[Returnanalytics-commits] r3179 - in pkg/PortfolioAnalytics: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Sep 24 03:55:37 CEST 2013
Author: rossbennett34
Date: 2013-09-24 03:55:36 +0200 (Tue, 24 Sep 2013)
New Revision: 3179
Removed:
pkg/PortfolioAnalytics/man/PortfolioAnalytics-package.Rd
Modified:
pkg/PortfolioAnalytics/R/extract.efficient.frontier.R
pkg/PortfolioAnalytics/man/create.EfficientFrontier.Rd
Log:
Updating documentation
Modified: pkg/PortfolioAnalytics/R/extract.efficient.frontier.R
===================================================================
--- pkg/PortfolioAnalytics/R/extract.efficient.frontier.R 2013-09-24 01:37:53 UTC (rev 3178)
+++ pkg/PortfolioAnalytics/R/extract.efficient.frontier.R 2013-09-24 01:55:36 UTC (rev 3179)
@@ -266,12 +266,12 @@
#' than the simple mean-var and mean-ETL cases. For this type, we actually
#' call \code{\link{optimize.portfolio}} with \code{optimize_method="DEoptim"}
#' and then extract the efficient frontier with
-#' \code{\link{extract.efficient.frontier}}.}
+#' \code{extract.efficient.frontier}.}
#' \item{"random":}{ This can handle more complex constraints and objectives
#' than the simple mean-var and mean-ETL cases. For this type, we actually
#' call \code{\link{optimize.portfolio}} with \code{optimize_method="random"}
#' and then extract the efficient frontier with
-#' \code{\link{extract.efficient.frontier}}.}
+#' \code{extract.efficient.frontier}.}
#' }
#'
#' @param R xts object of asset returns
@@ -290,8 +290,7 @@
#' @seealso \code{\link{optimize.portfolio}},
#' \code{\link{portfolio.spec}},
#' \code{\link{meanvar.efficient.frontier}},
-#' \code{\link{meanetl.efficient.frontier}},
-#' \code{\link{extract.efficient.frontier}}
+#' \code{\link{meanetl.efficient.frontier}}
#' @export
create.EfficientFrontier <- function(R, portfolio, type, n.portfolios=25, risk_aversion=NULL, match.col="ES", search_size=2000, ...){
# This is just a wrapper around a few functions to easily create efficient frontiers
Deleted: pkg/PortfolioAnalytics/man/PortfolioAnalytics-package.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/PortfolioAnalytics-package.Rd 2013-09-24 01:37:53 UTC (rev 3178)
+++ pkg/PortfolioAnalytics/man/PortfolioAnalytics-package.Rd 2013-09-24 01:55:36 UTC (rev 3179)
@@ -1,91 +0,0 @@
-\name{PortfolioAnalytics-package}
-\alias{PortfolioAnalytics-package}
-\alias{PortfolioAnalytics}
-\docType{package}
-\title{
-Numeric methods for optimization of portfolios
-}
-
-\description{
-\kbd{PortfolioAnalytics} provides an \R packaged to provide numerical solutions for portfolio problems with complex constraints and objective sets. The goal of the package is to aid practicioners and researchers in solving portfolio optimization problems with complex constraints and objectives that mirror real-world applications.
-
-One of the goals of the packages is to provide a common interface to specify constraints and objectives that can be solved by any supported solver (i.e. optimization method). Currently supported optimization methods include random portfolios, differential evolution, particle swarm optimization, generalized simulated annealing, and linear and quadratic programming routines. Additional information on random portfolios is provided below. The differential evolution algorithm is implemented via the \kbd{DEoptim} package, the particle swarm optimization algorithm via the \kbd{pso} package, the generalized simulated annealing via the \kbd{GenSA} package, and linear and quadratic programming are implemented via the \kbd{ROI} package which acts as an interface to the \kbd{Rglpk} and \kbd{quadprog} packages.
-
-A key strength of \kbd{PortfolioAnalytics} is the generalization of constraints and objectives that can be solved. The quadratic and linear programming solvers can solve a limited type of convex optimization problems.
-\itemize{
- \item Maxmimize portfolio return subject leverage, box, group, position limit, target mean return, and/or factor exposure constraints on weights.
- \item Minimize portfolio variance subject to leverage, box, group, turnover, and/or factor exposure constraints (otherwise known as global minimum variance portfolio).
- \item Minimize portfolio variance subject to leverage, box, group, and/or factor exposure constraints and a desired portfolio return.
- \item Maximize quadratic utility subject to leverage, box, group, target mean return, turnover, and/or factor exposure constraints and risk aversion parameter.
- \item Minimize ETL subject to leverage, box, group, position limit, target mean return, and/or factor exposure constraints and target portfolio return.
-}
-
-Many real-world portfolio optimization problems are 'global optimization' problems, and therefore are not suitable for linear or quadratic programming routines. \kbd{PortfolioAnalytics} provides a random portfolio optimization method, and also utilizes the \R packages DEoptim, pso, and GenSA for solving non-convex global optimization problems. \kbd{PortfolioAnalytics} supports three methods of generating random portfolios.
-
-\itemize{
- \item The ’sample’ method to generate random portfolios is based on an idea by Pat Burns. This is the most flexible method, but also the slowest, and can generate portfolios to satisfy leverage, box, group, and position limit constraints.
- \item The ’simplex’ method to generate random portfolios is based on a paper by W. T. Shaw. The simplex method is useful to generate random portfolios with the full investment constraint, where the sum of the weights is equal to 1, and min box constraints. Values for min_sum and max_sum of the leverage constraint will be ignored, the sum of weights will equal 1. All other constraints such as the box constraint max, group and position limit constraints will be handled by elimination. If the constraints are very restrictive, this may result in very few feasible portfolios remaining. Another key point to note is that the solution may not be along the vertexes depending on the objective. For example, a risk budget objective will likely place the portfolio somewhere on the interior.
- \item The ’grid’ method to generate random portfolios is based on the \code{gridSearch} function in package \kbd{NMOF}. The grid search method only satisfies the min and max box constraints. The min_sum and max_sum leverage constraint will likely be violated and the weights in the random portfolios should be normalized. Normalization may cause the box constraints to be violated and will be penalized in \code{constrained_objective}.
-}
-
-\kbd{PortfolioAnalytics} leverages the \kbd{PerformanceAnalytics} package for many common objective functions. The objective types in \kbd{PortfolioAnalytics} are designed to be used with \kbd{PerformanceAnalytics} functions, but any user supplied valid R function can be used as an objective.
-
-This summary attempts to provide an overview of how to construct a portfolio object with constraints and objectives, run the optimization, and chart the results.
-}
-
-\section{Optimization}{
-The portfolio object is instantiated with the \code{\link{portfolio.spec}} function. The main argument to \code{\link{portfolio.spec}} is \code{assets}. The \code{assets} argument can be a scalar value for the number of assets, a character vector of fund names, or a named vector of initial weights.
-
-Adding constraints to the portfolio object is done with \code{\link{add.constraint}}. The \code{\link{add.constraint}} function is the main interface for adding and/or updating constraints to the portfolio object. This function allows the user to specify the portfolio to add the constraints to, the type of constraints, arguments for the constraint, and whether or not to enable the constraint. If updating an existing constraint, the indexnum argument can be specified.
-
-Objectives can be added to the portfolio object with \code{\link{add.objective}}. The \code{\link{add.objective}} function is the main function for adding and/or updating objectives to the portfolio object. This function allows the user to specify the portfolio to add the objectives to, the type, name of the objective function, arguments to the objective function, and whether or not to enable the objective. If updating an existing constraint, the indexnum argument can be specified.
-
-With the constraints and objectives specified in the portfolio object, the portfolio object can be passed to \code{\link{optimize.portfolio}} or \code{\link{optimize.portfolio.rebalancing}} to run the optimization. Arguments to \code{\link{optimize.portfolio}} include asset returns, the portfolio obect specifying constraints and objectives, optimization method, and other parameters specific to the solver. \code{\link{optimize.portfolio.rebalancing}} adds support for backtesting portfolio optimization through time with rebalancing or rolling periods.
-}
-
-\section{Charts and Graphs}{
-Intuition into the optimization can be aided through visualization. The goal of creating the charts is to provide visualization tools for optimal portfolios regardless of the chosen optimization method.
-
-\code{\link{chart.Weights}} plots the weights of the optimal portfolio. \code{\link{chart.RiskReward}} plots the optimal portfolio in risk-reward space. The random portfolios, DEoptim, and pso solvers will return trace portfolio information at each iteration when \code{\link{optimize.portfolio}} is run with \code{trace=TRUE}. If this is the case, \code{\link{chart.RiskReward}} will plot these portfolios so that the feasible space can be easily visualized. Although the GenSA and ROI solvers do not return trace portfolio information, random portfolios can be be generated with the argument \code{rp=TRUE} in \code{\link{chart.RiskReward}}. A \code{plot} function is provided that will plot the weights and risk-reward scatter chart. The component risk contribution can be charted for portfolio optimization problems with risk budget objectives with \code{\link{chart.RiskBudget}}. Neighbor portfolios can be plotted in \code{\link{chart.RiskBudget}}, \code{\link{chart.Weights}}, and \code{\link{chart.RiskReward}}.
-
-Efficient frontiers can be extracted from \code{optimize.portfolio} objects or created from a \code{portfolio} object. The efficient frontier can be charted in risk-reward space with \code{\link{chart.EfficientFrontier}}. The weights along the efficient frontier can be charted with \code{\link{chart.Weights.EF}}.
-
-Multiple objects created via \code{\link{optimize.portfolio}} can be combined with \code{\link{combine.optimizations}} for visual comparison. The weights of the optimal portfolios can be plotted with \code{\link{chart.Weights}}. The optimal portfolios can be compared in risk-reward space with \code{\link{chart.RiskReward}}. The portfolio component risk contributions of the multiple optimal portfolios can be plotted with \code{\link{chart.RiskBudget}}.
-}
-
-\section{Further Work}{
-Continued work to improved charts and graphs.
-
-Continued work to improve features to combine and compare multiple optimal portfolio objects.
-
-Support for more solvers.
-
-Comments, suggestions, and/or code patches are welcome.
-}
-
-\author{
-Kris Boudt \cr
-Peter Carl \cr
-Brian G. Peterson \cr
-
-Maintainer: Brian G. Peterson \email{brian at braverock.com}
-}
-
-\references{
-Shaw, William Thornton, \emph{Portfolio Optimization for VAR, CVaR, Omega and Utility with General Return Distributions: A Monte Carlo Approach for Long-Only and Bounded Short Portfolios with Optional Robustness and a Simplified Approach to Covariance Matching} (June 1, 2011). Available at SSRN: http://ssrn.com/abstract=1856476 or http://dx.doi.org/10.2139/ssrn.1856476 \cr
-
-Scherer, B. and Martin, D. \emph{Modern Portfolio Optimization}. Springer. 2005. \cr
-
-}
-
-\section{Acknowledgements}{
-TODO
-}
-
-\seealso{
-CRAN task view on Empirical Finance \cr \url{http://cran.r-project.org/src/contrib/Views/Econometrics.html}
-
-CRAN task view on Optimization \cr \url{http://cran.r-project.org/web/views/Optimization.html}
-
-Large-scale portfolio optimization with DEoptim \cr \url{http://cran.r-project.org/web/packages/DEoptim/vignettes/DEoptimPortfolioOptimization.pdf}
-}
\ No newline at end of file
Modified: pkg/PortfolioAnalytics/man/create.EfficientFrontier.Rd
===================================================================
--- pkg/PortfolioAnalytics/man/create.EfficientFrontier.Rd 2013-09-24 01:37:53 UTC (rev 3178)
+++ pkg/PortfolioAnalytics/man/create.EfficientFrontier.Rd 2013-09-24 01:55:36 UTC (rev 3179)
@@ -63,14 +63,13 @@
\code{\link{optimize.portfolio}} with
\code{optimize_method="DEoptim"} and then extract the
efficient frontier with
- \code{\link{extract.efficient.frontier}}.}
- \item{"random":}{ This can handle more complex
- constraints and objectives than the simple mean-var and
- mean-ETL cases. For this type, we actually call
- \code{\link{optimize.portfolio}} with
- \code{optimize_method="random"} and then extract the
+ \code{extract.efficient.frontier}.} \item{"random":}{
+ This can handle more complex constraints and objectives
+ than the simple mean-var and mean-ETL cases. For this
+ type, we actually call \code{\link{optimize.portfolio}}
+ with \code{optimize_method="random"} and then extract the
efficient frontier with
- \code{\link{extract.efficient.frontier}}.} }
+ \code{extract.efficient.frontier}.} }
}
\author{
Ross Bennett
@@ -79,7 +78,6 @@
\code{\link{optimize.portfolio}},
\code{\link{portfolio.spec}},
\code{\link{meanvar.efficient.frontier}},
- \code{\link{meanetl.efficient.frontier}},
- \code{\link{extract.efficient.frontier}}
+ \code{\link{meanetl.efficient.frontier}}
}
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