| Type: | Package |
| Title: | Hierarchical Risk Clustering Portfolio Allocation Strategies |
| Version: | 1.0.1 |
| Date: | 2024-08-18 |
| Description: | 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. |
| License: | GPL-2 |
| Depends: | R (≥ 3.6.0) |
| Imports: | fastcluster, cluster |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.1 |
| URL: | https://github.com/ctruciosm/HierPortfolios |
| BugReports: | https://github.com/ctruciosm/HierPortfolios/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-08-18 12:49:16 UTC; ctruciosm |
| Author: | Carlos Trucios 
[aut, cre],
Moon Jun Kwon [aut],
São Paulo Research Foundation (FAPESP), grant 2022/09122-0 [fnd],
Programa de Incentivo a Novos Docentes da UNICAMP (PIND), grant 2525/23
[fnd] |
| Maintainer: | Carlos Trucios <ctrucios@unicamp.br> |
| Repository: | CRAN |
| Date/Publication: | 2024-08-18 13:10:05 UTC |
Constrained Hierarchical Risk Parity
Description
Performs the Constrained Hierarchical Risk Parity portfolio strategy proposed by Pfitzinger and Katzke (2019).
Usage
DHRP_Portfolio(covar, graph = FALSE, tau = 1, UB = NULL, LB = NULL)
Arguments
covar |
Covariance matrix of returns. The covariance matrix will be transformed into correlation matrix and then into a distance matrix. |
graph |
To plot de dendrogram set this value to TRUE. By default this value is equal to FALSE. |
tau |
Parameter to evaluate asset similarity at the cluster edges. Default value is 1. |
UB |
Upper bound for weights. By default this value is equal to NULL |
LB |
Lower bound for weights. By default this value is equal to NULL |
Value
portfolio weights
Author(s)
Carlos Trucios and Moon Jun Kwon
References
Pfitzinger, J., and Katzke, N. A constrained hierarchical risk parity algorithm with cluster-based capital allocation (2019). Working Paper.
See Also
HCAA_Portfolio, HRP_Portfolio and HERC_Portfolio
Examples
covar <- cov(mldp_returns)
DHRP_Portfolio(covar)
Hierarchical Clustering-Based Asset Allocation
Description
Performs the Hierarchical Clustering-Based Asset Allocation strategy proposed by Raffinot (2017). Several linkage methods for the hierarchical clustering can be used, by default the "ward" linkage is used. The numbers of clusters is selected using the Gap index of Tibshirani et al. (2001).
Usage
HCAA_Portfolio(covar, linkage = "ward", graph = FALSE, clusters = NULL)
Arguments
covar |
Covariance matrix of returns. The covariance matrix will be transformed into correlation matrix and then into a distance matrix. |
linkage |
Linkage method used in the hierarchical clustering. Allowed options are "single", "complete", "average" or "ward". Default option is "ward". |
graph |
To plot de dendrogram set this value to TRUE. By default this value is equal to FALSE. |
clusters |
Numbers of clusters. If NULL (default), the gap index is applied. |
Value
portfolio weights.
Author(s)
Carlos Trucios
References
Raffinot, Thomas. "Hierarchical clustering-based asset allocation." The Journal of Portfolio Management 44.2 (2017): 89-99.
Tibshirani, Robert, Guenther Walther, and Trevor Hastie. "Estimating the number of clusters in a data set via the gap statistic." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63.2 (2001): 411-423.
See Also
HRP_Portfolio, HERC_Portfolio and DHRP_Portfolio
Examples
covar <- cov(daily_returns)
HCAA_Portfolio(covar)
Hierarchical Equal Risk Contribution
Description
Performs the Hierarchical Equal Risk Contribution portfolio strategy proposed by Raffinot (2018). Several linkage methods for the hierarchical clustering can be used, by default the "ward" linkage is used. This function uses the variance as risk measure. The number of clusters is selected using the Gap index of Tibshirani et al. (2001). The implemenation follows Sjostrand and Nina (2020).
Usage
HERC_Portfolio(covar, linkage = "ward", graph = FALSE, clusters = NULL)
Arguments
covar |
Covariance matrix of returns. The covariance matrix will be transformed into correlation matrix and then into a distance matrix. |
linkage |
Linkage method used in the hierarchical clustering. Allowed options are "single", "complete", "average" or "ward". Default option is "ward". |
graph |
To plot de dendrogram set this value to TRUE. By default this value is equal to FALSE. |
clusters |
Numbers of clusters. If NULL (default), the gap index is applied. |
Value
portfolio weights.
Author(s)
Carlos Trucios and Moon Jun Kwon
References
Raffinot, Thomas. "The hierarchical equal risk contribution portfolio." Available at SSRN 3237540 (2018).
Tibshirani, Robert, Guenther Walther, and Trevor Hastie. "Estimating the number of clusters in a data set via the gap statistic." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63.2 (2001): 411-423.
See Also
HRP_Portfolio, HCAA_Portfolio and DHRP_Portfolio
Examples
covar <- cov(daily_returns)
HERC_Portfolio(covar)
Hierarchical Risk Parity
Description
Performs the Hierarchical Risk Parity portfolio proposed strategy by De Prado (2016). Several linkage methods for the hierarchical clustering can be used, by default the "single" linkage is used.
Usage
HRP_Portfolio(covar, linkage = "single", graph = FALSE)
Arguments
covar |
Covariance matrix of returns. The covariance matrix will be transformed into correlation matrix and then into a distance matrix. |
linkage |
Linkage method used in the hierarchical clustering. Allowed options are "single", "complete", "average" or "ward". Default option is "single". |
graph |
To plot de dendrogram set this value to TRUE. By default this value is equal to FALSE. |
Value
portfolio weights
Author(s)
Carlos Trucios
References
De Prado, Marcos Lopez. "Building diversified portfolios that outperform out of sample." The Journal of Portfolio Management 42.4 (2016): 59-69.
See Also
HCAA_Portfolio, HERC_Portfolio and DHRP_Portfolio
Examples
covar <- cov(mldp_returns)
HRP_Portfolio(covar)
Daily returns (in percentage) of 15 assets.
Description
Dataset used to illustrate how to use the portfolio allocation strategies implemented in this package.
Returns of 10 simulated assets.
Description
This dataset contains the simulated returns used in the numerical example of Marcos Lopez de Prado's paper, hence the name mldp_returns. The Python code used to reproduce this simulated data is kindly provided by the author in the supplementary material of his paper.
References
De Prado, Marcos Lopez. "Building diversified portfolios that outperform out of sample." The Journal of Portfolio Management 42.4 (2016): 59-69.