S&P 500 Stock Market Analysis

This project analyzes historical S&P 500 stock market data to build a machine learning model that predicts daily price movements.

Overview

The notebook downloads historical S&P 500 data using Yahoo Finance API, processes the data to create features and targets, and implements a Random Forest classifier to predict whether the market will go up or down the next day.

Features

• Downloads and processes S&P 500 historical data from Yahoo Finance

• Creates technical features like Open, Close, High, Low, and Volume

• Generates target variable (whether price increases the next day)

• Implements a Random Forest classification model

• Evaluates model performance using precision scoring

Requirements

• Python 3+

• pandas

• yfinance

• scikit-learn

• matplotlib

Installation

pip install yfinance

Usage

1. Run the Jupyter notebook SP500.ipynb
2. The notebook will:

• Download S&P 500 historical data
• Preprocess and feature engineer the dataset
• Train a Random Forest Classifier
• Evaluate model performance

Model

The model uses a Random Forest classifier with:

• 1000 estimators
• Minimum samples split of 100
• Random state set to 1 for reproducibility

Results

The model achieves a precision score of approximately 58% on the test set, demonstrating predictive capability better than random guessing for market direction prediction.

File Structure

• SP500.ipynb - Main analysis notebook
• sp500.csv - Historical data file

Note

This is an educational project for financial data analysis and machine learning. The predictions should not be used for actual trading decisions without further validation and risk assessment