Slow performance of nested loop in R

Question

I have a xts series consisting of 250 observations and 584 columns. I am running a nested for loop on this series. This nested loops takes too much time. I have tried to create a reproducible example. On actual data set nested loop is taking too much time. Please suggest some efficient ways to do the same nested loop

library(PerformanceAnalytics)
library(xts)
library(zoo)
## dataset
bsereturn<-managers
##### calculating bse_lag
bse_lag<-head(bsereturn,-1)

## calculating bse forward
bse_forward<-tail(bsereturn,-1)

## defining look back and skip period
s=12
k=1
## Empty xts to store looping results
XSMOM = bse_lag
XSMOM[1:nrow(XSMOM),1:ncol(XSMOM)] <- NA
# Compute Momentum
system.time(for (i in 1:ncol(bse_lag)){
  
  for (t in (s + 1):nrow(bse_lag)){
    XSMOM[t,i] =  Return.cumulative(bse_lag[(t-s):(t-1-k),i])
    
  }
})

Answer 1

The results in your example look weird to me. The return you have for 1997-01-31 is the cumulative return for 1996-01-31 through 1996-11-30. Why do you ignore the return from 1996-12-31?

Anyway, you use rollapply to get your answer. Your example runs ~4x faster for me than the single loop in Jerome's answer.

# this is what I would do
xsmom <- lag(rollapplyr(1 + bsereturn, 11, prod) - 1)
# this is what you have (lagged 2 periods)
xsmom2 <- lag(rollapplyr(1 + bsereturn, 11, prod) - 1, 2)