Use 'diff' to determine where the changes are:
> S <- sample(0:1,30,TRUE)
> S
[1] 0 0 1 1 0 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 1 0
> which(diff(S) == -1)
[1] 4 9 13 15 18 21 23 29
>
then use the indices for the other processing.
On Thu, Apr 7, 2011 at 10:30 PM, Worik R <wor...@gmail.com> wrote:
> Friends.
>
> I cannot simplify this much, and I think the loop is unavoidable. As a
> recovering C programmer I want to avoid loops and in cases like this I
> almost allways can by using an apply function. But I suspect in this case
> there is nothing I can do.
>
> It is a finance example where a price series is compared to a moving
> average. If the price goes above the average, plus a bit, buy the
> security. If we are holding the security and the price dips below the
> moving average sell it.
>
> P is the series of prices
>
> m is the moving average series
>
> S <- P>(m*1.005)
> S[S]<-1
>
> Now S is my signal it is 1 when P > m plus a margin of 0.005 x m
>
> But now I need to control when S goes from 1 to 0. As far as I can tell
> this is the only way...
>
> for(i in 2:length(S)){
> if(S[i]==0 && S[i-1] == 1){
> ## Was long, now short. SHould I be short? Is P>m still?
> if(P[i] > m[i]){
> ## Stay long
> S[i] <- 1
> }
> }
> }
>
> As I mentioned I am a recovering C programmer, so I have a buit in loop
> reflex, but I am struggling to adapt. But this one has me beat! Can anyone
> help?
>
> cheers
> W
>
> [[alternative HTML version deleted]]
>
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>
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
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