Let me add my two cents. Old Max Dama blog (mirror):

http://smartdatacollective.com/maxdama/22571/voodoo-spectrum-machine-learning-and-data-sets

Optimization is good to examine the sensitivity of the model and the
selection of appropriate parameters - this is useful. But playing with the
evolutionary strategy, what you Michael ask, is very risky.

regards,
Daniel



2012/3/8 Patrick Burns <patrick@burns-stat.com>

> Comments inline.
>
>
> On 08/03/2012 18:16, Michael wrote:
>
>> Thanks folks!
>>
>> After digging further on the Internet, I have the following questions:
>>
>> Q1: I read the following article:
>>
>> http://cran.r-project.org/web/**packages/DEoptim/vignettes/**
>> DEoptimPortfolioOptimization.**pdf<http://cran.r-project.org/web/packages/DEoptim/vignettes/DEoptimPortfolioOptimization.pdf>
>>
>> It seems that there are a bunch of parameters in this optimizer and the
>> results are sensitive to these parameters.
>>
>> So there is another layer of optimization with respect to these optimizer
>> parameters.
>>
>> Is the "tweaking" of these optimizer parameters data-mining, which will
>> lead to data-snooping bias?
>>
>
> I wouldn't think so, but there might be
> a way to manage it.
>
>
>
>> Q2: Due to the random nature of the optimizer, each time you run the
>> backtest, you will have different performance.
>>
>> What do you do in that case?
>>
>
> That may be a good thing, if you are
> willing to use it.
>
> In what I've done on backtesting:
>
> http://www.portfolioprobe.com/**2010/11/05/backtesting-almost-**wordless/<http://www.portfolioprobe.com/2010/11/05/backtesting-almost-wordless/>
>
> I show how to assess whether the strategy
> is better than luck by using random trades.
>
> The standard thing to assume (as I do in
> that piece) is that the optimization is
> noiseless.  But really the optimization
> depends on a multitude of subtle influences.
> Even if you always got the exact global
> optimum, if a variance or expected return
> were slightly different, you could get a
> very different path.  The "optimal" path
> is fuzzy in actuality.
>
>
>
>> So for out-of-sample real-trading, we are trading a random strategy?
>>
>
> Yes.  But the inputs are random so even
> non-stochastic optimizers give you a
> random strategy in a sense.
>
>
>
>> Q3: It's pretty easy to understand using Genetic Algorithms to serve as a
>> replacement for regular optimizers;
>>
>> but using Genetic Algorithms to evolve trading strategies seem to be
>> different. Anywhere we could find such an example in R?
>>
>
> Yes, that is different.
>
> In https://stat.ethz.ch/**pipermail/r-sig-finance/**2010q4/007033.html<https://stat.ethz.ch/pipermail/r-sig-finance/2010q4/007033.html>
> you can find Josh quoting me quoting Lao-Tzu
> on why you are unlikely to find much useful
> on that subject.
>
> Pat
>
>
>
>>
>>
>>
>> On Thu, Mar 8, 2012 at 8:25 AM, Zachary Mayer<zach.mayer@gmail.com>
>>  wrote:
>>
>>  There is the DEoptim<http://cran.r-project.**org/web/packages/DEoptim/**
>>> index.html <http://cran.r-project.org/web/packages/DEoptim/index.html>>library
>>> in r, which is an excellent library for differential evolution.  If
>>> you can define your trading strategy in terms of a bunch of parameters to
>>> adjust and an objective function (i.e. turn it into an optimization
>>> problem), DEoptim will help you find the minimum (or maximum).
>>>
>>> DEoptim works well on non-differentiable problems with many local minima.
>>>  Here is an example of using it to solve a portfolio optimization
>>> problem:
>>>
>>> http://cran.r-project.org/web/**packages/DEoptim/vignettes/**
>>> DEoptimPortfolioOptimization.**pdf<http://cran.r-project.org/web/packages/DEoptim/vignettes/DEoptimPortfolioOptimization.pdf>
>>>
>>>
>>>
>>> On Thu, Mar 8, 2012 at 12:43 AM, Sofian Hadiwijaya<reztinpeace@gmail.**
>>> com <reztinpeace@gmail.com>>wrote:
>>>
>>>  how about quantmod library..
>>>>
>>>> On Wed, Mar 7, 2012 at 10:30 PM, Michael<comtech.usa@gmail.com>  wrote:
>>>>
>>>>  Hi all, Good morning, good afternoon and good evening!
>>>>>
>>>>> Could anybody please kindly point me to resources in R which shows
>>>>> about
>>>>> how to use Genetic algorithm to evolve trading strategies?
>>>>>
>>>>> I did a lot search on Google these days and certainly it's a
>>>>>
>>>> well-covered
>>>>
>>>>> and popular topic, but I don't see anywhere in R...
>>>>>
>>>>> Thanks a lot!
>>>>>
>>>>>        [[alternative HTML version deleted]]
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>>>>> should go.
>>>>>
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>>>
>>>
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> --
> Patrick Burns
> patrick@burns-stat.com
> http://www.burns-stat.com
> http://www.portfolioprobe.com/**blog <http://www.portfolioprobe.com/blog>
> twitter: @portfolioprobe
>
>
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