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

[Sergey Goriatchev]

Hi, everybody

OK, I got it working with "recursive". Don't know why this argument
slipped my mind, as I use filter() so often!
Now it is 44 times faster, which is good enough for me. :-)

Thank you, Gabor and Jim.

Best,
Sergey

On Fri, Jun 19, 2009 at 15:23, jim holtman<jholtman at gmail.com> wrote:
> check out 'filter' to see if it does what you want with the 'recursive'
> option.
>
> On Fri, Jun 19, 2009 at 3:33 AM, Sergey Goriatchev <sergeyg at gmail.com>
> wrote:
>>
>> 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
>>
>> --
>> I'm not young enough to know everything. /Oscar Wilde
>> Experience is one thing you can't get for nothing. /Oscar Wilde
>> When you are finished changing, you're finished. /Benjamin Franklin
>> Tell me and I forget, teach me and I remember, involve me and I learn.
>> /Benjamin Franklin
>> Luck is where preparation meets opportunity. /George Patten
>>
>> ______________________________________________
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>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Jim Holtman
> Cincinnati, OH
> +1 513 646 9390
>
> What is the problem that you are trying to solve?
>



-- 
I'm not young enough to know everything. /Oscar Wilde
Experience is one thing you can't get for nothing. /Oscar Wilde
When you are finished changing, you're finished. /Benjamin Franklin
Tell me and I forget, teach me and I remember, involve me and I learn.
/Benjamin Franklin
Luck is where preparation meets opportunity. /George Patten