Stock Price Analysis with Python

In this project, you will download historical stock price data and analyze it using Pandas time series tools. You will work with dates as an index, resample data at different frequencies, and compute moving averages.

Stock data is a natural fit for time series because it changes every day and has a clear structure: open, close, high, low, volume.

Project Requirements

• Download historical stock data for at least two companies using the yfinance library

• Set the date column as the DataFrame index

• Plot closing prices over time using line charts

• Resample data to weekly and monthly frequency using .resample()

• Compute 20-day and 200-day moving averages with .rolling()

• Compare trading volume between the two companies

Technologies to Use

• Python

• Pandas

• yfinance

• Matplotlib

• Jupyter Notebook

What You Will Learn

You will learn how to work with dates in Pandas, how resampling and rolling windows work, and how to read time series charts. These are fundamental skills for anyone working with financial, sales, or sensor data.

Want to See a Solution?

For inspiration, check out this tutorial published in Towards Data Science: 🔗 Practical Guide for Time Series Analysis with Pandas

Also, for a more detailed explanation on how to download data using yfinance, check this How to download market data with yfinance and Python tutorial