Hedging of Financial Derivative 💼

Welcome to Hedging of Financial Derivative! 📈

This project focuses on implementing a robust trading strategy using statistical arbitrage and convergence techniques for hedging financial derivatives.

Overview 📊

The project utilizes:

• Financial Programming 💻
• Deep learning 🧠
• Machine learning 🤖

Getting Started 🚀

To contribute to this project, follow these steps:

1. Fork the repository on GitHub.
2. Clone the forked project to your local machine: git clone <forked_repo_url>
3. Create a new branch for your work: git checkout -b your-branch-name
4. Make changes and improvements in your branch.
5. Commit your changes: git commit -m 'Add your descriptive commit message'
6. Push your changes to your forked repository: git push origin your-branch-name
7. Submit a Pull Request (PR) to the main repository for review.

Ways to Contribute 🛠️

We welcome contributions in various forms, such as:

• Reporting bugs or issues 🐞
• Providing feedback on the existing codebase 💬
• Submitting fixes for identified issues ✅
• Proposing new features or enhancements 🚀
• Improving documentation 📝
• Adding code snippets, algorithms, or techniques related to financial programming 💼

Code Guidelines 📝

Please adhere to proper coding standards and conventions:

• Follow clear and descriptive commit messages.
• Provide adequate comments within the code for readability.
• Thoroughly test your changes before submitting a PR.

Issue Tracking 🔍

We use GitHub issues to manage tasks. Feel free to open an issue for bugs, suggestions, or discussions related to the project.

Code of Conduct 🤝

We maintain a Code of Conduct to ensure a welcoming environment for all contributors. Please review and follow our Code of Conduct.

Thank you for your interest in contributing to the Financial Derivative Hedging Project! 🙌

Example Strategy 📊

Hedging is a market-neutral trading strategy that enables traders to profit from virtually any market conditions: uptrend, downtrend, or sideways movement. This strategy is categorized as a statistical arbitrage and convergence trading strategy.

How It Works:

1. Cointegration Analysis: Identify cointegrated pairs of stocks within a specified time interval.
2. Spread Calculation: Calculate the spread of the cointegrated pairs using linear regression.
3. Signal Generation: Generate trading signals based on Z-score normalization.
   • Go "Long" the spread whenever the Z-score is below -1.0
   • Go "Short" the spread when the Z-score is above 1.0
   • Exit positions when the Z-score approaches zero
4. Backtesting: Test the strategy on historical data to evaluate performance.
5. Portfolio Returns: Calculate and analyze the returns of the portfolio based on the strategy.