Portfolio-Analysis

Open-source portfolio analysis tools for DIY investors and finance enthusiasts. This repository aims to provide a comprehensive suite of tools to analyze and optimize investment portfolios, with an emphasis on transparency, flexibility, and extensibility.

Getting Started

Try out the Basic Portfolio Analysis Notebook using Google Colab. It's free and runs in the browser.

Prerequisites

• Python 3.7+
• numpy, pandas, scipy, matplotlib
• Additional libraries as specified in requirements.txt

Installation

git clone https://github.com/yourusername/portfolio-analysis.git
cd portfolio-analysis
pip install -r requirements.txt

Contribution Guidelines

We welcome contributions from the community. Please read the following guidelines before contributing:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-name).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature-name).
5. Create a new Pull Request.

Here is a more detailed instrctuion set for beginners: A Beginner's Guide to Contributing to the Portfolio Analysis Repository

Key Feature Requests and Improvements

• Clarify Leverage Handling: Ensure that documentation and examples are clear on how leverage is treated in portfolio calculations.
• Import Time-Varying Risk-Free Rate: Integrate the ability to use dynamic risk-free rates, particularly using short-term T-bill data.
• Add Unit Tests: Develop a comprehensive suite of unit tests to cover the functionality of the package.
• Package Code for PyPI: Prepare and publish the package to PyPI for easier distribution and installation.
• Add Factor Regressions: Implement factor regression analysis tools to assess portfolio exposures.
• Integrate Portfolio Optimization Tools: Add tools for optimizing portfolios based on various risk and return metrics.
• Integrate Visualization Tools: Develop visualization tools to aid in the interpretation and presentation of portfolio analysis results.

List of Other Open-Source Finance Resources

• Awesome Quant: A curated list of open-source libraries and resources in quantitative finance.
• Quant Stats: Portfolio analytics for quants, including performance and risk metrics.
• skfolio: A package for portfolio performance evaluation.
• Riskfolio-Lib: A Python library for portfolio optimization and risk management.
• PyPortfolioOpt

License

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

Contact

For any questions or suggestions, please feel free to reach out.

Twitter: @egr_investor

GitHub: engineerinvestor

Email: egr.investor (gmail)