A Multi-Agent Approach to Stock Market Prediction and Risk Management

Abstract

This research introduces a simulated AI trading system that functions as an Agentic AI for individuals, capable of understanding and learning while autonomously performing buying and selling of equities in the stock market. The design of this Agentic AI is based on four key pillars: real-time news analysis to assess market sentiment, chart pattern and technical indicator analysis to predict market trends, supply-demand dynamics evaluation to identify market imbalances, and risk management strategies, such as stop-loss mechanisms, to minimize potential losses. Each of these pillars is implemented as a separate Agentic AI model, integrated through a common communication channel. This channel facilitates message exchange between the pillars, enabling collaborative decision-making. Once consensus is achieved among the pillars, the system executes trades autonomously in the stock market. This research demonstrates how advanced AI technologies can be integrated into an Agentic AI system to create a fully autonomous trading bot capable of analyzing, deciding, and acting in real-time financial environments.