An End-To-End LLM Enhanced Trading System

Ziyao Zhou; Ronitt Mehra

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arXiv · 2502.01574

An End-To-End LLM Enhanced Trading System

Ziyao Zhou·Ronitt Mehra

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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.

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Ziyao Zhou, Ronitt Mehra. 2025-02-03. An End-To-End LLM Enhanced Trading System. https://arxiv.org/abs/2502.01574

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