Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets

Abstract

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability due to their black-box nature. This paper presents ElliottAgents, a multi-agent system that combines the Elliott wave principle with LLMs, showcasing the application of deep reinforcement learning (DRL) and natural language processing (NLP) in financial analysis. By integrating retrieval-augmented generation (RAG) and deep reinforcement learning (DRL), the system processes vast amounts of market data to identify Elliott wave patterns and generate actionable insights. The system employs a coordinated team of specialized agents, each responsible for specific aspects of analysis, from pattern recognition to investment strategy formulation. We tested ElliottAgents on both stock and cryptocurrency markets, evaluating its effectiveness in pattern identification and trend prediction across different time scales. Our experimental results demonstrate improvements in prediction accuracy when combining classical technical analysis with AI-driven approaches, particularly when enhanced by DRL-based backtesting process. This research contributes to the advancement of financial technology by introducing a scalable, interpretable framework that enhances market analysis capabilities, offering a promising new methodology for both practitioners and researchers.

Keywords

• multi-agent systems
• Elliott wave principle
• large language models (LLMs)
• investment strategies
• deep reinforcement learning (DRL)
• financial markets