HierPortfolios: Hierarchical Clustering-Based Portfolio Allocation Strategies

Machine learning portfolio allocation strategies based on hierarchical clustering methods. 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>.

Version: 1.0.0
Depends: R (≥ 3.6.0)
Imports: fastcluster, cluster
Published: 2024-03-31
Author: Carlos Trucios and Moon Jun Kwon
Maintainer: Carlos Trucios <ctrucios at unicamp.br>
BugReports: https://github.com/ctruciosm/HierPortfolios/issues
License: GPL-2
URL: https://github.com/ctruciosm/HierPortfolios
NeedsCompilation: no
Materials: README
CRAN checks: HierPortfolios results

Documentation:

Reference manual: HierPortfolios.pdf

Downloads:

Package source: HierPortfolios_1.0.0.tar.gz
Windows binaries: r-devel: HierPortfolios_1.0.0.zip, r-release: HierPortfolios_1.0.0.zip, r-oldrel: HierPortfolios_1.0.0.zip
macOS binaries: r-release (arm64): HierPortfolios_1.0.0.tgz, r-oldrel (arm64): HierPortfolios_1.0.0.tgz, r-release (x86_64): HierPortfolios_1.0.0.tgz
Old sources: HierPortfolios archive

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