Posted by Amir Ahmed Ansari-2 on Jul 29, 2011; 2:30pm URL: http://quantlib.414.s1.nabble.com/Reg-Contributing-tp13540p13547.html
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.
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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