AI-Driven Network Optimization for Real-Time Financial Data Streaming in High-Frequency Trading Systems

This paper demonstrates how network and edge computing is used for streaming real-time financial data via AI driven high-frequency trading system. By adopting cutting-edge AI methodologies, the study seeks to enhance the efficacy of core networking functions including latency, throughput, packet loss, security, scalability, and energy efficiency. The results show that AI-optimized networks exceed those based on traditional techniques in terms of response times, delays and uptime. Moreover, based on predictive analysis and adaptive resource management, AI-powered solutions can guarantee constant performance even in scenarios with high traffic and failure of the network. Finally, the work emphasizes the need to balance computational costs and the performance improvements achieved through AI in high-frequency trading. The findings highlight the potential of AI to dramatically change the landscape of next-gen financial data networks.