[Returnanalytics-commits] r2810 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 17 23:40:48 CEST 2013
Author: braverock
Date: 2013-08-17 23:40:48 +0200 (Sat, 17 Aug 2013)
New Revision: 2810
Added:
pkg/PerformanceAnalytics/sandbox/Shubhankit/.Rbuildignore
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EMaxDDGBM.Rd
Removed:
pkg/PerformanceAnalytics/sandbox/Shubhankit/Shubhankit/
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Normalized.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
Log:
- add DESCRIPTION, NAMESPACE, .Rbuildignore
- changes to allow roxygenize to work
- changes to allow R CMD build to work
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/.Rbuildignore
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/.Rbuildignore (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/.Rbuildignore 2013-08-17 21:40:48 UTC (rev 2810)
@@ -0,0 +1,5 @@
+sandbox
+generatechangelog\.sh
+ChangeLog\.1\.0\.0
+week*
+Week*
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-17 21:20:42 UTC (rev 2809)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-17 21:40:48 UTC (rev 2810)
@@ -1,53 +1,32 @@
-Package: Shubhankit
-Type: Package
-Title: Econometric tools for performance and risk analysis.
-Version: 1.1.0
-Date: $Date: 2013-01-29 21:04:00 +0800 (Tue, 29 Jan 2013) $
-Author: Peter Carl, Brian G. Peterson
-Maintainer: Brian G. Peterson <brian at braverock.com>
-Description: Collection of econometric functions for
- performance and risk analysis. This package aims to aid
- practitioners and researchers in utilizing the latest
- research in analysis of non-normal return streams. In
- general, it is most tested on return (rather than
- price) data on a regular scale, but most functions will
- work with irregular return data as well, and increasing
- numbers of functions will work with P&L or price data
- where possible.
-Depends:
- R (>= 2.14.0),
- zoo,
- xts (>= 0.8-9)
-Suggests:
- Hmisc,
- MASS,
- tseries,
- quadprog,
- sn,
- robustbase,
- quantreg,
- gplots,
- ff
-License: GPL
-URL: http://r-forge.r-project.org/projects/returnanalytics/
-Copyright: (c) 2004-2012
-Contributors: Kris Boudt, Diethelm Wuertz, Eric Zivot, Matthieu Lestel
-Thanks: A special thanks for additional contributions from
- Stefan Albrecht, Khahn Nygyen, Jeff Ryan,
- Josh Ulrich, Sankalp Upadhyay, Tobias Verbeke,
- H. Felix Wittmann, Ram Ahluwalia
-Collate:
- 'GLMSmoothIndex.R'
- 'chart.Autocorrelation.R'
- 'ACStdDev.annualized.R'
- 'CalmarRatio.Normalized.R'
- 'na.skip.R'
- 'Return.GLM.R'
- 'table.ComparitiveReturn.GLM.R'
- 'table.UnsmoothReturn.R'
- 'UnsmoothReturn.R'
- 'EmaxDDGBM.R'
- 'maxDDGBM.R'
- 'table.normDD.R'
- 'CDDopt.R'
- 'CDrawdown.R'
+Package: noniid.sm
+Type: Package
+Title: Non-i.i.d. GSoC 2013 Shubhankit
+Version: 0.1
+Date: $Date: 2013-05-13 14:30:22 -0500 (Mon, 13 May 2013) $
+Author: Shubhankit Mohan <shubhankit1 at gmail.com>
+Contributors: Peter Carl, Brian G. Peterson
+Depends:
+ xts,
+ PerformanceAnalytics
+Suggests:
+ PortfolioAnalytics
+Maintainer: Brian G. Peterson <brian at braverock.com>
+Description: GSoC 2013 project to replicate literature on drawdowns and
+ non-i.i.d assumptions in finance.
+License: GPL-3
+ByteCompile: TRUE
+Collate:
+ 'ACStdDev.annualized.R'
+ 'CalmarRatio.Normalized.R'
+ 'CDDopt.R'
+ 'CDrawdown.R'
+ 'chart.Autocorrelation.R'
+ 'EmaxDDGBM.R'
+ 'GLMSmoothIndex.R'
+ 'maxDDGBM.R'
+ 'na.skip.R'
+ 'Return.GLM.R'
+ 'table.ComparitiveReturn.GLM.R'
+ 'table.normDD.R'
+ 'table.UnsmoothReturn.R'
+ 'UnsmoothReturn.R'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-17 21:20:42 UTC (rev 2809)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-17 21:40:48 UTC (rev 2810)
@@ -1,11 +1,12 @@
-export(ACStdDev.annualized)
-export(CDrawdown)
-export(chart.Autocorrelation)
-export(EMaxDDGBM)
-export(GLMSmoothIndex)
-export(QP.Norm)
-export(SterlingRatio.Normalized)
-export(table.ComparitiveReturn.GLM)
-export(table.EMaxDDGBM)
-export(table.NormDD)
-export(table.UnsmoothReturn)
+export(ACStdDev.annualized)
+export(CalmarRatio.Normalized)
+export(CDrawdown)
+export(chart.Autocorrelation)
+export(EMaxDDGBM)
+export(GLMSmoothIndex)
+export(QP.Norm)
+export(SterlingRatio.Normalized)
+export(table.ComparitiveReturn.GLM)
+export(table.EMaxDDGBM)
+export(table.NormDD)
+export(table.UnsmoothReturn)
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Normalized.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Normalized.R 2013-08-17 21:20:42 UTC (rev 2809)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Normalized.R 2013-08-17 21:40:48 UTC (rev 2810)
@@ -1,3 +1,5 @@
+#' QP function fo calculation of Sharpe Ratio
+#'
#' calculate a Normalized Calmar or Sterling reward/risk ratio
#'
#' Normalized Calmar and Sterling Ratios are yet another method of creating a
@@ -45,14 +47,14 @@
#' Normalized.SterlingRatio(managers[,1:6])
#'
#' @export
-#' @rdname CalmarRatio
-#' QP function fo calculation of Sharpe Ratio
+#' @rdname CalmarRatio.normalized
QP.Norm <- function (R, tau,scale = NA)
{
Sharpe= as.numeric(SharpeRatio.annualized(edhec))
return(.63519+(.5*log(tau))+log(Sharpe))
}
+#' @export
CalmarRatio.Normalized <- function (R, tau = 1,scale = NA)
{ # @author Brian G. Peterson
@@ -89,7 +91,7 @@
}
#' @export
-#' @rdname CalmarRatio
+#' @rdname CalmarRatio.normalized
SterlingRatio.Normalized <-
function (R, tau=1,scale=NA, excess=.1)
{ # @author Brian G. Peterson
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd 2013-08-17 21:40:48 UTC (rev 2810)
@@ -0,0 +1,77 @@
+\name{QP.Norm}
+\alias{Normalized.CalmarRatio}
+\alias{Normalized.SterlingRatio}
+\alias{QP.Norm}
+\alias{SterlingRatio.Normalized}
+\title{QP function fo calculation of Sharpe Ratio}
+\usage{
+ QP.Norm(R, tau, scale = NA)
+
+ SterlingRatio.Normalized(R, tau = 1, scale = NA,
+ excess = 0.1)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+
+ \item{scale}{number of periods in a year (daily scale =
+ 252, monthly scale = 12, quarterly scale = 4)}
+
+ \item{excess}{for Sterling Ratio, excess amount to add to
+ the max drawdown, traditionally and default .1 (10\%)}
+}
+\description{
+ calculate a Normalized Calmar or Sterling reward/risk
+ ratio
+}
+\details{
+ Normalized Calmar and Sterling Ratios are yet another
+ method of creating a risk-adjusted measure for ranking
+ investments similar to the \code{\link{SharpeRatio}}.
+
+ Both the Normalized Calmar and the Sterling ratio are the
+ ratio of annualized return over the absolute value of the
+ maximum drawdown of an investment. The Sterling ratio
+ adds an excess risk measure to the maximum drawdown,
+ traditionally and defaulting to 10\%.
+
+ It is also traditional to use a three year return series
+ for these calculations, although the functions included
+ here make no effort to determine the length of your
+ series. If you want to use a subset of your series,
+ you'll need to truncate or subset the input data to the
+ desired length.
+
+ Many other measures have been proposed to do similar
+ reward to risk ranking. It is the opinion of this author
+ that newer measures such as Sortino's
+ \code{\link{UpsidePotentialRatio}} or Favre's modified
+ \code{\link{SharpeRatio}} are both \dQuote{better}
+ measures, and should be preferred to the Calmar or
+ Sterling Ratio.
+}
+\examples{
+data(managers)
+ Normalized.CalmarRatio(managers[,1,drop=FALSE])
+ Normalized.CalmarRatio(managers[,1:6])
+ Normalized.SterlingRatio(managers[,1,drop=FALSE])
+ Normalized.SterlingRatio(managers[,1:6])
+}
+\author{
+ Brian G. Peterson
+}
+\references{
+ Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya,
+ Maximum drawdown. Risk Magazine, 01 Oct 2004.
+}
+\seealso{
+ \code{\link{Return.annualized}}, \cr
+ \code{\link{maxDrawdown}}, \cr
+ \code{\link{SharpeRatio.modified}}, \cr
+ \code{\link{UpsidePotentialRatio}}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EMaxDDGBM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EMaxDDGBM.Rd (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EMaxDDGBM.Rd 2013-08-17 21:40:48 UTC (rev 2810)
@@ -0,0 +1,23 @@
+\name{EMaxDDGBM}
+\alias{EMaxDDGBM}
+\title{Expected Drawdown using Brownian Motion Assumptions}
+\usage{
+ EMaxDDGBM(R, digits = 4)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+}
+\description{
+ Works on the model specified by Maddon-Ismail
+}
+\author{
+ R
+}
+\keyword{Assumptions}
+\keyword{Brownian}
+\keyword{Drawdown}
+\keyword{Expected}
+\keyword{Motion}
+\keyword{Using}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd 2013-08-17 21:20:42 UTC (rev 2809)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd 2013-08-17 21:40:48 UTC (rev 2810)
@@ -1,23 +1,25 @@
-\name{EMaxDDGBM}
-\alias{EMaxDDGBM}
-\title{Expected Drawdown using Brownian Motion Assumptions}
-\usage{
- EMaxDDGBM(R, digits = 4)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-}
-\description{
- Works on the model specified by Maddon-Ismail
-}
-\author{
- R
-}
-\keyword{Assumptions}
-\keyword{Brownian}
-\keyword{Drawdown}
-\keyword{Expected}
-\keyword{Motion}
-\keyword{Using}
-
+\name{table.EMaxDDGBM}
+\alias{table.EMaxDDGBM}
+\title{Expected Drawdown using Brownian Motion Assumptions}
+\usage{
+ table.EMaxDDGBM(R, digits = 4)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+}
+\description{
+ Works on the model specified by Maddon-Ismail which
+ investigates the behavior of this statistic for a
+ Brownian motion with drift.
+}
+\author{
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+}
+\keyword{Assumptions}
+\keyword{Brownian}
+\keyword{Drawdown}
+\keyword{Expected}
+\keyword{Motion}
+\keyword{Using}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-17 21:20:42 UTC (rev 2809)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-17 21:40:48 UTC (rev 2810)
@@ -1,47 +1,47 @@
-\name{Return.GLM}
-\alias{Return.GLM}
-\title{GLM Return Model}
-\usage{
- Return.GLM(edhec,4)
-}
-\arguments{
- \item{Ra}{: an xts, vector, matrix, data frame,
- timeSeries or zoo object of asset returns}
-
- \item{q}{: order of autocorrelation coefficient lag
- factors}
-}
-\description{
- True returns represent the flow of information that would
- determine the equilibrium value of the fund's securities
- in a frictionless market. However, true economic returns
- are not observed. The returns to hedge funds and other
- alternative investments are often highly serially
- correlated.We propose an econometric model of return
- smoothingand develop estimators for the smoothing
- profile as well as a smoothing-adjusted Sharpe ratio.
-}
-\details{
- To quantify the impact of all of these possible sources
- of serial correlation, denote by R(t) the true economic
- return of a hedge fund in period 't'; and let R(t)
- satisfy the following linear single-factor model: where:
- \deqn{R(0,t) = \theta_{0}R(t) + \theta_{1}R(t-1) +
- \theta_{2}R(t-2) .... + \theta_{k}R(t-k)} where
- \eqn{\theta}'i is defined as the weighted lag of
- autocorrelated lag and whose sum is 1.
-}
-\author{
- Brian Peterson,Peter Carl, Shubhankit Mohan
-}
-\references{
- Mila Getmansky, Andrew W. Lo, Igor Makarov,\emph{An
- econometric model of serial correlation and and
- illiquidity in hedge fund Returns},Journal of Financial
- Economics 74 (2004).
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{ts}
-
+\name{Return.GLM}
+\alias{Return.GLM}
+\title{GLM Return Model}
+\usage{
+ Return.GLM(edhec,4)
+}
+\arguments{
+ \item{Ra}{: an xts, vector, matrix, data frame,
+ timeSeries or zoo object of asset returns}
+
+ \item{q}{: order of autocorrelation coefficient lag
+ factors}
+}
+\description{
+ True returns represent the flow of information that would
+ determine the equilibrium value of the fund's securities
+ in a frictionless market. However, true economic returns
+ are not observed. The returns to hedge funds and other
+ alternative investments are often highly serially
+ correlated.We propose an econometric model of return
+ smoothingand develop estimators for the smoothing profile
+ as well as a smoothing-adjusted Sharpe ratio.
+}
+\details{
+ To quantify the impact of all of these possible sources
+ of serial correlation, denote by R(t) the true economic
+ return of a hedge fund in period 't'; and let R(t)
+ satisfy the following linear single-factor model: where:
+ \deqn{R(0,t) = \theta_{0}R(t) + \theta_{1}R(t-1) +
+ \theta_{2}R(t-2) .... + \theta_{k}R(t-k)} where
+ \eqn{\theta}'i is defined as the weighted lag of
+ autocorrelated lag and whose sum is 1.
+}
+\author{
+ Brian Peterson,Peter Carl, Shubhankit Mohan
+}
+\references{
+ Mila Getmansky, Andrew W. Lo, Igor Makarov,\emph{An
+ econometric model of serial correlation and and
+ illiquidity in hedge fund Returns},Journal of Financial
+ Economics 74 (2004).
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
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