Joshua,
Thanks for the tip.
I need to "roll my own" code on this. But perhaps I can borrow some code from
the package you mentioned.
Is the package just performing the loop, but in a faster language?
--
Noah Silverman
UCLA Department of Statistics
8117 Math Sciences Building #8208
Los Angeles, CA 90095
On Sep 2, 2011, at 9:58 AM, Joshua Ulrich wrote:
> On Fri, Sep 2, 2011 at 11:47 AM, R. Michael Weylandt
> <michael.weyla...@gmail.com> wrote:
>> Have you looked at SMA/EMA from the TTR package? That's a pretty quick
>> implementation.
>>
>> runmean from caTools is even better for the SMA but I don't think there's an
>> easy way to turn that into an EWMA.
>>
> SMA still calls Fortran code, so that's why it's slower than
> caTools::runmean. I've moved the EMA code to C, so it's about as fast
> as it can be.
>
> Noah, use EMA's ratio argument to replicate your for loop.
>
>> Hope this helps,
>>
>> Michael Weylandt
>>
>
> Best,
> --
> Joshua Ulrich | FOSS Trading: www.fosstrading.com
>
>
>
>> On Fri, Sep 2, 2011 at 12:43 PM, Noah Silverman
>> <noahsilver...@ucla.edu>wrote:
>>
>>> Hello,
>>>
>>> I need to calculate a moving average and an exponentially weighted moving
>>> average over a fairly large data set (500K rows).
>>>
>>> Doing this in a for loop works nicely, but is slow.
>>>
>>> ewma <- data$col[1]
>>> N <- dim(data)[1]
>>> for(i in 2:N){
>>> data$ewma <- alpha * data$ewma[i-1] + (1-alpha) * data$value[i]
>>> }
>>>
>>>
>>> Since the moving average "accumulates" as we move through the data, I'm not
>>> sure on the best/fastest way to do this.
>>>
>>> Does anyone have any suggestions on how to avoid a loop doing this?
>>>
>>>
>>>
>>>
>>> --
>>> Noah Silverman
>>> UCLA Department of Statistics
>>> 8117 Math Sciences Building #8208
>>> Los Angeles, CA 90095
>>>
>>>
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>>>
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>>>
>>
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>>
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