Stock Performance Analysis — Extraction, Scraping & Dashboard

An end-to-end financial data analysis project that combines API-based data retrieval and web scraping to extract, process, and visualize historical stock price and revenue data for major publicly traded companies.

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Project Notebooks

Notebook	Focus
01_stock_data_extraction.ipynb	Extracting stock metadata, price history, and dividends using the yfinance API
02_web_scraping.ipynb	Scraping historical stock price tables from web pages using BeautifulSoup
03_stock_dashboard.ipynb	Full pipeline: combining both techniques to build interactive price vs revenue dashboards

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Key Questions Explored

• How has Apple's stock price trended over its full trading history, and how have dividend payments grown?
• How does yfinance compare to web scraping as a data extraction method for financial analysis?
• Does Tesla's stock price growth reflect its underlying revenue growth?
• How did GameStop's 2021 short squeeze compare to its actual business revenue — and what does that tell us about market sentiment?

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Dashboard Previews

Stock	Dashboard
Tesla
GameStop

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Key Findings

• Apple's long-term growth: AAPL's price chart shows sustained appreciation with dramatic acceleration post-2019, supported by consistent dividend growth since 2012.
• Tesla — fundamentals-driven: Tesla's stock price rise closely tracks its revenue growth, making it a strong example of price reflecting improving business performance.
• GameStop — sentiment vs fundamentals: GME's revenue was in long-term decline, yet its stock price spiked explosively in early 2021 due to a Reddit-driven short squeeze — a textbook case of market sentiment decoupling from reality.
• API vs scraping: provides clean, structured data instantly, while BeautifulSoup scraping is essential for data sources without an API — both are valuable tools in a financial analyst's toolkit.