SPY Linear Regression Analysis

Purpose

This project aims to measure recent volatility in the equities market by creating a simple linear regression model of the SPDR S&P 500 ETF Trust (SPY) from April 21, 2024, to July 21, 2024.

Project Goals

1. Clean and preprocess SPY market data
2. Develop a linear regression model to predict closing prices
3. Analyze the model's performance and feature importance
4. Visualize the results using a Streamlit web application

Requirements

• Python 3.7+
• pandas
• numpy
• scikit-learn
• matplotlib
• streamlit

Project Structure

• dataCleaner.py: Handles data preprocessing and feature engineering
• model.py: Contains the linear regression model and training functions
• app.py: Streamlit web application for displaying results and visualizations
• SPY_4-21-2024_to_7-21-2024.csv: Raw data file (not included in repository)

Installation

1. Clone the repository
2. Install required packages: pip install -r requirements.txt

Usage

1. Ensure the SPY data file is in the correct location
2. Run the Streamlit app: streamlit run app.py

Features

• Data cleaning and preprocessing
• Linear regression model training
• Model performance metrics (MSE, RMSE, R-squared)
• Visualization of actual vs predicted close prices
• Feature importance analysis

License

Undecided