The increasing complexity of financial markets has amplified the need for accessible, data-driven tools that assist investors—particularly retail participants—in understanding stock behavior and making informed decisions. This thesis explores the automation of data analysis and visualization techniques for stock market interpretation, with a focus on improving usability for non-expert investors. The study adopts an exploratory-descriptive research design, developing a Python-based system that automates data retrieval, preprocessing, and chart generation using libraries such as yfinance, pandas, matplotlib, and dash. Key technical indicators—such as moving averages, cumulative returns, trading volume, and the Relative Strength Index (RSI)—are derived and visualized through both static plots and an interactive dashboard built using dash. This dashboard enables dynamic filtering, comparative stock analysis, and real-time data exploration via a web-based interface. The system prioritizes user accessibility, providing a modular, scalable framework that simplifies financial data analysis without requiring programming expertise. Future enhancements include live trading integration, tailored investment views, portfolio simulation, and support for multiple markets and asset classes. Overall, this research contributes to the democratization of stock market analysis by combining automation, visualization, and user-centered design.