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Modeling-Funding-Rates-Using-Stochastic-Models-and-Quantifying-Risk-for-BTC-Prepetuals

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This project models and predicts funding rates for perpetual contracts using Monte Carlo simulations. It employs Merton’s jump diffusion for index prices and the Ornstein-Uhlenbeck process for funding rates to derive metrics like expected liquidation time and probability. These figures enhance risk management in perpetual contracts trading.

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# Modeling Funding Rates Using Stochastic Models and Quantifying Risk for BTC Prepetuals

## 1. Introduction

This project models and predicts funding rates for perpetual contracts using **Monte Carlo simulations**. It employs **Merton’s jump diffusion** process for index prices and the **Ornstein-Uhlenbeck process** for funding rates to derive metrics like **expected liquidation time** and **probability**. These figures enhance *risk management* in perpetual contracts trading.

## 2. Dependencies

The project requires the following Python libraries:

- `numpy`
- `pandas`
- `plotly`
- `datetime`
- `statsmodels`
- `glob`

## 3. How to Run

1. Clone the repository.
2. Install the required libraries.
3. Run the main script in a Jupyter Notebook or directly using a Python interpreter.

## 4. Project Structure

In this directory you will find:

- The project report `Project Report.pdf` detailing the modeling process, the pipeline architecture and the findings.
- The `Data` directory containing the dataset on which this project was conducted.
- The `Pipeline.ipynb` file containing the python pipeline of the project.
- The `Contracts.csv` contaning the featued contracts in the study.

## 5. Contributing

If you'd like to contribute to this project, feel free to fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

## License

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

## 6. Contact

For any questions or suggestions, please reach out via Email on [yosri.benhalima@ept.ucar.tn].

Modeling-Funding-Rates-Using-Stochastic-Models-and-Quantifying-Risk-for-BTC-Prepetuals

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