English

An End-To-End LLM Enhanced Trading System

Trading and Market Microstructure 2025-02-04 v1

Abstract

This project introduces an end-to-end trading system that leverages Large Language Models (LLMs) for real-time market sentiment analysis. By synthesizing data from financial news and social media, the system integrates sentiment-driven insights with technical indicators to generate actionable trading signals. FinGPT serves as the primary model for sentiment analysis, ensuring domain-specific accuracy, while Kubernetes is used for scalable and efficient deployment.

Keywords

Cite

@article{arxiv.2502.01574,
  title  = {An End-To-End LLM Enhanced Trading System},
  author = {Ziyao Zhou and Ronitt Mehra},
  journal= {arXiv preprint arXiv:2502.01574},
  year   = {2025}
}

Comments

6 pages, 1 figure

R2 v1 2026-06-28T21:30:56.475Z