This project provides a Python script to perform Monte Carlo simulations for option pricing. It's especially useful for complex options with various features and payoffs. Additionally, the project includes a Streamlit web app for visualizing the results. The final result is deployed as Monte Carlo Option Pricing Web App
Feel free to follow this Medium article for detailed instructions.
- Monte Carlo simulation for option pricing.
- Visualize simulated price paths and option payoffs.
- Deployed as a Streamlit web app.
- Python (3.7 or higher)
- Required Python packages (install via
pip install -r requirements.txt
):- numpy
- matplotlib (for local visualization)
- plotly (for web app visualization)
- streamlit
-
Clone the repository to your local machine:
git clone https://github.com/JKaterina/monte-carlo-python.git
-
Navigate to the project directory:
cd monte-carlo-python
-
Install the required Python packages:
pip install -r requirements.txt
Run the Monte Carlo option pricing script:
python monte_carlo_sim.py
Follow the prompts to specify option parameters and view the estimated option price. The script can also be customized for your specific use case.
The project includes a Streamlit web app. To run the web app, use the following command:
streamlit run monte_carlo_app.py
This will launch the app locally, and you can access it via your web browser.
The web app is deployed and accessible online at:
Monte Carlo Option Pricing Web App
If you'd like to contribute to this project, please follow these steps:
- Fork the repository on GitHub.
- Create a new branch with your changes:
git checkout -b feature/your-feature-name
. - Commit your changes and push to your branch.
- Create a pull request to the original repository's
main
branch.
This project is licensed under the MIT License - see the LICENSE file for details.
- The Monte Carlo option pricing model is based on standard financial engineering principles.
- Special thanks to the open-source Python community for the libraries and tools used in this project.
Feel free to customize the README with additional details, project-specific instructions, and acknowledgements as needed.