The easiest probably would be to use the multicore package (linux) on
one machine, but if you're feeling ambitious, there's also the
possibility of using doSNOW, but there's some small idosyncracies that
will leave you (or at least it did for me) pulling your hair out trying
to figure out why certain things aren't working.
If you're on Windows only, another single box solution would be the
"doSMP" and "foreach" packages that were released by Revolution into CRAN.
here's a short example of how I use it on Windows (I have a more
complicated multiple computer script buried somewhere using doSNOW on
linux32):
require(doSMP)
require(foreach)
clust <- startWorkers(4)
registerDoSMP(clust)
symbols = c("SPX","DIA","QQQQ")
# the function that you want to parallelize, gets exported to each
"node" -- could insert your backtest code here
parallel.arima <- function(data) {
library(forecast)
library(quantmod)
tmp = get(data)
fit = auto.arima(ts(Cl(tmp)), approximation=TRUE, allowdrift=TRUE,
stepwise=TRUE)
}
res <- foreach(dat=symbols, .export=symbols) %dopar% parallel.arima(dat))
There's more info on the r-sig-hpc list regarding some of the finer
details of the packages mentioned above. Standard disclaimer, this
probably isn't the "best" way to do it but it should give you some idea
of where to start.
HTH,
C
On 06/24/2011 07:00 AM, benjamin sigel wrote:
Hi,
I would like to run multiple backtests with R on intraday data, using
"quantstrat" and "backtest package" and I was wondering what would be the
most time efficient hardware solution between these two:
- 1 PC: *1 Quad-Core* (Intel® Core™ i5-2300, 2.8 GHz (up to 3.1 GHz with
Turbo Boost) /6GB installed DDR3 RAM (1066 MHz) + *16GB maximum RAM capacity
*
OR
- *2 PC's Hooked-up:* 2 Dual-core (Intel® Core™ i3-550 Processor, 3.20 GHz,
4 MB Smart Cache, 4GB DDR3 + *maximum expandable memory 16GB* *each*
Many Thanks for your help,
Ben
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