On 09/30/2009 09:51 AM, Abhijit Bera wrote:
Hi Romain
I got what ur saying. Good idea. First I convert the data into a
timeseries and return object using C, then I pass it onto an R function
which I have written, I call the R function from my C code, it evaluates
and then I process the values returned by it in my C code. Much simpler
that way. So, I can do away with all these confusing constructs. :)
What about performance? You think I will see a difference if I follow a
C only approach of creating all the objects?
Essentially you are not using a C only approach, even in your original
example, since you call functions from fPortfolio (btw, R has to look
down the search path each time to find the function, so you can make
this more efficient, ...)
It might even be faster like this since you only need to parse the R
functions once, so you pay the (not so expensive btw) price of parsing
once.
Combine this with an approach that caches the references to the
functions, so you only pay the price of searching through R search path
once, and you're set.
Anyway, chances are the code from fPorfolio you call will be what costs
the most, after all that is what is actually working here. Before
converting your R script to C, did you do some profiling to identify
what takes the most time ?
Romain
Regards
Abhijit
On Wed, Sep 30, 2009 at 12:45 PM, Abhijit Bera <abhib...@gmail.com
<mailto:abhib...@gmail.com>> wrote:
Hi
I'm pulling financial datasets from a DB, converting it to a
timeseries object then creating a returns object out of it.
I plan to embed R into an application, which is why I'm taking this
route of using C.
Regards
Abhijit
On Wed, Sep 30, 2009 at 12:07 PM, Romain Francois
<romain.franc...@dbmail.com <mailto:romain.franc...@dbmail.com>> wrote:
On 09/30/2009 08:51 AM, Abhijit Bera wrote:
Hi
Thanks all of you for your suggestions. I will put up my
code shortly based
on your suggestions.
I wonder how the parsing and eval will work when most of my
data comes in
from an external source like a DB? Probably it would be
more efficient to
make an object? Hmmmm... maybe it has to be a mix of parsing
and eval?
What's in the database ? Is this the data or the R code ? What's
wrong with writing your own set of R functions and evaluate
calls to these functions instead of basically replicate this in
C or C++ or whatever.
Dirk's code certainly is nicer, but would you really do it like
that in real life ?
Romain
Yes, the lang4 c idea sucks. mkstring is better.
Regards
Abhijit
On Tue, Sep 29, 2009 at 11:55 PM, Dirk
Eddelbuettel<e...@debian.org <mailto:e...@debian.org>> wrote:
This is so much fun. The C code posted wasn't exactly
legible. So here is
a
new C++ variant that I just committed to the RInside SVN
as a new example.
And it mine works (against RInide and Rcpp as on CRAN):
e...@ron:~/svn/rinside/pkg/inst/examples> ./rinside_sample4
Package 'sn', 0.4-12 (2009-03-21). Type 'help(SN)' for
summary information
Using the GLPK callable library version 4.37
Title:
MV Feasible Portfolio
Estimator: covEstimator
Solver: solveRquadprog
Optimize: minRisk
Constraints: LongOnly
Portfolio Weights:
SBI SPI SII LMI MPI ALT
0.1 0.1 0.1 0.1 0.3 0.3
Covariance Risk Budgets:
SBI SPI SII LMI MPI ALT
-0.0038 0.1423 0.0125 -0.0058 0.4862 0.3686
Target Return and Risks:
mean mu Cov Sigma CVaR VaR
0.0548 0.0548 0.4371 0.4371 1.0751 0.6609
Description:
Tue Sep 29 13:43:36 2009 by user:
SBI -0.00380065
SPI 0.142261
SII 0.0125242
LMI -0.00576251
MPI 0.486228
ALT 0.368551
e...@ron:~/svn/rinside/pkg/inst/examples>
The final few lines are C++ accessing the result,
earlier in the code I
assign the weight vector from C++ as you desired from C.
All with error
checking / exception handling and what have in under 60
lines of (IMHO more
readable) code -- see below.
Dirk
// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4;
tab-width: 8; -*-
//
// Another simple example inspired by an r-devel mail by
Abhijit Bera
//
// Copyright (C) 2009 Dirk Eddelbuettel and GPL'ed
#include "RInside.h" // for the
embedded R via RInside
#include "Rcpp.h" // for the R /
Cpp interface used
for transfer
#include<iomanip>
int main(int argc, char *argv[]) {
try {
RInside R(argc, argv); // create an
embedded R instance
SEXP ans;
std::string txt =
"suppressMessages(library(fPortfolio))";
if (R.parseEvalQ(txt)) // load library,
no return value
throw std::runtime_error("R cannot evaluate
'" + txt + "'");
txt = "lppData<- 100 * LPP2005.RET[, 1:6]; "
"ewSpec<- portfolioSpec(); "
"nAssets<- ncol(lppData); ";
if (R.parseEval(txt, ans)) // prepare problem
throw std::runtime_error("R cannot evaluate
'" + txt + "'");
const double dvec[6] = { 0.1, 0.1, 0.1, 0.1,
0.3, 0.3 }; // choose
any weights you want
const std::vector<double> w(dvec,&dvec[6]);
R.assign( w, "weightsvec"); // assign STL
vector to R's
'weightsvec' variable
txt = "setWeights(ewSpec)<- weightsvec";
if (R.parseEvalQ(txt)) // evaluate
assignment
throw std::runtime_error("R cannot evaluate
'" + txt + "'");
txt = "ewPortfolio<- feasiblePortfolio(data =
lppData, spec =
ewSpec, constraints = \"LongOnly\"); "
"print(ewPortfolio); "
"vec<- getCovRiskBudgets(ewportfo...@portfolio)";
if (R.parseEval(txt, ans)) // assign
covRiskBudget weights to
ans
throw std::runtime_error("R cannot evaluate
'" + txt + "'");
RcppVector<double> V(ans); // convert SEXP
variable to an
RcppMatrix
R.parseEval("names(vec)", ans); // assign
columns names to ans
RcppStringVector names(ans);
for (int i=0; i<names.size(); i++) {
std::cout<< std::setw(16)<< names(i)<< "\t"
<< std::setw(11)<< V(i)<< "\n";
}
} catch(std::exception& ex) {
std::cerr<< "Exception caught: "<< ex.what()<<
std::endl;
} catch(...) {
std::cerr<< "Unknown exception caught"<< std::endl;
}
exit(0);
}
--
Romain Francois
Professional R Enthusiast
+33(0) 6 28 91 30 30
http://romainfrancois.blog.free.fr
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