Path shadowing Monte carlo simulation

Using this article, we will implement the Path Shadowing Monte Carlo method.

Overview

This repository implements the Path Shadowing Monte Carlo (PSMC) method. PSMC is a stochastic model designed for volatility prediction and option pricing by averaging future quantities over generated price paths whose past history aligns with the actual observed history. The method can be applied to financial models and time-series data analysis.

Features

• Prediction and Pricing: Simulates future volatility and provides insights for pricing options based on historical paths.
• Shadowing Paths: Tracks price paths that align with observed historical data.
• Multi-Processed: Leverages multi-processing to efficiently scan large datasets for shadowing paths.
• Time-Series Generation: Utilizes wavelet scattering spectra to generate synthetic time-series data.

Installation

1. Clone the repository:

   git clone https://github.com/MathQuantLab/path-shadowing-monte-carlo.git

2. Install the dependencies:

   pip install -r requirements.txt

Optional: Install the scattering spectra package used for time-series generation:

pip install git+https://github.com/RudyMorel/scattering_spectra

Usage

To use the Path Shadowing Monte Carlo method, you can run the main class PathShadowing from the path_shadowing.py file.

Example:

from path_shadowing import PathShadowing

# Example usage
shadowing = PathShadowing(data)
shadowing.run_simulation()

Additionally, you can explore the notebook tutorial.ipynb for a hands-on guide on how to use the method with sample data.

Dependencies

• Python 3.x
• NumPy
• Scikit-learn
• Pandas
• Matplotlib
• scattering_spectra

Contributing

Contributions are welcome! Feel free to submit issues, feature requests, or pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.