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While ago I built two R scripts to track OMX Baltic Benchmark Fund against the index. One script returns the deviation of fund from the index and it works fast enough. The second calculates the value of the fund every minute and it used to take for while. For example, it spent 2 minutes or more to get the values for one day. Here is an example of the result:
Following piece of code was in question:
for(y in 1:NROW(x))
{
z=x[y,]
if(as.numeric(z$last_price>0))
{
if(as.numeric(z$bid>z$last_price))rez[y]=z$bid
else if(as.numeric(z$ask>0 & z$ask<z$last_price))rez[y]=z$ask
else rez[y]=z$last_price
}
else
{
rez[y]=(z$ask+z$bid)/2
}
} |
The code above loops over time series and based on set of rules tries to decide which price (bid, ask or previous one) to use for calculations. Pure R script used to take 100 seconds to derive the price.
During the weekend I found time to watch very interesting Rcpp presentation. To my surprise, there are numerous ways to seamlessly integrate C++ into R code. So, I decided to rewrite the code above in C++ (Rcpp and inline packages were used).
#c++ code embed in code value
code='
NumericVector bid(bid_);NumericVector ask(ask_);NumericVector close(close_);NumericVector ret(ask_);
int bid_size = bid.size();
for(int i =0;i<bid_size;i++)
{
if(close[i]>0)
{
if(bid[i]>close[i])
{
ret[i] = bid[i];
}
else if(ask[i]>0 && ask[i]<close[i])
{
ret[i] = ask[i];//
}
else
{
ret[i] = close[i];//
}
}
else
{
ret[i]=(bid[i]+ask[i])/2;
}
}
return ret;
'
#a glue function between C++ and R
getLastPrice = cxxfunction(signature( bid_ = "numeric",ask_ = "numeric",close_="numeric"),body=code,plugin="Rcpp")
#and the call of the function
getLastPrice(as.numeric(x$bid),as.numeric(x$ask),as.numeric(x$last_price)) |
What did I get in return? Well, 0.1 of a second instead of 100 seconds!
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