This project analyzes the stock prices and quarterly revenue data of Tesla (TSLA) and GameStop (GME). The analysis is performed using Python in a Jupyter Notebook, with data fetched via the yfinance
library and web scraping techniques.
This analysis covers the following key steps:
-
Data Retrieval:
- Use
yfinance
to fetch historical stock data for Tesla and GameStop. - Web scraping to extract quarterly revenue data for both companies.
- Use
-
Data Processing:
- Cleaning and preparing the data for analysis, including resetting indices and converting date formats.
-
Data Visualization:
- Generate visualizations comparing stock prices and revenue over time to observe trends and anomalies.
Analysis of Tesla and GameStop.ipynb
: The Jupyter Notebook containing all the code and analysis.README.md
: This file, providing an overview of the project.
To run this notebook, you'll need to have the following installed:
- Python 3.x
- Jupyter Notebook
- Required Python libraries:
yfinance
,pandas
,matplotlib
,beautifulsoup4
,requests
You can install the required libraries using pip:
pip install yfinance pandas matplotlib beautifulsoup4 requests