Re: [Quantlib-dev] Reg. Contributing

[urun d. <uru...@gm...>]

These are very good points. Actually recently I heard many machine learning
researchers who were hired from finance industry. I do not know what are
they doing in jobs. Although I know that in algoritmic trading people use
some machine learning techniques I have also similar serious concerns that
are pointed in the previous e-mail. As said before it would be really great
if some practitoners share their experience and opinions.

Best regards
Urun
On 29 Jul 2011 16:30, "Amir Ahmed Ansari" <phd...@ya...> wrote:
> I doubt people would be willing to leave the trading of billions of
dollars to a neural network/SVM whose inputs and outputs they don't fully
understand. Then again, the way people in the investment management industry
blindly rely on tools would suggest people can do anything :) It would be
great to get some insight from an actual practitioner.
>
>
> From: urun dogan <uru...@gm...>
> To: Kim Kuen Tang <kue...@vo...>
> Cc: qua...@li...; lui...@gm...
> Sent: Friday, July 29, 2011 6:57 PM
> Subject: Re: [Quantlib-dev] Reg. Contributing
>
>
> Dear All;
>
>
>
>>
>>>
>>>
>>>Hi Kim,
>>>
>>>
>>>>
>>>>Hi urun,
>>>>
>>>>Am 20.07.2011 22:29, schrieb urun dogan:
>>>>Hi Luigi,
>>>>>I am a post-doc doctoral researcher in Germany. My
> main researc focus is machine learning/artificial
> intelligence techniques. I am a develor of Shark
> machine learning library.
> Just my 2 cent.
>>>>Since you are involved in the development of the shark
> machine you definitely know how to use machine learning to
> classify and to predict observations. If this is the case
> you should try to get involved into designing and
> implementing (alpha-) strategies for trading and
> backtesting.
>>>>
>>>
>>>I have significant amount of knowledge on classification,
> regression and model selection techniques. I made a small
> research on " (alpha-) strategies for trading and backtesting"
> . They are quite interesting. Is there a group implementin
> these techniques in quant-lib?
> Difficult to say. As far as i know there is nobody planing to implement a
backtesting framework in quantlib.
>>
>>If yes where can I found contact details of members?
>>If no, should I start by my own?
>>>
> Hmmm, i remember seeing a webinar from mathwork showing how to use a
neural network for risk controlling energy trading.
>>Perhaps you can try to implement a neural network in ql and use this
> tool to forecast energy prices.
>>Just a suggestion.
>>
>
> I have implemented several feedforward neural network methods, recurrent
neural network methods, support vector machines and so on. I think this line
of implmentation can be beneficial to some people.
> I am reading some documents/papers about algorithmic trading and I think
these methods can be also used in algorithmic trading. To put such kind of
methods in quantlib is very interesting for me.
> I have three questions. First one is: Where can I find some data like
energy trading data or finance data? The second one is ehich methods are the
state of art nethods for this kind of tasks? The third one is
> which methods have more priority than others? I mean what are the industry
requirement? Does quantitative developers/analysers/traders need very fast
and accurate methods, e.g. Neural networks, conventional
> linear regression methods or accurate but slow methods e.g SVMs, Gaussian
Process?
>
> Best regards
> Urun
>
>
>
>>>>
>>>>
>>>>
>>>>This library is implemented by C++ . I am really interested in
contributing to quant lib because I find find finsnce chalenging and
interesting. I am open to implement some machine learning techniques to
quant lib also it is absolutelly ok for me to implement other things. Is
there any todo list? Are there any ideas for starting?
>>>>>Thanks a lot for your help.
>>>>>Best regards
>>>>>Ueruen Dogan
>>>>>On 18 Jul 2011 13:40, "Luigi Ballabio" <lui...@gm...>
wrote:
>>>>>
>>>>>
>
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>>>>
>>>
>>
>
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