3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machines

Here are some results for EURUSD 30min obtained for SVM, RBM net and DBN net.

Experiment was done for 2 strategies, instantpip and pipmaximizer for training on 1000 and 2500 bars. Number of OOS bars in both cases was 500.

SVM was using linear kernel, RBM was using 500 neurons and trained for 500 epochs,
DBN net had configuration 500-500-500 and trained for 500 epochs also.

Results were classified if the equity curve ended positive or negative or no trade

pipmaximaizer

no trade - 8
positive - 1 RBMsell2500
negative - 3

instantpip

no trade - 1
positive - 5 DBNsell1000, RBMsell100(final eq=0), RBMsell2500,SVMsell100(finaleq=0),SVMsell2500
negative - 6

so clearly instantpip strategy seems to extract series features much better than pipmaximaizer.

than 2 questions are coming to my mind

1) are we tricked by randomenss here - I dont think so. TradeFX tries to trade on each bar
so we have 500 real OOS samples but......

2) Are those features are constant for certain TF and currency pair or they are changing in time ??

Perhaps some WF test should be done to have more data.

Any ideas ??

A while back I have tested SVMs on noisy cycles; I got terrible results. Could you confirm that with your setup?

Could you please show the full equity curve including the trend transition?

SVM are classifiers so it is not surprising. You need at least two separate classes for them to discriminate well. I don’t think your test is conclusive because learning only from a trend is a one class problem. SVM will try to separate this class explaining the bad results you got.

I think it depends if you will denoise input data to SVM. I just provided output from different indicators so data was not denosied.

see post 60 and 61. All those post show only OOS part,

The purpose of those test was to see if basic funcionality of TradeFX is working
and to see which trading signal will be generated in case when OOS pattern wont match
training pattern and result was as expected. The same behaviour shows RBN and DBN.

Krzysztof

nice video lectures about Advances in Machine Learning for Computational Finance

http://videolectures.net/amlcf09_london/

Obviously the results from post 65 were not so much encouraging. But it's not end of the world.

The following ways forward coming my mind:

1) Use the strategies with numeric values. Current strategies are using binary values
so it's just 7 and 9 bits of information. It can be not enough to extract market info

In this case classifiers based on RBM and DBN can no be used but
classifiers based on CRBM and FCRBM should be used instead

2) Use ensembled methods so multiple classifiers. There is a lot if ensembled methods like

* AdaBoostM1
* Bagging
* Up Sampling
* Down Sampling
* Stacking / Hierachial Classification
* Stacking with Feature Subspaces
* Majority Voting
* Multi-Class Classification Wrapper using Output Coding
* Multi-Label Classification Wrapper

For more info see book 'PATTERN CLASSIFICATION USING ENSEMBLE METHODS'

3) Design other strategies which hopefully will extract more info from market

4) Use other (e.g. tree) classifiers

Any other ideas ??

You post 58 has clean sinusoids. As the first part of the equity lines seems to indicate (your posts 60, 61) I'd like to have confirmation that SVM don't work on noisy (market) conditions.

I'd like to see the full equity line including the transition to the down trend; your post 62 does not show it. Just curious to see whether SVM could be wrong in detecting the change in trend for various training periods (the start or end date vary).