[R] (FULL) Need help to optimize a piece of code involving zoo objects

Gabor Grothendieck ggrothendieck at gmail.com
Fri Jun 19 11:38:13 CEST 2009


Check out method = "recursive" in filter().

On Fri, Jun 19, 2009 at 3:36 AM, Sergey Goriatchev<sergeyg at gmail.com> wrote:
> (Sorry, sent the message before I finished it)
> Hello, everyone
>
> I have a long script that uses zoo objects. In this script I used
> simple moving averages and these I can very efficiently calculate with
> filter() functions.
> Now, I have to use special "exponential" moving averages, and the only
> way I could write the code was with a for-loop, which makes everything
> extremely slow.
> I don't know how to optimize the code, but I need to find a solution.
> I hope someone can help me.
>
> The special moving average is calculated in the following way:
>
> EMA = ( K x ( C - P ) ) + P
>
> where,
>
> C = Current Value
> P = Previous periods EMA    (A SMA is used for the first period's calculation)
> K = Exponential smoothing constant
>
> K = 2 / ( 1 + Periods )
>
> Below is the code with the for-loop.
>
> -"temp" contains C
> -Periods is variable "j" in the for loop (so K varies)
> - I first produce a vector of simple equally weighted moving average,
> and use the first non-NA value to initiate the second for-loop
>
> x.Date <- as.Date("2003-02-01") + seq(1,1100) - 1
> temp <- zoo(rnorm(1100, 0, 10)+100, x.Date)
>
> start.time <- proc.time()
>
> for(j in seq(5,100,by=5)){
>
>        #PRODUCE FAST MOVING AVERAGE
>        #Create equally weighted MA vector (we need only the first value)
>        smafast <- zoo(coredata(filter(coredata(temp[,1]), filter=rep(1/j,
> j), sides=1)), order.by=time(temp))
>
>        #index of first non-NA value, which is the first SMA needed
>        #which(is.na(smafast))[length(which(is.na(smafast)))]+1
>
>        #Calculate decay factor K
>        #number of periods is j
>        K <- 2/(1+j)
>
>        #Calculate recursively the EMA for the fast index (starting with
> second non-NA value)
>        for (k in
> (which(is.na(smafast))[length(which(is.na(smafast)))]+2):length(smafast))
> {
>                smafast[k] <-
> coredata(smafast[k-1])+K*(coredata(temp[k,1])-coredata(smafast[k-1]))
>        }
>
>        #PRODUCE SLOW MOVING AVERAGE
>        #Create equally weighted MA vector (we need only the first value)
>        smaslow <- zoo(coredata(filter(coredata(temp[,1]),
> filter=rep(1/(j*4), (j*4)), sides=1)), order.by=time(temp))
>        K <- 2/(1+j*4)
> #Calculate EMA
>        for (k in
> (which(is.na(smaslow))[length(which(is.na(smaslow)))]+2):length(smaslow))
> {
>                smaslow[k] <-
> coredata(smaslow[k-1])+K*(coredata(temp[k,1])-coredata(smaslow[k-1]))
>        }
>
>        #COMBINE DIFFERENCES OF FAST AND SLOW
>        temp <-         merge(temp, ma=smafast-smaslow)
> }
>
> proc.time()-start.time
>
> --------------------------------------------------------------------------------------
> Could someone help me to optimize the two EMA for-loops within the
> bigger for-loop?
> I need to cut the execution time down by at least half.
>
> Thank you in advance for your help!
>
> Best,
> Sergey
>
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