Thanks Joshua,
I really like the example given in the blog post that Abhijit pointed me to.
Doing it in C++ using the Inline seems like an easy way to get a massive
improvement in speed without the hassle of writing a package.
I'm working on coding that now.
--
Noah Silverman
UCLA Department of Statistics
8117 Math Sciences Building #8208
Los Angeles, CA 90095
On Sep 2, 2011, at 10:32 AM, Joshua Ulrich wrote:
> On Fri, Sep 2, 2011 at 12:06 PM, Noah Silverman <noahsilver...@ucla.edu>
> wrote:
>> 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?
>>
> As I said, the function is in C. You could also use the compiler
> package to compile your pure R function for a 3-4x speedup.
>
> Best,
> --
> Joshua Ulrich | FOSS Trading: www.fosstrading.com
>
>
>>
>> --
>> 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
>>>>>
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>>>
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>>>>>
>>>>
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>>
>>
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