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Stock Analysis and Visualization

This project analyzes and visualizes stock data for SPY (S&P 500 ETF) and GOOG (Google/Alphabet) using Python. It demonstrates various data analysis techniques and visualization methods using popular libraries such as pandas, numpy, matplotlib, and seaborn.

Features

  • Fetches historical stock data using yfinance
  • Calculates logarithmic returns
  • Computes correlation matrix between stocks
  • Performs linear regression analysis
  • Visualizes stock price trends and scatter plots

Dependencies

  • numpy
  • pandas
  • pandas_datareader
  • matplotlib
  • seaborn
  • yfinance

Usage

  1. Install the required dependencies:
  2. Run the script to generate visualizations and analysis:

Visualizations

The script generates several visualizations:

  1. Scatter plot of SPY vs GOOG returns with a linear regression line
  2. Time series plot of SPY (S&P 500) closing prices with a trend line
  3. Seaborn regression plot for the last 63 days of SPY data

Analysis

The script performs the following analyses:

  • Calculates correlation matrix between SPY and GOOG returns
  • Fits a linear regression model to the sampled data
  • Predicts future stock prices based on the regression model

Contributing

Feel free to fork this repository and submit pull requests with improvements or additional features.

License

This project is open-source and available under the MIT License.