garpar 1.6.0.dev1

Generation and Analysis of Real and Artificial Portfolio Returns

Garpar

Generación y análisis de retornos de portafolios artificiales y reales

Generation and analysis of artificial and real portfolio returns

Garpar is a comprehensive toolset for analyzing and managing financial portfolios and markets through advanced quantitative methods. It provides functionality for portfolio optimization, risk assessment, and performance analysis, integrated into the scientific Python stack. The library is open source and commercially usable.

Key Features

• Portfolio Construction & Rebalancing: Build and maintain optimal portfolios with flexible rebalancing strategies
• Risk Metrics Calculation: Comprehensive risk assessment including variance, Value at Risk (VaR), and other standard metrics
• Expected Returns Estimation: Multiple methods for estimating future returns based on historical data
• Correlation & Covariance Analysis: Deep analysis of asset relationships and dependencies
• Diversification Metrics: Quantitative measures of portfolio diversification
• Visualization Tools: Rich set of plotting utilities for portfolio analysis
• Market Data Handling: Robust data validation and preprocessing capabilities
• Entropy-Based Analysis: Advanced information-theoretic approaches to portfolio analysis

💬 Help & Contact

You can contact us at:

• [e-mail]
• [e-mail]

☕ Support

This project is completely free of charge and open source. If you find it useful in your work or simply want to support us, you can buy us a coffee:

📦 Code Repository & Issues

https://github.com/quatrope/garpar

📜 License

Garpar is under MIT License

This license allows unlimited redistribution for any purpose as long as its copyright notices and the license's disclaimers of warranty are maintained.

📚 Citation

If you are using Garpar in your research, please cite:

If you use Garpar in a scientific publication, we would appreciate citations to the following paper:

Giménez, Diego N., Nadia Luczywo, Juan B. Cabral, and Mariana Funes. 2025. "Generación y diseño de herramientas para el análisis de retornos de carteras de inversión artificiales y reales." Revista de la Escuela de Perfeccionamiento en Investigación Operativa 33, no. 57 (2025).

Bibtex entry:

@article{gimenez2025generacion,
  title={Generaci{\'o}n y dise{\~n}o de herramientas para el an{\'a}lisis de retornos de carteras de inversi{\'o}n artificiales y reales},
  author={Gim{\'e}nez, Diego N and Luczywo, Nadia and Cabral, Juan B and Funes, Mariana},
  journal={Revista de la Escuela de Perfeccionamiento en Investigaci{\'o}n Operativa},
  volume={33},
  number={57},
  year={2025}
}

Full Publication: https://revistas.unc.edu.ar/index.php/epio/article/view/49002