mldp_returns

<p>This dataset contains the simulated returns used in the numerical example of Marcos Lopez de Prado's paper, hence the name <code>mldp_returns</code>. The Python code used to reproduce this simulated data is kindly provided by the author in the supplementary material of his paper.</p>

dataset

Machine learning hierarchical risk clustering portfolio allocation strategies.
The implemented methods are:
Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059>.
Hierarchical clustering-based asset allocation (Raffinot, 2017)
<DOI: 10.3905/jpm.2018.44.2.089>.
Hierarchical equal risk contribution portfolio (Raffinot, 2018)
<DOI: 10.2139/ssrn.3237540>.
A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019)
<https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf>.

Carlos Trucios

HierPortfolios

Hierarchical Risk Clustering Portfolio Allocation Strategies

Moon Jun Kwon 

São Paulo Research Foundation (FAPESP), grant 2022/09122-0 

Programa de Incentivo a Novos Docentes da UNICAMP (PIND), grant 2525/23 

mldp_returns function

Returns of 10 simulated assets. — mldp_returns

Returns of 10 simulated assets.

mldp_returns: Returns of 10 simulated assets.

Description

Arguments

References