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
• Project Overview
• Features
• Tech Stack
• File Structure
• Usage
• Logic and Implementation
• Contributing
• License

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.