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

ArXiv ID: 2402.09746 “View on arXiv”

Authors: Unknown

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.

Keywords: Alpha mining, Human-in-the-Loop, Large Language Models (LLMs), Quantitative investment, Interactive AI, Multi-Asset / General Equities

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Philosophers
  • Why: The paper focuses on a framework and system architecture for Human-AI interaction using LLMs, which involves more conceptual and workflow design than advanced mathematical modeling. While it mentions backtesting and benchmarking, the excerpt lacks specific statistical metrics, code, or implementation details, suggesting it is more conceptual than data-heavy.
  flowchart TD
    A["Research Goal:<br>Enhance Alpha Mining &<br>Quantitative Pipeline"] --> B["Methodology:<br>Alpha-GPT 2.0 Framework<br>Human-in-the-Loop AI"]
    
    B --> C["Data Inputs:<br>Market Data &<br>Financial Reports"]
    
    C --> D["Computational Process:<br>Iterative Human-AI Interaction<br>LLM Reasoning & Backtesting"]
    
    D --> E["Key Outcomes:<br>Improved Alpha Discovery<br>Efficient Modeling & Analysis<br>Enhanced Research Precision"]