Optimizing Options Models for Cryptocurrency

Welcome to the Optimizing Options Models for Cryptocurrency repository. This project is focused on developing and refining mathematical models for pricing Bitcoin options in the over-the-counter (OTC) market.

Introduction

Options are financial derivatives that give the buyer the right, but not the obligation, to buy or sell an underlying asset at a predetermined price within a specified time period. The cryptocurrency market, while relatively new, presents unique opportunities for the application of options. This project aims to create accurate and reliable models that reflect the complexity and volatility of the cryptocurrency market.

Project Structure

data: This directory contains datasets used in the project.
Data-Preprocessing.ipynb: A Jupyter notebook for preprocessing the data to be used in the models.
EDA.ipynb: An exploratory data analysis notebook to investigate the datasets and glean insights.

Steps to Achieve the Objective:

Background Research: Conduct comprehensive research on traditional options pricing models and the peculiarities of the crypto market.
Data Collection: Gather historical data on cryptocurrency prices, volatility, trading volumes, and information on existing crypto options traded in the OTC market.

Here rn:

Model Adaptation: Begin with the Black-Scholes model as a baseline and modify it to account for high volatility and unique risk factors associated with cryptocurrencies.

-stochastic volatility, where the volatility itself is treated as a random process.
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- Monte Carlo simulator
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Incorporating Crypto-Specific Factors: Include factors such as regulatory environment, technological changes, and market sentiment that significantly impact crypto markets.
Statistical Analysis and Model Testing: Apply statistical methods to assess the accuracy and reliability of the adapted model through back-testing with historical data.
Optimization: Refine the model based on testing results and employ advanced techniques like machine learning for dynamic adjustments.
Risk Management Integration: Develop a framework for managing risks inherent in crypto options, such as counterparty risk, liquidity risk, and systemic risks.
Documentation and Reporting: Document methodologies, assumptions, and findings. Prepare detailed reports and academic papers outlining the model's development and performance.
Compliance and Regulatory Review: Ensure compliance with relevant financial regulations and standards, particularly those applicable to the crypto market.
Implementation and Real-Time Testing: Implement the model in a real-world environment, monitor, and adjust it based on market feedback.
Continual Review and Update: Regularly review the model to incorporate new market data and emerging trends in the crypto market.

Project Status

Ongoing

Getting Started

To get started with this project, you can clone the repository to your local machine: