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arXiv 2402.09746q-fin.CPcs.AI
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Alpha-GPT 2.0:面向量化投资的人在回路 AI

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

The Hong Kong University of Science and Technology(香港科技大学);IDEA Research(IDEA研究院)

Hang Yuan, Saizhuo Wang, Jian Guo

AI总结:

本文提出 Alpha-GPT 2.0 量化投资框架,通过大型语言模型驱动的迭代人机交互与人在回路策略,将人类洞察融入系统化 alpha 研究,覆盖建模与分析阶段,有效提升了量化投资研究的效率与精度。

摘要:

最近,我们在量化投资领域的 alpha 挖掘中引入了一种新范式,开发了一个新的交互式 alpha 挖掘系统框架 Alpha-GPT。该系统以基于大型语言模型的迭代人机交互为中心,引入了人在回路方法用于 alpha 发现。在本文中,我们提出了下一代 Alpha-GPT 2.0,这是一个进一步涵盖量化投资中关键建模和分析阶段的量化投资框架。该框架强调人类与 AI 之间的迭代、交互式研究,在整个量化投资流程中体现了人在回路策略。通过将人类研究人员的洞察力同化到系统的 alpha 研究过程中,我们有效地利用了人在回路方法,提高了量化投资研究的效率和精度。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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