On 01/25/2011 10:01 AM, Andres Susrud wrote:
The bottlenecks is when running backtests for larger datasets.I'm also
looking at some other functions that are updated for every new timestep.
We run backtests in R on complex indicators on tick data where the
indicator updates every tick. These complete backtests, including the
path dependent trade generation rules run (for us) in seconds to a few
minutes per day of tick data per instrument. All our code is R (mostly)
or C/C++ (either implied, like with TTR, or specific to our proprietary
indicators).
for(i in 1:length(dataset)){
function(dataset[1:i])}
This is a known performance bottleneck in R, and there is quite a lot of
literature about either reworking this to a vectorized formulation, or
moving such calculations which *must* be looped to C, Fortran, Java, or
C++ (for which good integration options already exist in R).
When generating BM's also for comparisons, I also find the speed in R a bit
slow, and that's why I'm looking for the bridge that gives more speed.
R-help would be a better place to see if people are doing Lisp in R in
any repeatable, scalable, manner.
Regards,
- Brian
--
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock
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