Monte Carlo

Monte Carlo is a simulation and uses CoinGecko API. This fintech app fetches historical cryptocurrency price data and performs Monte Carlo simulations for future value prediction of a cryptocurrency investment, generating interactive graphs.

Video Presentation

Deployment Instructions

Windows:

1. Clone the git repository: git clone https://github.com/Dealer86/Monte_Carlo.git.
2. CD into your created directory.
3. Create a new virtual environment by running python -m venv env/.
4. Activate the virtual environment by running .\env\Scripts\activate.
5. Upgrade pip by running python.exe -m pip install --upgrade pip.
6. Install the required dependencies by running pip install -r requirements.txt.
7. CD into the montecarlo directory where manage.py module lives.
8. Run: python manage.py runserver
9. Check localhost.

Technology Stack

Django Web Framework

• Django: The core web framework for building the project.

Backend

• Python 3.11: The primary programming language for the backend logic.
• Requests: Python library for making HTTP requests.
• NumPy: Library for numerical computations in Python.
• Pandas: Data manipulation and analysis library.
• Matplotlib: Plotting library for creating visualizations.
• mpld3: Matplotlib-based library for D3.js-inspired interactive visualizations.

Frontend

• HTML: Used for structuring web pages.
• CSS: Applied for styling the user interface.
• Bootstrap: Front-end framework for creating responsive and modern UI components.

Testing

• Unit Tests: Using Django's built-in testing framework.
• Continuous Integration: This project uses GitHub Actions for automated continuous integration.
• GitHub Actions Status Badge

Third-Party Tools

• OBS: Used for video presentation.
• DaVinci Resolve: Video editing.

License

• This project is licensed under the MIT License.