Adaptive LLM-based multi-agent systems to enhance quantitative trading performance

Traditional quantitative trading approaches have limited capacity for synthesizing heterogeneous data sources and adapting to market regime shifts. We propose a three-layer framework that systematically integrates large language models for semantic processing, multi-agent systems for collaborative i...

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Bibliografiske detaljer

Principais autores:	Edward Yu-Cheng Cheng, Cheng-Jui Tseng, Hsueh-Ting Chu
Format:	Artigo
Sprog:	Inglês
Udgivet:	PeerJ Inc. 2026-03-01
Serier:	PeerJ Computer Science
Fag:	Multi-agent systems Large language models Quantitative trading Model context protocol Deep reinforcement learning Proximal policy optimization
Online adgang:	https://peerj.com/articles/cs-3630.pdf
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