This might be of interest: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1584905 Regards Dave
On 01/23/2011 11:23 PM, Ulrich Staudinger wrote: > Lui, > > you can also have a look at > http://aqr.activequant.org/index.php/2010/08/genetically-optimizing-a-trading-system/for > inspiration on how to genetically optimize a trading system, whether > it's quadratic or not. It's just some snipplet but exemplifies it pretty > well ... > > Regards, > Ulrich > > > On Sun, Jan 23, 2011 at 11:15 PM, Guillaume Yziquel < > [email protected]> wrote: > >> Le Sunday 23 Jan 2011 à 15:56:40 (-0600), Brian G. Peterson a écrit : >>> On Saturday, January 22, 2011 04:33:09 pm Lui ## wrote: >>>> Dear group, >>>> >>>> I was just wondering whether some of you have some experience with the >>>> package "rgenoud" which does provide genetic algorithms for complex >>>> optimization problems. >>> <...> >>> >>>> What is your general experience? Did you ever try solving the >>>> Markowitz portfolio with the rgenoud package? >>>> I know that there are good solvers around for the qudratic programming >>>> problem of the markowitz portfolio, but I want to go into a different >>>> direction which translates into a quadratic problem with quadratic >>>> constraints (and I havent found a good solver for that...). >>>> >>>> I am interested in your replies! Have a good weekend! >>> As others have already said, for a quadtratic problem with quadratic >>> constraints, there is an exact analytical solution. >> I wouldn't qualify dual interior point methods as an "exact" solution, but, >> yes, that's the basic idea: they're better suited for that. >> >>> In these cases, you will be much better off both from a performance and >>> accuracy perspective in using a quadratic solver (quadprog is most often >>> applied in R, see list archives and many packages for examples). >> Is quadprog a second-order cone programming solver? If that is the case, >> yes, it probably solves quadratic objective function with quadratic >> constraints faster and with more accuracy than a full-fledged SDP >> solver. >> >>> Other portfolio problems may be stated in terms of linear solvers, which >> will >>> likewise be faster than a global optimizer for finding an exact >> analytical >>> solution. >>> >>> If, however, your portfolio problem is non-convex and non-smooth, then a >>> genetic algorithm, a migration algorithm, grid search, or random >> portfolios >>> may be a good option for finding a near-optimal portfolio. If this is >> your >>> true goal, perhaps you can say a little more about your actual >> constraints and >>> objectives (and use assets that are outside of your true area of >> interest, >>> such as the S&P sector indices). >> Yes, the problem structure often gives good insight as to which method >> to apply. It may be noted, however, that quite a lot of non-convex >> problems may be transformed into convex ones. And using some relaxation >> methods, you can often use SDPs to optimise multivariate polynomial >> objective under multivariate polynomial constraints, without too many >> convexity hypothesis. >> >> SDPs are not always easy to manipulate, but they do solve a broad range >> of optimisation problems. >> >>> Regards, >>> >>> - Brian >> Best regards, >> >> -- >> Guillaume Yziquel >> http://yziquel.homelinux.org >> >> _______________________________________________ >> [email protected] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-sig-finance >> -- Subscriber-posting only. If you want to post, subscribe first. >> -- Also note that this is not the r-help list where general R questions >> should go. >> > > _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
