The purpouse of this thread would be to verify if the deep belief nets trained for Restricted Boltzmann Machines can predict or classify FOREX strategies results. The comparative analysis with results obtained using Support Vector Machines with different kernels can be also done.

Deep belief nets seems to be quite new thing based on analysis of human brain way to work. Links to video tutorials are below

http://videolectures.net/icml09_bengio_lecun_tldar/

http://videolectures.net/mlss09uk_hinton_dbn/

http://videolectures.net/jul09_hinton_deeplearn/

The most research seems to be done by Mr. Hinton from Uni of Toronto and Mr Yann and Mr. Taylor from Uni of NY see links

http://www.cs.toronto.edu/~hinton/

http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html

http://www.cs.nyu.edu/~gwtaylor/

http://www.cs.nyu.edu/~yann/

To achieve our goals we can use already existing FOREX system TradeFX which is using MATLAB + MT4 and using Support Vector Machines for classification

http://www.columbia.edu/~xv2103/finance/TradeFX/index.htm

http://www.columbia.edu/~xv2103/finance/TradeFX/Report.htm

For people interested in SVM theory here is very good video course

http://videolectures.net/epsrcws08_campbell_isvm/

1) double check if all scripts of TradeFX are correct (already bugs found in three of them) and eventually expand this design by adding e.g. Buy Orders

2) Adapt the MATLAB code of Hintons autoencoder to be usable with TradeFX

3) Adapt the MATLAB code related to Phd Thesis of Taylor to be usable with TradeFX

4) Try to change standard MATLAB SVM kernel to different one (e.g. Wavelet)

Than back test and OOS test for systems from 2,3,4 can be done and results compared.

All code is dowloadable from links above

Questions are welcome

Krzysztof

Deep belief nets seems to be quite new thing based on analysis of human brain way to work. Links to video tutorials are below

http://videolectures.net/icml09_bengio_lecun_tldar/

http://videolectures.net/mlss09uk_hinton_dbn/

http://videolectures.net/jul09_hinton_deeplearn/

The most research seems to be done by Mr. Hinton from Uni of Toronto and Mr Yann and Mr. Taylor from Uni of NY see links

http://www.cs.toronto.edu/~hinton/

http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html

http://www.cs.nyu.edu/~gwtaylor/

http://www.cs.nyu.edu/~yann/

To achieve our goals we can use already existing FOREX system TradeFX which is using MATLAB + MT4 and using Support Vector Machines for classification

http://www.columbia.edu/~xv2103/finance/TradeFX/index.htm

http://www.columbia.edu/~xv2103/finance/TradeFX/Report.htm

For people interested in SVM theory here is very good video course

http://videolectures.net/epsrcws08_campbell_isvm/

**So our tasks would be:**1) double check if all scripts of TradeFX are correct (already bugs found in three of them) and eventually expand this design by adding e.g. Buy Orders

2) Adapt the MATLAB code of Hintons autoencoder to be usable with TradeFX

3) Adapt the MATLAB code related to Phd Thesis of Taylor to be usable with TradeFX

4) Try to change standard MATLAB SVM kernel to different one (e.g. Wavelet)

Than back test and OOS test for systems from 2,3,4 can be done and results compared.

All code is dowloadable from links above

Questions are welcome

Krzysztof

Last edited: