mlsanityData to 13 Oct 2026

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

arXiv 2402.09746 ↗First posted 15 Feb 2024q-fin.CP cs.AIcited by 11

Not on the map. Why?

Abstract (arXiv)

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 footnoteDraft. 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.

Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 15 Feb 2024.

Builds on

3 of 7 references.

References · 3

  1. 2019AutoML: A Survey of the State-of-the-Artcited by 389
  2. 2023Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investmentcited by 55
  3. 2023Generating Synergistic Formulaic Alpha Collections via Reinforcement Learningcited by 38

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 3.

11 papers.

  1. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 89
  2. 2025Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crewscited by 12
  3. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  4. 2025ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanismcited by 8
  5. 2025QuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Frameworkcited by 6
  6. 2025Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learningcited by 5
  7. 2025Integrating Large Language Models in Financial Investments and Market Analysis: A Surveycited by 4
  8. 2025Market-Dependent Communication in Multi-Agent Alpha Generationcited by 0
  9. 2026Talking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social Mediacited by 0
  10. 2026SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemescited by 0
  11. 2026AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learningcited by 0