An End-To-End LLM Enhanced Trading System

Ziyao Zhou and Ronitt Mehra

Papers from arXiv.org

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.

Date: 2025-02
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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