This project presents an automated stock trading and portfolio management system inspired by how human investors analyze publicly available information before making investment decisions. The system combines feature selection techniques, machine learning algorithms, and statistical calculations to support intelligent trading decisions and market analysis. It incorporates financial indicators such as Bollinger Bands and market simulation techniques to evaluate trading strategies and optimize investment outcomes. The project demonstrates the application of algorithmic trading concepts in finance through data-driven decision-making and predictive analysis.