Another Experiment with R and Sweave

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The R package PApages is a great start towards addressing the very common problem of internal and external reporting in the money management industry.  Advent’s APX, Axys, and Black Diamond and the up and coming extremely well-connected and well-funded Addepar provide basic and acceptable reporting but generally don’t provide the full set of risk and return metrics that I would expect.  Since the very successful GSOC projects with PerformanceAnalytics …Now With More Bacon (2008)! and New Attribution Functions for PortfolioAnalytics, we have a comprehensive and robust set of risk, return, and attribution measures in R.  Combined with the near limitless graphical abilities of R with xtsExtra, ggplot, lattice, and base graphics, R seems to offer one of the best platforms for reporting, so I’ve committed myself to continue my series http://timelyportfolio.blogspot.com/search/label/reporting exploring various reporting options in R.

This is a fairly crude sketch of something we can accomplish easily with R, Sweave, and PerformanceAnalytics.  I hope to itergreat to something a little more compelling.  If the embedded pdf does not work below, please see at https://www.box.com/s/xpfn3rjwwmv8aftmkbyi.

R Sweave file from GIST:

\documentclass[nohyper,justified]{tufte-handout}
%\documentclass{article}
%\usepackage[absolute,showboxes]{textpos}
\usepackage[absolute]{textpos}
\usepackage{sidecap}
%\usepackage{color}
%\usepackage[usenames,dvipsnames,svgnames,table]{xcolor}
\begin{document}
\SweaveOpts{concordance=TRUE}
\begin{wide}
\section{\Huge Performance Summary with Sweave and R}
{\Large Here is a little experiment with R and Sweave to produce a performance report. I have done some samples in the past, but I wanted to iterate through a couple more, especially to evaluate other options for what has been started in the PApages package. Of course, this text could be easily replaced with some commentary from a manager about opportunities, thoughts, or current allocation. A dashboard set of charts also might be very helpful here.}
\hrulefill
\end{wide}
<<eval=TRUE,echo=FALSE,results=hide,warning=FALSE>>=
#do requires and set up environment for reporting
require(xtable)
require(ggplot2)
require(directlabels)
require(reshape2)
require(latticeExtra)
require(quantmod)
require(PerformanceAnalytics)
data(managers)
#get xts in df form so that we can melt with the reshape package
#will use just manager 1, sp500, and 10y treasury
managers <- managers[,c(1,8,9)]
#add 0 at beginning so cumulative returns start at 1
#also cumulative will match up datewise with returns
managers <- as.xts(rbind(rep(0,NCOL(managers)),coredata(managers)),
order.by=c(as.Date(format(index(managers)[1],"%Y-%m-01"))-1,index(managers)))
managers.df <- as.data.frame(cbind(index(managers),coredata(managers)),stringsAsFactors=FALSE)
#melt data which puts in a form that lattice and ggplot enjoy
managers.melt <- melt(managers.df,id.vars=1)
colnames(managers.melt) <- c("date","account","return")
managers.melt[,1] <- as.Date(managers.melt[,1])
#get cumulative returns starting at 1
managers.cumul <- as.xts(
apply(managers+1,MARGIN=2,FUN=cumprod),
#add end of first month to accommodate the 1 that we add
order.by=index(managers))
managers.cumul.df <- as.data.frame(cbind(index(managers.cumul),
coredata(managers.cumul)),
stringsAsFactors=FALSE)
managers.cumul.melt <- melt(managers.cumul.df,id.vars=1)
colnames(managers.cumul.melt) <- c("date","account","return")
managers.cumul.melt[,1] <- as.Date(managers.cumul.melt[,1])
#get rolling returns for 1y, 3y, 5y, since inception
trailing <- table.TrailingPeriods(managers, periods=c(12,36,60,NROW(managers)),FUNCS=c("mean"),funcs.names=c("return"))
trailing.df <- as.data.frame(cbind(c("1y","3y","5y","SinceIncep"),
c(rep("return",4)),
coredata(trailing)),
stringsAsFactors=FALSE)
trailing.melt <- melt(trailing.df,id.vars=1:2)
colnames(trailing.melt) <- c("period","measure","account","return")
#function to get numbers in percent format
#will use \\ to play well with tikz
percent <- function(x, digits = 2, format = "f", ...)
{
paste(formatC(100 * x, format = format, digits = digits, ...), "%", sep = "")
}
@
\begin{textblock*}{150mm}(5mm,70mm)
\begin{figure}
%to really fill the page, this works nicely
%\begin{minipage}[t]{1.2\linewidth}
%\begin{minipage}[t]{0.5\linewidth}
\vspace{0pt}
<<echo=FALSE,eval=TRUE,fig=TRUE,width=4,height=6>>=
charts.PerformanceSummary(managers,
colorset=c(brewer.pal(9,"Blues")[6],brewer.pal(8,"Greys")[c(5,4)]),
xlab=NULL)
@
\end{figure}
\end{textblock*}
\begin{textblock*}{85mm}(120mm,103mm)
Cumulative returns offer one of the best methods to evaluate the ability of a manager to achieve long term returns. Ultimately, the cumulative return is often one of the primary objectives of our clients.
\newline
\begin{figure}
\vspace{0pt}
<<echo=FALSE,eval=TRUE,results=tex>>=
trailingtable <- apply(trailing,MARGIN=2,FUN=percent)
rownames(trailingtable) <- c("1y","3y","5y",paste("Since Inception ",format(index(managers)[1],"%b %Y")))
print(xtable(trailingtable), floating=FALSE, scalebox=0.8)
@
\end{figure}
\end{textblock*}
\begin{textblock*}{85mm}(120mm,161mm)
However, cumulative returns must also be evaluated with reference to the risks incurred to generate those returns. Below are multiple risk measures. We are most concerned with limiting drawdowns shown in the bottom left chart.
\newline
\begin{figure}
\vspace{0pt}
<<echo=FALSE,eval=TRUE,results=tex>>=
risktable <- table.DownsideRisk(managers)
print(xtable(risktable), floating=FALSE, scalebox=0.7)
@
\end{figure}
\end{textblock*}
\end{document}

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