I'm Dany from Peru 🇵🇪, I studied economic engineering and I have a master in finance. I learned to programming by myself because I like to applied finance theory to real problems. My hobbies are workout at home, play video games, listen metal music, read about quantitative finance and programming applied to finance.

• Riskfolio-Lib: a python library for quantitative portfolio optimization.
• Riskfolio.jl: is a julia version of Riskfolio-Lib, now is in development. It is my excuse to learn Julia.
• Financioneroncios: my personal blog where I share python examples applied to finance, mostly in my native language (spanish).

If you have a question about my projects, want to talk about quantitative finance or a related topic, want to hire my services, have a job opportunity for me or have an interested project you think we can colaborate, you can contact me through:

• LinkedIn
• Gmail

If you want to fund my open-source work and help to keep to maintain and update Riskfolio, you can help me through the following links:

• Github Sponsorship

• 

I'm not an academic but I wrote thirteen working papers based on the mathematics that I needed to build Riskfolio-Lib:

• Advanced Portfolio Optimization: A Cutting-edge Quantitative Approach: This book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions.
• A Semidefinite Programming Approach to Risk Parity Portfolio Optimization: shows a generalization of the risk parity model for the variance that allows to integrate risk contribution inequalities and equalities constraints into classic mean variance models.
• Efficient Gini Mean Difference and Tail Gini Portfolio Optimization based on P-Norms: shows an approximation based on P-Norms to the Gini Mean Difference and Tail Gini portfolio models that are computationally efficient compared to the linear programming formulations.
• A Graph Theory Approach to Portfolio Optimization Part II: shows a how to incorporate the information from dendrogram's cluster into the portfolio optimization process.
• A Graph Theory Approach to Portfolio Optimization: shows a how to incorporate the information about the centrality and neighborhood of graphs into the portfolio optimization process.
• Portfolio Optimization of Brownian Distance Variance: shows a convex formulation of the optimization of brownian distance variance.
• On the Spectral Decomposition of Portfolio Skewness and its Application to Portfolio Optimization: shows how to split portfolio skewness into positive skewness and negative skewness, and how we can approximate the minimization of negative skewness as a quadratic problem.
• Approximation of Portfolio Kurtosis through Sum of Squared Quadratic Forms: shows a convex formulation of the optimization of an approximation of portfolio kurtosis based on a reformulation of kurtosis as a sum of squared quadratic forms.
• Higher Order Moment Portfolio Optimization with L-Moments: shows an alternative way to optimize skewness and kurtosis using a convex formulation based on the OWA portfolio model and L-moments like L-skewness and L-kurtosis.
• Portfolio Optimization of Relativistic Value at Risk: shows a generalization of Entropic Value at Risk based on Kaniadakis entropy. Allows to optimize the Relativistic Value at Risk and Relativistic Drawdown at Risk using softwares that supports disciplined convex programming rules.
• Convex Optimization of Portfolio Kurtosis: shows a convex formulation of the optimization of portfolio kurtosis based on semidefinite and second order cone programming.
• OWA Portfolio Optimization: A Disciplined Convex Programming Framework: shows a generalization of linear risk measures based on the ordered weighted average (OWA) operator. Allows to optimize all linear risk measures using the same formulation only changing the weight parameter of the OWA operator.
• Kelly Portfolio Optimization: a Disciplined Convex Programming Framework: shows a generalization of Kelly criterion that allows us to built Logarithmic Mean Risk Optimal Portfolios.
• Entropic Portfolio Optimization: a Disciplined Convex Programming Framework: shows a discretization of Entropic Value at Risk and Entropic Drawdown at Risk that allows us to use softwares that supports disciplined convex programming rules.
• Robust Portfolio Selection with Near Optimal Centering: shows a method that allows to increase robustness and diversification of portfolios.