## 1 Introduction  
The increasing complexity and velocity of financial markets have created unprecedented challenges in market analysis and prediction. Traditional technical analysis methods [1], while foundational to market understanding, often struggle to process and interpret the vast amounts of data generated in modern trading environments [2]. This limitation has sparked significant interest in leveraging AI and machine learning techniques to enhance market analysis capabilities. Evolution of financial market analysis has traditionally relied on two primary approaches: fundamental analysis, which examines intrinsic value through financial metrics, and technical analysis, which focuses on price patterns and market behavior. While these approaches remain relevant across both traditional and cryptocurrency markets, their application and effectiveness vary significantly between these domains. Traditional markets benefit from established valuation metrics and regulated information disclosure [3], whereas cryptocurrency markets often rely more heavily on technical analysis due to the relative absence of standardized fundamental metrics. Technical analysts primarily use andlestick charts, as shown in Figure 1, which provide detailed visualization of price movements and market trends. Among technical analysis methods, the Elliott wave principle (EWP) [4] has emerged as a particularly effective approach for identifying market patterns based on collective investor psychology. The EWP identifies recurring wave formations in price movements, illustrated in Figure 2, which reflect cycles of market sentiment and behavior. However, the manual application of such methods faces significant challenges in today's high-speed, data-intensive trading environment. Market analysis through wave theory was introduced by [PERSON] in the late 1930s. His theory postulates that financial markets progress in predictable cycles, driven by alternating phases of optimism and pessimism among market participants. As demonstrated in Figure 2, these cycles manifest as distinct wave sequences in both upward and downward trends. The figure illustrates how a complete market movement consists of a five-wave pattern in the primary trend direction, followed by reversal of the trend with new wave sequences. This structured approach to market analysis provides analysts with a framework for anticipating potential market reversals and forecasting future price movements. Recent advancements in AI, particularly in the introduction of LLMs into multi-agent systems [6; 7; 8], offer new possibilities for enhancing these traditional approaches. This research makes two important contributions to computer analysis of financial markets. First, we introduce a multi-agent framework that successfully integrates the Elliott wave principle with modern AI technologies, demonstrating how classical technical analysis methods can be enhanced through computational intelligence. Our second contribution is the development of an innovative pattern recognition system that leverages LLMs, retrieval-augmented generation (RAG), and deep reinforcement learning (DRL) to automatically identify and classify Elliott wave formations, significantly improving the accuracy and reliability of technical analysis in high-speed trading environments. Despite the proliferation of AI-driven market analysis tools, several critical challenges persist in the field. Existing systems often struggle to effectively combine traditional technical analysis methods with modern AI capabilities [9], resulting in either overly rigid rule-based systems or black-box AI models that lack interpretability [10]. The identification and interpretation of complex market patterns, particularly those described by the EWP, require advanced analysis that exceeds the capabilities of conventional automated systems. Furthermore, market conditions evolve rapidly, necessitating systems that can continuously learn and adapt while maintaining the reliability of their predictions.