{"title":"Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment","authors":"Hang Yuan, Saizhuo Wang, Jian Guo","doi":"arxiv-2402.09746","DOIUrl":null,"url":null,"abstract":"Recently, we introduced a new paradigm for alpha mining in the realm of\nquantitative investment, developing a new interactive alpha mining system\nframework, Alpha-GPT. This system is centered on iterative Human-AI interaction\nbased on large language models, introducing a Human-in-the-Loop approach to\nalpha discovery. In this paper, we present the next-generation Alpha-GPT 2.0\n\\footnote{Draft. Work in progress}, a quantitative investment framework that\nfurther encompasses crucial modeling and analysis phases in quantitative\ninvestment. This framework emphasizes the iterative, interactive research\nbetween humans and AI, embodying a Human-in-the-Loop strategy throughout the\nentire quantitative investment pipeline. By assimilating the insights of human\nresearchers into the systematic alpha research process, we effectively leverage\nthe Human-in-the-Loop approach, enhancing the efficiency and precision of\nquantitative investment research.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"80 1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Computational Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.09746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.