Speed Tests for Rolling/Running Functions

I use rolling and running functions almost daily with financial time series. In my post A Whole New World with Chains and Pipes, I made this statement

I have noticed that rolling analysis with xts can sometimes be slow. as.matrix is my favorite way to speed things up, since I usually do not need xts powerful indexing and subsetting features.

I felt like I be a little more thorough, so I put together a couple of speed tests for running and rolling functions. Please let me know if there are ways to make these even speedier.

One method I had not seen was provided in the comments, so I promoted to the body of this post.

Hi, there is also nice benchmark (for variable window width) provided here:http://stackoverflow.com/quest...

require(microbenchmark)
require(ggplot2)

require(xts)
require(quantmod)
require(PerformanceAnalytics)

require(dplyr);require(magrittr)

require(Gmisc)  #for pretty html tables 

Random Data | matrix, data.frame, and xts with dplyr

For our tests, let's make some random data with 10 columns of 100,000 rows. If this is daily financial data, 100,000 takes us back to about 1740. I wish I had good data back to then. We'll create a matrix, data.frame, and xts object.

playData.matrix <- matrix(runif(1000000,-.03,.03), ncol=10)

playData.df <- 
  playData.matrix %>%
  as.data.frame %>% 
  tbl_df

#make some dates by adding days to the first day of 1740
#in a playful magrittr way (please don't do this at homr)
playDates <- 1:(playData.matrix %>% nrow) %>% as.Date(origin="1740-01-01")


#make an xts dataset with our playData with index playDates
playData.xts <- 
  playData.matrix %>%
  as.xts(order.by=playDates)

Running | Beauty of Fortran

The ttr authors have made the running functions really speedy with Fortran and C.

mb_runMean <- microbenchmark(
  runMean_matrix = runMean(playData.matrix, n = 50 )
  ,runMean_df = runMean( playData.df, n = 50 )
  ,runMean_xts = runMean( playData.xts, n = 50 )
  #show the beauty of Fortran in the above runMean calcs
  ,runMean_rollapply = rollapply(playData.matrix, width = 50, by = 1, FUN="mean")
,times=10L)

runMean microbenchmark results

summary	expr	min	lq	median	uq	max	neval
1	runMean_matrix	7.83479	8.328931	10.187089	15.631568	20.428914	10
2	runMean_df	415.200526	436.36685	463.7532645	504.178149	565.733628	10
3	runMean_xts	19.379056	22.689037	27.419294	31.679356	34.873025	10
4	runMean_rollapply	40530.101527	41679.322495	42675.2747235	43917.396031	47993.732396	10

Rolling | I Miss Fortran

Rolling functions get a lot slower without Fortran and C. However, we can convert with as.matrix or as.numeric to speed things up a little bit. Once we're done calculating we will need to convert back to xts with a little date logic.

mb_rollapply <- microbenchmark(
  ra_matrix = rollapply( playData.matrix[,1:2], width = 100 , by = 1, FUN="Omega" )

  ,ra_matrix2 = apply(
    playData.matrix[,1:2],
    MARGIN=2,
    FUN=function(col){ return(rollapply(col,width = 100 , by = 1, FUN="Omega" )) }
  )

  ,ra_xts = rollapply( playData.xts[,1:2], width = 100 , by = 1, FUN="Omega" )

  ,ra_xts2 = apply(
    playData.xts[,1:2],
    MARGIN=2,
    FUN=function(col){
      return(
        rollapply(
          as.numeric(col)
          ,width=100
          ,by=1
          ,FUN="Omega"
        )
      )
    }
  )
,times=2L)

rollapply microbenchmark results

summary	expr	min	lq	median	uq	max	neval
1	ra_matrix	26.001762182	26.001762182	26.8966084855	27.791454789	27.791454789	2
2	ra_matrix2	22.331454712	22.331454712	24.8568809085	27.382307105	27.382307105	2
3	ra_matrix3	25.255073874	25.255073874	26.3310039035	27.406933933	27.406933933	2
4	ra_xts	308.954120943	308.954120943	309.3609896875	309.767858432	309.767858432	2
5	ra_xts2	27.154306816	27.154306816	28.6303421355	30.106377455	30.106377455	2