🤖 AI Summary
Traditional technical analysis suffers from limited predictive accuracy in financial markets characterized by nonlinearity, high noise, and frequent exogenous shocks. To address this, we propose ElliottAgents—a novel interpretable AI trading system that is the first to deeply integrate Elliott Wave Theory into a large language model (LLM)-based multi-agent framework, synergizing retrieval-augmented generation (RAG), deep reinforcement learning (DRL), and multi-agent coordination. Our system enables automated wave-pattern recognition, cross-temporal trend inference, and decision traceability, thereby bridging interpretability and adaptability in AI-driven trading. Empirical evaluation on historical U.S. equity data demonstrates that ElliottAgents significantly outperforms baseline methods: waveform identification accuracy and trend prediction stability are markedly improved, with signal accuracy increasing by 23.6% and strategy Sharpe ratio rising by 31.4%.
📝 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.