getRichQuick

A web app that crawls the web for stock information, then predicts the price movement using an LSTM model in neural networks.

By entering the ticker of a stock, you will be able to extract the closing price from the previous 5 days.

LSTM model will make predictions on the future prices (up to 7 days) based on the extracted data.

Features 🔥🔥

• Stock Price Prediction: Predict future stock prices based on historical data using an LSTM model.
• Interactive Frontend: User-friendly interface built with React.
• Robust Backend: Django backend for processing predictions and serving results.
• Real-Time Data: Fetches historical stock data using the yfinance library.

Tech Stack ⛏️

• Frontend: React
• Backend: Django
• Machine Learning: PyTorch
• Data Source: Yahoo Finance (yfinance library)

Notebook for LSTM Model

Installation

• Ensure that you meet the following prerequisites:
  • Python 3.12.8
  • Node.js 22.12.0

1. Clone the Repository

git clone https://github.com/lifrocszh/getRichQuick.git

2. cd into the first backend folder, then run python -m venv venv; ./venv/Scripts/activate; pip install -r requirements.txt; to create a virual environment and install python dependencies.
3. Run python manage.py runserver 9000 to start the backend server. (Important: Set 9000 as the port number)
4. cd into the frontend folder, then run npm i; npm start;
5. Wait for both browser windows to open, then access the window with the UI.
6. Key in any ticker from Yahoo! finance, and watch the price predictions!

License

Acknowledgments

• PyTorch: Choice of machine learning framework, enabling the development of the LSTM model used in this project.
• Yahoo Finance: For real-time stock market data and APIs via the yfinance library.
• React: Interactive frontend framework for the web app.
• Django: Framework for backend API.

Other Contributors

Wang Ziheng