Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment
This work addresses quantitative investment for financial researchers, but it appears incremental as an extension of a previous system.
The paper tackles the problem of alpha mining in quantitative investment by introducing Alpha-GPT 2.0, a framework that integrates human-in-the-loop AI to enhance efficiency and precision, though no concrete numbers are provided.
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.