AI-Based Trading System

Abstract

The financial trading environment has evolved significantly due to rapid advancements in data generation, computational power, and market complexity. Traditional trading systems rely heavily on manual analysis, technical indicators, and predefined strategies, which are often insufficient to handle real-time market fluctuations and large-scale datasets. This limitation creates the need for intelligent systems capable of analyzing both numerical and textual data efficiently. This paper presents an AI-Based Trading System that integrates Machine Learning (ML) techniques with Large Language Models (LLMs) to enhance trading decision-making. The system utilizes Long Short-Term Memory (LSTM) networks for time-series forecasting, enabling accurate prediction of stock price movements based on historical data. Simultaneously, LLMs are employed to perform sentiment analysis on financial news, social media content, and market reports, extracting valuable insights that influence market behavior.

A hybrid decision engine is developed to combine numerical predictions with sentiment scores, generating actionable trading signals such as BUY, SELL, or HOLD. The system also includes a real-time visualization dashboard that displays market trends, confidence levels, and performance metrics, improving user understanding and interaction. Experimental results demonstrate that the proposed system improves prediction accuracy, adaptability, and automation compared to traditional methods. The integration of ML and LLM provides a comprehensive approach to intelligent trading, making the system suitable for real-world financial applications.