[R-SIG-Finance] random portfolios

[frednovo at pipeline.com]

Please look at my package rportfolios.  It allows you to construct individual and samples of random portfolios that satisfy a wide range of constraints ( e.g., long only, short only, long short portfolios with notional exposure constraints, bounded weights).

Regards,

Frederick Novomestky

-----Original Message-----
>From: Kevin Dhingra <kevin.dhingra at appliedacademics.com>
>Sent: Mar 20, 2017 6:28 PM
>To: Ross Bennett <rossbennett34 at gmail.com>
>Cc: "r-sig-finance at r-project.org" <r-sig-finance at r-project.org>
>Subject: Re: [R-SIG-Finance] random portfolios
>
>Hi Ross,
>
>Sure. Even though I have not profiled the bottlenecks quite in detail as of
>yet, i will give you a decent idea of the problem I am working with. I can
>have multiple indices with as much as 2000 assets with group, position and
>turnover limits (Not sure if i can increase the speed by removing
>constraints and doing rejection sampling later). In order to generate a
>daily possible set for the market in this case, I was playing around with
>~4-5 thousand permutations. Also I think I will end up using the "sample"
>method because of the type of constraints we have and as you already have
>mentioned that method is the slowest (takes about 30 times the time using
>"simplex" for the same constraints). Adding box and position limit
>constraints are causing it to run a bit slower (but its not a big
>difference). I can always provide a more thorough analysis of the potential
>bottlenecks with a lot more detail when I have a chance to start working on
>translating it to cpp
>
>Thank you,
>
>On Mon, Mar 20, 2017 at 4:04 PM, Ross Bennett <rossbennett34 at gmail.com>
>wrote:
>
>> Kevin,
>>
>> Can you give us a sense of the number of assets in the portfolio and
>> the constraints? That will help us understand where the potential
>> bottlenecks are in the random portfolio generation. For example,
>> generating a set of random portfolios for box and weight constraints
>> if relatively fast, but adding group or position limit constraints
>> makes the algorithm more complicated and slower.
>>
>> Thanks,
>> Ross
>>
>>
>> On Mon, Mar 20, 2017 at 2:35 PM, Kevin Dhingra
>> <kevin.dhingra at appliedacademics.com> wrote:
>> > Brian,
>> >
>> > Thank you for a quick reply. I will soon be working on that problem and
>> > from what I have played with so far, it is unlikely that for our example
>> > ~2k portfolios will be enough (really hoping it would) to get a good
>> sense
>> > of the feasible space and seems like I need to implement an Rcpp version
>> of
>> > the random portfolios function. I will be happy to collaborate and share
>> my
>> > code once i get a decent handle on it locally for the purposes of our
>> > current project.
>> >
>> > Regards,
>> > Kshitij Dhingra
>> >
>> >
>> >
>> > On Mon, Mar 20, 2017 at 3:17 PM, Brian G. Peterson <brian at braverock.com>
>> > wrote:
>> >
>> >> On Mon, 2017-03-20 at 15:09 -0400, Kevin Dhingra wrote:
>> >> > I have been using the random_portfolios function from the
>> >> > `PortfolioAnalytics` package to simulate the range of possibilities
>> >> > for return paths at each step under various portfolio constraints /
>> >> > mandates for evaluating mutual fund managers. As more managers are
>> >> > added to the universe, however, and more simulations are needed, the
>> >> > pure R implementations get pretty heavy and hard to scale. I was
>> >> > wondering if there has been any work out there thus far on
>> >> > implementing any of the three random portfolio generation methods
>> >> > (sample, simplex, and grid search) at a lower level, using something
>> >> > like `Rcpp` to enhance the efficiency of these algorithms?
>> >>
>> >> We've discussed it, but I can't say that it is terribly high on our
>> >> list of priorities.
>> >>
>> >> In most cases, no more than 1-2k portfolios should be required to get a
>> >> fair view of the feasible space given your constraints and objectives.
>> >>
>> >> We'd be happy to work with you if you want to craft a patch to use C or
>> >> Rcpp for this.
>> >>
>> >> Regards,
>> >>
>> >> Brian
>> >>
>> >
>> >
>> >
>> > --
>> > Kshitij Dhingra
>> > Applied Academics LLC
>> > Office: +1.917.262.0516
>> > Mobile: +1.206.696.5945
>> > Email: kshitij.dhingra at appliedacademics.com
>> > Website: http://www.AppliedAcademics.com
>> >
>> >         [[alternative HTML version deleted]]
>> >
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>
>
>
>-- 
>Kshitij Dhingra
>Applied Academics LLC
>Office: +1.917.262.0516
>Mobile: +1.206.696.5945
>Email: kshitij.dhingra at appliedacademics.com
>Website: http://www.AppliedAcademics.com
>
>	[[alternative HTML version deleted]]
>
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