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knitR Performance Attempt 2.rnw
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%% LyX 2.0.2 created this file. For more info, see http://www.lyx.org/.
%% Do not edit unless you really know what you are doing.
\documentclass[english,nohyper,noae]{tufte-handout}
\usepackage{helvet}
\usepackage[T1]{fontenc}
\usepackage[latin9]{inputenc}
\usepackage{babel}
\usepackage[unicode=true,pdfusetitle,
bookmarks=true,bookmarksnumbered=true,bookmarksopen=true,bookmarksopenlevel=1,
breaklinks=true,pdfborder={0 0 0},backref=false,colorlinks=false]
{hyperref}
\usepackage{breakurl}
 
\makeatletter
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LyX specific LaTeX commands.
 
\title{knitr Performance Summary Attempt 2}
\author{Timely Portfolio}
\providecommand{\LyX}{\texorpdfstring%
{L\kern-.1667em\lower.25em\hbox{Y}\kern-.125emX\@}
{LyX}}
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Textclass specific LaTeX commands.
<<echo=F>>=
if(exists(".orig.enc")) options(encoding = .orig.enc)
@
 
\makeatother
 
\begin{document}
\maketitle
\begin{abstract}
This time we will try to incorporate some \LyX{} brilliance to help
us format the end result. Unfortunately, this process (most likely
due to my ignorance) was not nearly as seamless as the first experiments
with knitr. I eventually went back to old-school manual coding in RStudio.
\end{abstract}
 
\section{Performance Overview}
 
We all know that performance reports generally blend both text tables
and graphical charts to communicate. In this example, we will use
two of the most popular Vanguard funds (vbmfx and vfinx) as the subjects
for our performance evaluation.
 
<<setup,eval=TRUE,echo=FALSE, warning=FALSE,message=FALSE, results='hide'>>=
require(quantmod)
require(PerformanceAnalytics)
getSymbols("VFINX",from="1990-01-01",adjust=TRUE)
getSymbols("VBMFX",from="1990-01-01",adjust=TRUE)
perf <- na.omit(merge(monthlyReturn(VBMFX[,4]),monthlyReturn(VFINX[,4])))
colnames(perf) <- c("VBMFX","VFINX")
@
 
 
<<lattice-chart,echo=FALSE,eval=FALSE,tidy=TRUE,warning=FALSE,message=FALSE>>=
require(lattice)
require(latticeExtra)
require(reshape2)
#note: table.CalendarReturns only handles monthly data
perf.annual<-as.data.frame(table.CalendarReturns(perf)[,13:14])
perf.annual<-cbind(rownames(perf.annual),perf.annual)
perf.annual.melt <- melt(perf.annual,id.vars=1)
colnames(perf.annual.melt)<-c("Year","Fund","Return")
p1 <- dotplot(Year~Return,group=Fund,data=perf.annual.melt,
pch=19,
lattice.opts=theEconomist.opts(),
par.settings = theEconomist.theme(box = "transparent"),
main="Annual Returns of VFINX and VBMFX",
auto.key=list(space="right"),
xlim=c(min(perf.annual.melt[,3]),max(perf.annual.melt[,3])))
p2 <- densityplot(~Return, group=Fund, data=perf.annual.melt,
lattice.opts=theEconomist.opts(),
par.settings = theEconomist.theme(box = "transparent"))
print(p1,position=c(0,0,0.6,1),more=TRUE)
print(p2+p1,position=c(0.6,0,1,1))
@
<<ref.label='lattice-chart',echo=FALSE,fig.height=4.75,fig.width=7,warning=FALSE,message=FALSE>>=
@
<<xtable-table,echo=FALSE,eval=TRUE,results='tex',tidy=TRUE,warning=FALSE,message=FALSE>>=
require(xtable)
print(xtable(t(last(table.CalendarReturns(perf)[,13:14],13))), floating=FALSE)
@
\newpage
\section{Risk and Return}
Although the summary and distribution of annual returns is a good first step, any real due diligence will require much more than just return. Let's do a very basic plot of risk and return. Of course, there are much more sophisticated methods, which we will explore in future versions.
\begin{figure}
<<chart-stats,echo=FALSE,eval=TRUE,tidy=TRUE,warning=FALSE,message=FALSE,fig.height=4.75,fig.width=7>>=
perf.stats <- table.Stats(perf)
#eliminate observations,na,skewness,and kurtosis
perf.stats <- perf.stats[3:(NROW(perf.stats)-2),]
perf.stats.melt <- melt(as.data.frame(cbind(rownames(perf.stats),perf.stats),stringsAsFactors=FALSE),id.vars=1)
colnames(perf.stats.melt)<-c("Statistic","Fund","Value")
barchart(Statistic~Value,group=Fund,data=perf.stats.melt,
origin=0,
lattice.opts=theEconomist.opts(),
par.settings=theEconomist.theme(box="transparent"),
main="Risk and Return Statistics")
@
\end{figure}
 
\section{Diversification}
In today's markets with almost universally high positive correlations, diversification is much more difficult. However, most performance reports should include some analysis of both correlation and diversification.
\begin{figure}
<<chart-correlation,echo=FALSE,eval=TRUE,tidy=TRUE,warning=FALSE,message=FALSE,fig.height=4,fig.width=6>>=
chart.Correlation(perf, main="Correlation Analysis")
@
<<chart-rollingcorrelation,echo=FALSE,eval=TRUE,tidy=TRUE,warning=FALSE,message=FALSE,fig.height=4,fig.width=6>>=
chart.RollingCorrelation(perf[,1],perf[,2],main="VBMFX and VFINX Rolling 12 Month Correlation",xlab="Correlation")
@
\end{figure}
\begin{figure}
<<chart-diversification,echo=FALSE,eval=TRUE,tidy=TRUE,warning=FALSE,message=FALSE,fig.height=5,fig.width=6,fig.keep="last">>=
require(fPortfolio)
frontier <- portfolioFrontier(as.timeSeries(perf))
frontierPlot(frontier,pch=19,title=FALSE)
singleAssetPoints(frontier,col=c("steelblue2","steelblue3"),pch=19,cex=2)
title(main="Efficient Frontier with VBMFX and VFINX",adj=0)
@
\end{figure}
 
\end{document}
knitr Performance Attempt 2.r
R
1 2
require(knitr)
knit2pdf("knitr performance 2.rnw")

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