Inspiration
I have always been passionate about finance, but accessing all the necessary data for comprehensive stock analysis can be challenging. To address this, I developed a Streamlit app that consolidates key financial information, making equity research more accessible. The app provides direct links to a company's SEC filings and performs advanced statistical analysis, including GARCH, EGARCH, QQ plots, and KDE estimation, to assess stock behavior. Additionally, it features an options pricing model, offering users insights into the fair value of options. With further development, this tool has the potential to benefit both retail and institutional investors, enhancing their analytical capabilities. One of the key challenges I faced was deploying my Streamlit app from a local environment to the web, primarily due to redundant dependencies in the requirements.txt file. Additionally, implementing the mathematical models for various analytical functions required a strong theoretical foundation, which proved to be quite complex. However, overcoming these challenges allowed me to deepen my technical knowledge and refine my problem-solving skills. I thoroughly enjoyed the learning process and will continue to enhance the app by improving its design, optimizing performance, and incorporating new features.
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