Sergey Goriatchev
2009-Jun-19 07:33 UTC
[R] Need help to optimize a piece of code involving zoo objects
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
jim holtman
2009-Jun-19 13:23 UTC
[R] Need help to optimize a piece of code involving zoo objects
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@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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html<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? [[alternative HTML version deleted]]