Here is a way to monitor bond return sources in R. In the next iteration, I will use CPI to add history to the series.

So right now, you can expect about a 5% return from bonds. How much of that is real return is unknown.

R code:

require(quantmod)

require(PerformanceAnalytics)

require(reshape2)

require(ggplot2)

getSymbols(“WGS10YR”,src=”FRED”) #load 10yTreasury

getSymbols(“WFII10″,src=”FRED”) #load 10yTIP for real return

getSymbols(“BAMLC0A0CM”,src=”FRED”) #load Corporate for credit

getSymbols(“CPIAUCSL”,src=”FRED”) #load Corporate for credit

bondReturnSources<-na.omit(merge(WGS10YR-WFII10,WFII10,to.weekly(BAMLC0A0CM)[,4])[“2003::”])

colnames(bondReturnSources)<-c(“10yTreasury”,”10yTIPReal”,”Credit”)

bondReturnSourcesToGraph<-data.frame(cbind(as.Date(index(bondReturnSources)),coredata(bondReturnSources)))

colnames(bondReturnSourcesToGraph)<-c(“Date”,”InflationBreakEven”,”10yTIPReal”,”Credit”)

bondReturnSourcesToGraph<-melt(bondReturnSourcesToGraph,id.vars=1)

colnames(bondReturnSourcesToGraph)<-c(“Date”,”ReturnSource”,”Yield”)

rownames(bondReturnSourcesToGraph)<-c(1:NROW(bondReturnSourcesToGraph))

ggplot(bondReturnSourcesToGraph, stat=”identity”, aes(x=Date,y=Yield,fill=ReturnSource,group=ReturnSource)) + geom_area() +scale_x_date(format = “%Y”) + opts(title = “Sources of Bond Return”)

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**Tags:** bonds, ggplot, R