ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction

TL;DR

ElliottAgents is a multi-agent system that uses NLP and LLMs to analyze stock market data, providing human-readable predictions and explanations based on the Elliott Wave Principle.

Contribution

The paper introduces a novel multi-agent system integrating NLP, LLMs, and financial analysis principles to enhance interpretability and collaboration in stock market prediction.

Findings

Effective pattern recognition in stock data

Generation of natural language market trend descriptions

Enhanced collaborative analysis through agent dialogue

Abstract

This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, and autonomous decision-making. Experiments demonstrate the system's effectiveness in pattern recognition and generating natural language descriptions of market trends. ElliottAgents contributes to NLP applications in specialized domains, showcasing how AI-driven dialogue systems can enhance collaborative analysis in data-intensive fields. This research bridges the gap between complex financial data and human understanding, addressing the need for interpretable and adaptive prediction systems in finance.