Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting

TL;DR

This paper presents ElliottAgents, a multi-agent AI system that combines Elliott Wave Principle with advanced AI techniques like LLMs, RAG, and DRL to improve stock market trend prediction accuracy.

Contribution

It introduces a novel multi-agent framework integrating traditional technical analysis with AI, enhancing pattern recognition and forecasting in complex financial markets.

Findings

Effective recognition of Elliott wave patterns in historical data

Improved trend forecasting accuracy across multiple time frames

Demonstrated system's robustness on major U.S. stocks

Abstract

Traditional technical analysis methods face limitations in accurately predicting trends in today's complex financial markets. This paper introduces ElliottAgents, an multi-agent system that integrates the Elliott Wave Principle with AI for stock market forecasting. The inherent complexity of financial markets, characterized by non-linear dynamics, noise, and susceptibility to unpredictable external factors, poses significant challenges for accurate prediction. To address these challenges, the system employs LLMs to enhance natural language understanding and decision-making capabilities within a multi-agent framework. By leveraging technologies such as Retrieval-Augmented Generation (RAG) and Deep Reinforcement Learning (DRL), ElliottAgents performs continuous, multi-faceted analysis of market data to identify wave patterns and predict future price movements. The research explores the system's ability to process historical stock data, recognize Elliott wave patterns, and generate actionable insights for traders. Experimental results, conducted on historical data from major U.S. companies, validate the system's effectiveness in pattern recognition and trend forecasting across various time frames. This paper contributes to the field of AI-driven financial analysis by demonstrating how traditional technical analysis methods can be effectively combined with modern AI approaches to create more reliable and interpretable market prediction systems.