This research introduces a sentiment-driven trading system that leverages large language models to analyze financial news and social media in real-time, generating actionable trading signals.

• Integrates FinGPT for domain-specific sentiment analysis of market data
• Combines sentiment insights with technical indicators for comprehensive trading signals
• Utilizes Kubernetes for scalable, efficient system deployment
• Addresses financial security concerns through robust system architecture

Why it matters: This system represents a significant advancement in algorithmic trading security, creating a more resilient approach that can adapt to market volatility through real-time sentiment analysis while maintaining data protection standards.

An End-To-End LLM Enhanced Trading System