http://quantlib.414.s1.nabble.com/Reg-Contributing-tp13540p13548.html
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.
On 29 Jul 2011 16:30, "Amir Ahmed Ansari" <
[hidden email]> 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 <
[hidden email]>
> To: Kim Kuen Tang <
[hidden email]>
> Cc:
[hidden email];
[hidden email]
> 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" <
[hidden email]> wrote:
>>>>>
>>>>>
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