhuo Wang
  • Jian Guo

Abstract

Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT. This system is centered on iterative Human-AI interaction based on large language models, introducing a Human-in-the-Loop approach to alpha discovery. In this paper, we present the next-generation Alpha-GPT 2.0 \footnote{Draft. Work in progress}, a quantitative investment framework that further encompasses crucial modeling and analysis phases in quantitative investment. This framework emphasizes the iterative, interactive research between humans and AI, embodying a Human-in-the-Loop strategy throughout the entire quantitative investment pipeline. By assimilating the insights of human researchers into the systematic alpha research process, we effectively leverage the Human-in-the-Loop approach, enhancing the efficiency and precision of quantitative investment research.

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  • Hang Yuan & Saizhuo Wang & Jian Guo, 2024. "Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment," Papers 2402.09746, arXiv.org.
    • Handle: RePEc:arx:papers:2402.09746
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    References listed on IDEAS

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    1. Tianping Zhang & Yuanqi Li & Yifei Jin & Jian Li, 2020. "AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment," Papers 2002.08245, arXiv.org, revised Apr 2020.
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