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Stock Price Prediction

This repository contains a comprehensive project for predicting stock prices using historical data and machine learning techniques. The project includes data fetching, feature engineering, model training, evaluation, and visualization of predictions.

Table of Contents

Installation

  1. Clone the repository:

    git clone https://github.com/mann-uofg/stock-price-prediction.git
cd stock-price-prediction

  2. Create and activate a virtual environment:

    python -m venv venv
source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`

  3. Install dependencies:

    pip install -r requirements.txt

Project Overview

This project aims to predict stock prices based on historical data using machine learning models such as LSTM and Random Forest. It includes a visualization component to display historical and predicted prices along with trade volumes.

Features

  • Fetch historical stock data using the Yahoo Finance API.
  • Implement feature engineering techniques like moving averages and technical indicators.
  • Train machine learning models for stock price prediction.
  • Evaluate models using metrics such as RMSE, MAE, and directional accuracy.
  • Visualize historical prices, future predictions, and feature importance.

Tech Stack

  • Programming Language: Python
  • Libraries:
    • Data Handling: pandas, numpy
    • Machine Learning: scikit-learn, tensorflow
    • Visualization: matplotlib, mplcursors
    • Data Fetching: yfinance

File Structure

src/
├── data/
│   ├── stock_data.py           # Fetch stock data
│   ├── data_loader.py          # Data loading logic
├── features/
│   ├── feature_engineering.py  # Create features
│   ├── technical_indicators.py # Add technical indicators
├── models/
│   ├── lstm_model.py           # LSTM model logic
│   ├── random_forest_model.py  # Random Forest logic
├── visualization/
│   ├── plotter.py              # Plot historical and predicted prices
│   ├── hover_annotations.py    # Add hover annotations
└── main.py                     # Entry point for the project

Usage (Step 1 & 2 are automatic, don't need to run)

  1. Fetch stock data:

    python src/data/stock_data.py

  2. Train models:

    python src/models/train_model.py

  3. Visualize predictions:

    python3 predict.py

Logic and Implementation

  • Data Fetching: Stock data is fetched from Yahoo Finance using the yfinance library.
  • Feature Engineering: Create new features to enhance predictive performance, such as moving averages and momentum indicators.
  • Model Training: Train models like LSTM for time-series forecasting and Random Forest for feature-based predictions.
  • Evaluation: Use evaluation metrics like RMSE and MAE to compare model performance.
  • Visualization: Plot historical stock prices, predicted values, and trade volumes using matplotlib.

Contributing

Contributions are welcome! Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch: git checkout -b feature-name.
  3. Make your changes and commit them: git commit -m 'Add feature'.
  4. Push the changes: git push origin feature-name.
  5. Submit a pull request.

License

This project is licensed under the MIT License.