Garpar
Ver. 1.6.0
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:
☕ 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¶
📜 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
Contents¶
Tutorials
Misc