I was on vacation the last week and wrote some code to run a 500-day
correlation between the Nasdaq tracking stock (QQQ) and 191 currency pairs
for 500 days. The initial run took 9 hours(!) and I'd like to make it
faster. So, I'm including my code below, in hopes that somebody will be able
to figure out how to make it faster, either through parallelisation, or by
making changes. I've marked the places where Rprof showed me it was slowing
down:
currencyCorrelation <- function(lagtime = 1) {
require(quantmod)
dataTrack <- getSymbols(commandArgs(trailingOnly=T)[1], from='2009-11-21',
to='2011-04-03')
stockData <- get(dataTrack)
currencies <- row.names(oanda.currencies[grep(pattern='oz.', fixed=T, x
=as.vector(oanda.currencies$oanda.df.1.length.oanda.df...2....1.)) == F])
correlations <- vector()
values <- list()
# optimise these loops using the apply family
for (i in currencies) {
for (j in currencies) {
if (i == j) next()
fx <- getFX(paste(i, j, sep='/'), from='2009-11-20', to='2011-04-02')
# Prepare data by getting rates for market days only
fx <- get(fx)
fx <- fx[which(index(fx) %in% index(QQQ$QQQ.Close))]
correlation <- cor(fx, QQQ$QQQ.Close)
correlations <- c(correlations, correlation)
string <- paste(paste(i,j,sep='/'), correlation, sep=',')
values <- c(values,paste(string,'\n', sep=''))
}
}
# TODO eliminate NA's
values <- values[which(correlations[is.na(correlations) == F])]
correlations <- correlations[is.na(correlations) == F]
values <- values[order(correlations, decreasing=T)]
write.table(values, file=commandArgs(trailingOnly=T)[2], sep='',
qmethod=NULL, quote = F, row.names=F, col.names=F)
rm('currencies', 'correlations', 'values', 'fx', 'string')
return()
}
lagtime <- as.integer(commandArgs(trailingOnly=T)[3])
if (is.na(lagtime)) lagtime <- 1
print(paste(Sys.time(), '<--- starting', lagtime, 'day lag currencies
correlation with', commandArgs(trailingOnly=T)[1], 'from 2009-11-20 to
2011-04-03'))
currencyCorrelation(lagtime)
print(paste(Sys.time(), '<--- ended, results in',
commandArgs(trailingOnly=T)[2]))
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