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https://doi.org/10.62836/iaet.v3i1.362
Kaixian Xu, Zhaoyan Zhang, Alan Wilson, Yu Qiao, Lisang Zhou, & Ying Jiang. (2024). Generalizable Multi-Agent Framework for Quantitative Trading of US Education Funds. Innovations in Applied Engineering and Technology, 3(1), 1–12. https://doi.org/10.62836/iaet.v3i1.362
Volume 3, Issue 1, 2024

Generalizable Multi-Agent Framework for Quantitative Trading of US Education Funds

Quantitative trading of specialized financial instruments like US education funds  requires comprehensive analysis of both market dynamics and external influenc- ing factors.  This paper proposes a novel multi-agent framework that integrates  collaborative agents for market analysis, macroeconomic trend assessment, and  policy change evaluation, along with a multi-level reflection mechanism for contin- uous strategy optimization. Through extensive experiments using a comprehensive  dataset from 2018 to 2024, the framework demonstrates superior performance  compared to traditional rule-based strategies and machine learning approaches, achieving higher returns, better risk-adjusted performance, and enhanced risk man- agement capabilities.  The integration of multi-agent collaboration, non-market  factor analysis, and adaptive strategy refinement provides a robust solution for  achieving long-term investment goals in dynamic market environments.

multi-agent framework; quantitative trading; non-market factor analysis; multi-level reflection mechanism; US education funds

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Supporting Agencies

  1. Funding: Funding This research was supported by the U.S. National Science Foundation under Grant No. 2339596.