dprint quarterly returns.r

require(reshape2)
require(dprint)
require(quantmod)
require(PerformanceAnalytics)

#get manager data set for some date to play with
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))

#get returns by quarter
managers.qtr <- na.omit(ROC(managers.cumul[endpoints(managers.cumul,"quarters")],n=1,type="discrete"))
#get a column for year and a column for the quarter
percent <- function(x, digits = 2, format = "f", ...) 
{ 
  paste(formatC(100 * x, format = format, digits = digits, ...), "%", sep = "") 
} 

managers.qtr.table <- as.data.frame(cbind(format(index(managers.qtr),"%Y"),
                            paste("Quarter ",as.numeric(format(index(managers.qtr),"%m"))/3,sep=""),
                            apply(round(coredata(managers.qtr),4),MARGIN=2,percent)),stringsAsFactors=FALSE)
colnames(managers.qtr.table)[1:2] <- c("year","quarter")

#print pretty table
dev.off()
dprint(year+quarter~., data=managers.qtr.table,
       pg.dim=c(8,10),fit=TRUE,margins=c(0,1,0,1),main="Quarterly Returns")