rolling computations in xts by month

Question

I am familiar with the zoo function rollapply which allows you to do rolling computations on zoo or xts objects and you c

Answer 1

If I understand correctly, you can get the dates of your endpoints, then for each endpoint (i.e. using lapply or for), call rollapply using data up to that point.

getSymbols("SPY", src='yahoo', from='2012-01-01', to='2012-08-01')
idx <- index(SPY)[endpoints(SPY, 'months')]
out <- lapply(idx, function(i) {
  as.xts(rollapplyr(as.zoo(SPY[paste0("/", i)]), 5, 
                    function(x) coef(lm(x[, 4] ~ x[, 1]))[2], by.column=FALSE))
})
sapply(out, NROW)
#[1]  16  36  58  78 100 121 142 143

I temporarily coerce to zoo for the rollapplyr to make sure the rollapply.zoo method is being used (as opposed to the unexported rollapply.xts method), then coerce back to xts

Answer 2

You want period.apply(), or its convenience helper apply.monthly(), both in xts.

Example:

R> foo <- xts(1:100, order.by=Sys.Date()+0:99)
R> apply.monthly(foo, sum)
           [,1]
2012-08-31  105
2012-09-30  885
2012-10-31 1860
2012-11-25 2200
R> 

or equally

R> apply.monthly(foo, quantile)
           0%   25%  50%   75% 100%
2012-08-31  1  4.25  7.5 10.75   14
2012-09-30 15 22.25 29.5 36.75   44
2012-10-31 45 52.50 60.0 67.50   75
2012-11-25 76 82.00 88.0 94.00  100
R> 

just to prove that functions returning more than one value can be used too.

Answer 3

As an answer to "Is the zoo/xts conversion needed?": It isn't needed in this case, but rollapply won't work if you send it a dataframe, as I recently discovered from this StackOverflow answer