An End-To-End LLM Enhanced Trading System

TL;DR

This paper presents an end-to-end trading system that uses Large Language Models for real-time sentiment analysis, integrating news and social media data with technical indicators to improve trading decisions.

Contribution

It introduces a novel trading system that combines LLM-based sentiment analysis with technical indicators, utilizing FinGPT and Kubernetes for scalable deployment.

Findings

Effective real-time sentiment analysis with FinGPT

Improved trading signal accuracy through integrated data sources

Scalable deployment using Kubernetes

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.