Self-Adapting Financial Sentiment Oracles: LLM-Agent Swarms for Real-Time Market Prediction

Abstract

Self-Adapting Financial Sentiment Oracles represents a revolutionary advancement in financial market prediction technology, leveraging biologically-inspired swarm intelligence principles to create unprecedented capabilities in real-time sentiment processing. The framework introduces a distributed network of specialized Large Language Model agents, each optimized for extracting sentiment signals from distinct financial data sources, including news articles, social media platforms, and regulatory filings. Through sophisticated attention-based consensus mechanisms, these agents collaborate to generate integrated market predictions that improve traditional quantitative models to a great extent. The self-adapting architecture of the system employed the algorithm of reinforcement to dynamically adjust agent weight and data source priority based on market conditions, which ensures optimal performance in diverse financial environments. Major innovations include sub-miles and processing delays, multi-source emotion fusion, and comprehensive audit trails that meet regulatory compliance requirements. Framework-distributed processing displays notable scalability through processing architecture that maintains high accuracy by obtaining important computational efficiency benefits. Applications expand algorithm trading, portfolio management, risk evaluation, and regulatory compliance, and benefit from a unique combination of specific intelligence and adaptive learning abilities of each domain. The herd-based design enables improvement in continuous performance through collective teaching mechanisms that overcome individual agent abilities.