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

Hang Yuan, Saizhuo Wang, Jian Guo
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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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Alpha-GPT 2.0:用于量化投资的环形人工智能
最近,我们在量化投资领域引入了一种新的阿尔法挖掘范式,开发了一种新的交互式阿尔法挖掘系统框架--阿尔法-GPT。该系统的核心是基于大型语言模型的迭代式人机交互,为阿尔法挖掘引入了一种 "人在回路中 "的方法。在本文中,我们将介绍下一代 Alpha-GPT 2.0(脚注{草稿。这是一个量化投资框架,进一步涵盖了量化投资中至关重要的建模和分析阶段。该框架强调人类与人工智能之间的迭代、互动研究,在整个量化投资流程中体现了 "人在回路中"(Human-in-the-Loop)的策略。通过将人类研究人员的见解吸收到系统化阿尔法研究过程中,我们有效地利用了 "人在回路中 "方法,提高了量化投资研究的效率和精确度。
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