[GSoC-PortA] Random Portfolios Speed Improvement with Rcpp

[Brian G. Peterson]

Ross, this is a very interesting prototype.

We haven't shied away from adding C dependencies in PerformanceAnalytics 
or blotter or quantstrat.  All of them have recently acquired compiled code.

The first part of your benchmark is a fair one, generating the random 
portfolios, and one that I would expect compiled code to do better than 
native R code (though we haven't spent any time profiling or trying to 
improve the native R code either) because it is a big loop.

The second part of your benchmark is rather unfair though, as you say 
yourself:

"Benchmark the optimization functions of PortfolioAnalytics and RcppRP.
The rp_optimize_v2 uses slimmed down C++ implementations of 
constrained_objective and optimize.portfolio from PortfolioAnalytics. 
The objective, constrained objective, and optimization functions
must all be in C++ so that I can ”stay in C++ world” for the
optimization when calling constrained_objective for each set of weights."

The entire point of constrained_objective is that the objective from the 
portfolio specification is of arbitrary complexity, and will typically 
include much more complex functions than 'mean' and 'sd'.

So, keeping everything in C++ world isn't really possible with arbitrary 
objectives, by construction, because the objectives can be *any R function*.

That said, where would generally useful improvements (in handling 
arbitrary objectives) likely lie?

* handling the loop over all the random weight vectors in compiled code:

This isn't likely to be a huge performance improvement with arbitrary 
objectives, as the time spent in the loop is likely dwarfed by the 
objective function itself.

* improved handling of arguments and argument matching

This one could be huge, but also doesn't require compiled code.  Josh 
Ulrich recently came up with huge speed improvements in quantstrat  in 
part by improving the argument matching and calling of arbitrary 
functions.  The prototype of that code in quantstrat came from 
PortfolioAnalytics.  The key improvement was in not evaluating large 
arguments. In this case, that would be the returns time series and the 
moments and co-moments.  This trick could and probably should be ported 
to PortfolioAnalytics.

We saw Dirk try to create a faster C++ version of DEoptim a few years 
ago.  His RcppDEoptim didn't pass dots (...) to the objective function. 
  Oops.  The entire performance gain came from this lack of ability to 
use an arbitrary objective.  When dots were added back in, the C++ 
version of DE is slower than the C version (as you'd expect). Passing 
dots to the objective isn't exactly optional outside of toy examples.

* general improvements in optimize.portfolio

Not clear without profiling, but i wouldn't expect this to be more than 
a fraction of the runtime with real objectives.

* general improvements in constrained_objective

There is almost certainly a role for compiled code in 
constrained_objective.  This is a very large, complex function that 
could definitely be improved.  The core functionality of calling 
arbitrary objectives as specified by the user can't be given up. though, 
or we lose the reason to allow an arbitrary portfolio specification in 
the first place.  Obviously, both C and C++ can call back to R code from 
compiled code.  We do this already in DEoptim to call the objective 
(constrained_objective in PortA) from DEoptim's C code.

Are optimize.portfolio and constrained_objective complex enough that a 
dependency on Rcpp would be worth it?  Quite possibly.  C code makes a 
lot of sense when the code can be kept compact and the overhead of 
defining objects in C isn't too great.  C++ or Rcpp makes sense when the 
complexity of the functions increases and the code would be more legible 
and maintainable in C++ than C.  Is constrained_objective complex enough 
to benefit from C++.  Maybe.

I think we'd need to be fair and ask where the performance gains come 
from and how we could gain a generic, generally useful improvement in 
PortfolioAnalytics.

If it makes sense, improving optimize.portfolio and 
constrained_objective would improve the performance of 
PortfolioAnalytics for all solvers, not just random portfolios.  That 
gain would need to come from benefits realizable even with an 
arbitrarily complex portfolio specification.

Regards,

Brian


On 11/12/2013 10:43 PM, Ross Bennett wrote:
> All,
>
> Over the course of the google summer of code project, I learned a lot
> about the random portfolios algorithm (among many other topics) and
> became quite fascinated with the concept. I had some free time over the
> weekend and decided to implement random portfolio optimization using
> Rcpp. My motivation for doing this was to learn C++ and Rcpp with no
> expectation of how much faster this could actually be.
>
> Here are the results of two benchmarks I did.
>
> This first benchmark is just generating random portfolios.
>
>      test replications elapsed relative
> 1     pa             10 188.74     6.583
> 2 rcpp_s           10   28.67    1.000
>
> The next benchmark is the actual optimization.
>
>    test replications elapsed relative
> 1   pa            10  211.027    808.5
> 2 rcpp           10      0.261    1.000
>
> I am a beginner at C++ so I am pretty sure there are further
> improvements that can be made with my C++ code.
>
> The benchmark results got me thinking that we might be able to use this
> in PortfolioAnalytics. The RcppRP package I started this weekend is
> really rough around the edges, but with some more improvements could
> serve as an alternate optimization method for random portfolios. We
> could have something like optimize.portfolio(...,
> optimize_method="random_rcpp") that calls the proper functions from RcppRP.
>
> The RcppRP package is on my github page if you are interested in looking
> at the code.
> https://github.com/rossb34/RcppRP
>
> Any thoughts on if this is worth continuing to pursue? Either way I plan
> to continue working on RcppRP for the sole purpose of learning C++ and Rcpp.
>
> Regards,
> Ross
>
>
>
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-- 
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock