When I saw the answer to this Stack Overflow question, I immediately remembered working on my old post Clustering with Currencies and Fidelity Funds and just had to try to apply this technique. As I should have guessed, it worked with only a minimal amount of changes. Hoping to incrementally improve, I added a couple of slight modifications.
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From TimelyPortfolio |
R code from GIST (select raw to copy/paste):
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require(quantmod) | |
require(fAssets) | |
#get asian currency data from the FED FRED data series | |
getSymbols("DEXKOUS",src="FRED") #load Korea | |
getSymbols("DEXMAUS",src="FRED") #load Malaysia | |
getSymbols("DEXSIUS",src="FRED") #load Singapore | |
getSymbols("DEXTAUS",src="FRED") #load Taiwan | |
getSymbols("DEXCHUS",src="FRED") #load China | |
getSymbols("DEXJPUS",src="FRED") #load Japan | |
getSymbols("DEXTHUS",src="FRED") #load Thailand | |
getSymbols("DEXBZUS",src="FRED") #load Brazil | |
getSymbols("DEXMXUS",src="FRED") #load Mexico | |
getSymbols("DEXINUS",src="FRED") #load India | |
getSymbols("DTWEXO",src="FRED") #load US Dollar Other Trading Partners | |
getSymbols("DTWEXB",src="FRED") #load US Dollar Broad | |
currencies<-merge(DEXKOUS,DEXMAUS,DEXSIUS,DEXTAUS,DEXCHUS,DEXJPUS,DEXTHUS,DEXBZUS,DEXMXUS,DEXINUS,DTWEXO,DTWEXB) | |
currencies<-na.omit(currencies) | |
currencies<-currencies/lag(currencies)-1 | |
# try to do http://stackoverflow.com/questions/9747426/how-can-i-produce-plots-like-this | |
# Sample data | |
n <- NROW(currencies) | |
k <- NCOL(currencies) | |
d <- as.matrix(na.omit(currencies)) | |
x <- apply(d+1,2,cumprod) | |
t <- assetsDendrogramPlot(as.timeSeries(currencies)) | |
r <- t$hclust | |
# Plot | |
op <- par(mar=c(0,0,0,0),oma=c(0,2,0,0)) | |
# set up plot area for the dendrogram | |
plot(NA,ylim=c(.5,k+.5), xlim=c(0,4),axes=FALSE) | |
# Dendogram. See ?hclust for details. | |
xc <- yc <- rep(NA,k) | |
o <- 1:k | |
o[r$order] <- 1:k | |
#separate into 4 groups for color classification | |
groups <- cutree(r, k=4)[r$order] | |
# loop through each to generate the dendrogram | |
# go from innermost to outermost | |
for(i in 1:(k-1)) { | |
a <- r$merge[i,1] | |
x1 <- if( a<0 ) o[-a] else xc[a] | |
y1 <- if( a<0 ) 0 else yc[a] | |
b <- r$merge[i,2] | |
x2 <- if( b<0 ) o[-b] else xc[b] | |
y2 <- if( b<0 ) 0 else yc[b] | |
#do the lines for the dendrogram | |
lines( | |
3+c(y1,i,i,y2)/k, | |
c(x1,x1,x2,x2), | |
lwd=k-i, | |
col=groups[colnames(d)[abs(a)]] | |
) | |
xc[i] <- (x1+x2)/2 | |
yc[i] <- i | |
} | |
# Time series | |
axis(2,1:k,colnames(d)[r$order],las=0, cex.axis=0.6, line=-1, lwd=0, lwd.ticks=1) | |
u <- par()$usr | |
for(i in 1:k) { | |
f <- c(0,3,i-.5,i+.5) | |
f <- c( | |
(f[1]-u[1])/(u[2]-u[1]), | |
(f[2]-u[1])/(u[2]-u[1]), | |
(f[3]-u[3])/(u[4]-u[3]), | |
(f[4]-u[3])/(u[4]-u[3]) | |
) | |
par(new=TRUE,fig=f) | |
plot(x[,r$order[i]],axes=FALSE,xlab="",ylab="",main="",type="l",col=groups[i],lwd=2) | |
box() | |
} | |
par(op) |
Interesting post. Thank you. I played a bit with your code, but there's just one thing that puzzles me is that this method aims at clustering similar series, but observing the results visually I couldnt find any similarities. e.g. why China series are similar to US, if visually India is much closer. or why Singapore and Taiwan are not in one group.
ReplyDeleteIt seems to me that this is a drawback of the method itself, not visualization.