Stock Correlations

Chart 1.R

#Load Data
rm(list = ls(all = TRUE))
library(quantmod)
library(PerformanceAnalytics)

symbols <- c('XLE','XLV','XLI','XLU','XLP','IYZ','XLK','XLY','XLF','XLB','GLD','SLV','EFA','EEM','FXA','FXE','FXY','HYG','LQD')

getSymbols(symbols,from='2007-01-01')
getSymbols('SPY',from='2007-01-01')

SP500 <- Cl(SPY)
colnames(SP500)[1] <- 'SPY'

#Function to build a dataframe form a list of symbols
symbolFrame <- function(symbolList) {
	Data <- data.frame(NULL)
	for (S in symbolList) {
		Data <- cbind(Data,Cl(get(S)))
	}
	colnames(Data) <- symbolList
	return(Data)

}

#Make a color palette for the graphj
library(fBasics)
colorset <- qualiPalette(length(symbols), name="Set1")

#Chart Correlations
Data <- symbolFrame(symbols)
Data <- Data['2010-01-01::']
chart.RollingCorrelation(Data, SP500, legend.loc="bottomleft",colorset=colorset, main = "Rolling 3-month Correlation",width=90)

Chart 2.R

#Modified version of chart.RollingCorrelation that returns the data rather than plotting it
table.RollingCorrelation <- function (Ra, Rb, width = 12, na.pad = FALSE, ...) 
{
    Ra = checkData(Ra)
    Rb = checkData(Rb)
    columns.a = ncol(Ra)
    columns.b = ncol(Rb)
    columnnames.a = colnames(Ra)
    columnnames.b = colnames(Rb)
    for (column.a in 1:columns.a) {
        for (column.b in 1:columns.b) {
            merged.assets = merge(Ra[, column.a, drop = FALSE], 
                Rb[, column.b, drop = FALSE])
            column.calc = rollapply(na.omit(merged.assets[, , 
                drop = FALSE]), width = width, FUN = function(x) cor(x[, 
                1, drop = FALSE], x[, 2, drop = FALSE]), by = 1, 
                by.column = FALSE, na.pad = na.pad, align = "right")
            column.calc.tmp = xts(column.calc)
            colnames(column.calc.tmp) = paste(columnnames.a[column.a], 
                columnnames.b[column.b], sep = " to ")
            column.calc = xts(column.calc.tmp, order.by = time(column.calc))
            if (column.a == 1 & column.b == 1) 
                Result.calc = column.calc
            else Result.calc = merge(Result.calc, column.calc)
        }
    }
    return(Result.calc)
}

#Calculate mean correlation among US Sectors & plot
sectors <- c('XLE','XLV','XLI','XLU','XLP','IYZ','XLK','XLY','XLF','XLB')
corrs <- table.RollingCorrelation(symbolFrame(sectors), SP500, width=90)
meancorr <- apply(corrs,1,mean)
meancorr <- xts(meancorr,order.by=index(corrs))
plot(meancorr, main = "Mean Rolling 3-month Correlation Among Major US Sectors")