Stock Market Prediction with Random Forest Classifier

This project aims to predict the direction of the S&P 500 index using historical price and volume data. It utilizes machine learning techniques, specifically a Random Forest Classifier, to forecast whether the index will increase or decrease in the future.

Key Features:

• Data Collection: Historical price data for the S&P 500 index is fetched from Yahoo Finance API.
• Preprocessing: Irrelevant columns are removed, and a target variable is created based on tomorrow's price compared to today's price.
• Model Training: A Random Forest Classifier model is trained using historical data, with features such as closing price, volume, open, high, and low.
• Backtesting: The trained model is backtested on historical data to evaluate its performance over different time horizons.
• Feature Engineering: Additional features such as rolling averages, ratios, and trend indicators are engineered to enhance model performance.
• Model Evaluation: Precision score and other evaluation metrics are calculated to assess the model's accuracy.

Project Structure:

• main.py: Contains the main script for data preprocessing, model training, backtesting, and evaluation.
• requirements.txt: Lists all the required dependencies for running the project.

Usage:

1. Install the required dependencies listed in requirements.txt.
2. Run main.py to execute the entire pipeline, from data preprocessing to model evaluation.

Future Improvements:

• Incorporate more advanced machine learning algorithms.
• Optimize model hyperparameters for better performance.
• Explore additional features and data sources for improved prediction accuracy.

Acknowledgements:

This project utilizes the yfinance library for fetching historical stock data and the scikit-learn library for machine learning implementations.