An End-To-End LLM Enhanced Trading System

2025/02/03 by Zhou, Ziyao, Ronitt Mehra, Mehra, Ronitt · 2 citations
Business, Management and Accounting · Economics, Econometrics and Finance · #Securities Regulation and Market Practices #Credit Risk and Financial Regulations

paper · pdf · doi:10.48550/arxiv.2502.01574

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.

Cited by

• BondBERT: What we learn when assigning sentiment in the bond market
• Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading

Related

• FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
• Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500
• FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications
• What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis
• MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents