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.