README.md

In braverock/PortfolioAnalytics: Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

README

This V2.1 version of PortfolioAnalytics is an update to the substantial V2.0 version that was released on 2024-07-03. We first describe the V2.0 features, then discuss the R demo capability, and finally we describe the additional V2.1 features.

2.0 Features

A major feature of 2.0 is the integration of the CVXR solver R package for convex optimization. CVXR supports eleven solver packages, each of which supports solvers for one or more of the following optimization problems: LP, QP, SOCP, SDP, EXP, MIP. See the Table near the beginning of the document “Convex Optimization in R” at https://cvxr.rbind.io/. Thus, with PortfolioAnalytics 2.0, users are able to use any one of a variety of solvers available in CVXR for their portfolio optimization problems.

A particular use of CVXR in PortfolioAnalytics 2.0 is for computing Minimum Coherent Second Moment (MinCSM) portfolios, which are second-order cone programming (SOCP) optimization problems. This is quite a new capability that is not available in other portfolio optimization software products. Details are provided in the Vignette “cvxrPortfolioAnalytics”.

Another important feature of PortfolioAnalytics 2.0, is that it contains functionality for computing outliers-robust minimum variance (MV) optimal portfolios based on any one of several robust covariance matrix estimators that are not much influenced by outliers Details are provided in the Vignette “robustCovMatForPA”.

New PortfolioAnalytics Functions:

1. meancsm.efficient.frontier (create Mean-CSM efficient frontier) utility function
2. meanrisk.efficient.frontier (generate multiple efficient frontiers for portfolios with the same constraint object.
3. extract_risk (extract the risk value, e.g., StdDev or ES or CSM, based on the weights of a portfolio)
4. chart.EfficientFrontierCompare (Overlay the efficient frontiers of different minRisk portfolio objects on a single plot)
5. backtest.plot (based on Peter Carl’s code, generate plots of the cumulative returns and/or drawdown for back-testing)
6. opt.outputMvo (converts output of optimize.portfolio to a list of the portfolio weights, mean, volatility and Sharpe Ratio)
7. plotFrontiers (plot frontiers based on the result of meanvar.efficient.frontier, meanetl.efficient.frontier or meancsm.efficient.frontier)

Enhanced PortfolioAnalytics Functions:

1. optimize.portfolio (enhanced with CVXR solvers, CSM objective, customizable arg momentFUN= and output ~$moment_values)
2. optimize.portfolio.rebalancing (enhanced with CVXR solvers, CSM objective and customizable arg momentFUN=)
3. create.EfficientFrontier (enhanced with type mean-CSM and mean-risk, and customizable arg momentFUN=)

Support S3 Methods for CVXR:

1. print.optimize.portfolio.CVXR
2. extractStats.optimize.portfolio.CVXR

Custom Moment Functions for Robust Covariance Matrices:

1. custom.covRob.MM
2. custom.covRob.Rocke
3. custom.covRob.Mcd
4. custom.covRob.TSGS
5. MycovRobMcd
6. MycovRobTSGS

New Vignettes and their Code Functions in the demo Folder:

1. cvxrPortfolioAnalytics: CRAN title = “CVXR for PortfolioAnalytics”.
2. demo_cvxrPortfolioAnalytics.R
3. robustCovMatForPA: CRAN title = “Robust Covariance Matrices for PortfolioAnalytics”
4. demo_robustCovMatForPA.R

New 2.1 Features

xxx

Bug Reportin

Please contribute with bug fixes, comments, and testing scripts!

Please take your data and disguise it when submitting, or use data sets like “edhec” like we do in the demos or or like “stocksCRSP” and “factorsSPGMI” in the PCRA package or with your constraints and other objectives modified to demonstrate your problem on public data.

Please report any bugs or issues on the PortfolioAnalytics GitHub page at https://github.com/braverock/PortfolioAnalytics/issues

Acknowledgements

The bulk of the work in creating PortfolioAnalytics 2.0 was done by Xinran Zhao, along with contributions from Yifu Kang, under the support of a 2022 Google Summer of Code (GSOC 2022). Xinran and Yifu were mentored in GSOC 2022 by Professor Doug Martin and Professor Steve Murray in the Applied Mathematics Department at the University of Washington.

braverock/PortfolioAnalytics documentation built on Oct. 16, 2024, 6:07 p.m.