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

This is a discussion on 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machines within the Trading Software forums, part of the Commercial category; Originally Posted by Highfreq I fed the net with 2D image representations of the patterns. For the example below I ...

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Old Oct 19, 2010, 9:48pm   #33
Joined Jan 2007
Re: 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machi

Quote:
Originally Posted by Highfreq View Post
I fed the net with 2D image representations of the patterns. For the example below I used cycles that fit price well in the first part of the pattern but degrade in the second part. Next I attached a label to each of these patterns by looking for cycles that fit well the second part. These classes of patterns capture a switch of the cycle. To make the network happy I had to generate tons of surrogates from these patterns making sure there were no ambiguities between the classes. Below, I show 2 features from the net. See how the feature on the right has rotated clockwise. They indicate that price has switched to a larger time-frame. Now the remaining problem is not an implementation issue but I did not figure out yet how to make an online version for the training phase that would provide nicely evolving features.
How much of the process required manual intervention?

What do you mean by cycles? There are so many discussions of cycles derived from FFT's, Goertzel, etc. but I think you mean something else.

How do you know when a network is happy? If you can distill that info, I'll buy a can or two. I still don't know when my wife is happy, and I've been married for many years.
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Old Oct 20, 2010, 2:37pm   #34
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Some TradeFX results

Krzysiaczek99 started this thread Here is some TradeFX results obtained using attached EURUSD 1m file for instantPip strategy. I was changing the value of cutpoint variable between 2500 - 400.

For explanation: cutpoint 400 means 400 OOS bars and 400 training bars, cutpoint 1000 means 1000 OOS bars and 1000 training bars

cutpoint accuracy, total profit, number of trades

2500 62.7 0 0

2000 64.2 -0.8 54

1500 62.86 -82.7 276

1000 58.5 12.9 244

400 64 50.8 92

so intial conclusion is that for using SVM in predicting trade strategy results accuracy is quite high 58-64 %. It also shows that the prediction time horizon is quite short. As this strategy makes sell orders only more tests like this on different data sets must be done to avoid 'lucky' results.

Krzysztof
Attached Files
File Type: csv EURUSD1.csv (510.8 KB, 262 views)

Last edited by Krzysiaczek99; Oct 20, 2010 at 2:54pm.
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Old Oct 20, 2010, 8:45pm   #35
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Re: 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machi

NNs of any kind are elaborate schemes for curve-fitting and nothing more than that.
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Old Oct 20, 2010, 10:52pm   #36
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Re: 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machi

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Originally Posted by fralo View Post
How much of the process required manual intervention?

What do you mean by cycles? There are so many discussions of cycles derived from FFT's, Goertzel, etc. but I think you mean something else.

How do you know when a network is happy? If you can distill that info, I'll buy a can or two. I still don't know when my wife is happy, and I've been married for many years.
The cycle part is now automated. I use it in my trading. The cycles are from a third party package for Neuroshell. It's better than FFT in that it is data aware. Bandpass filters are computed from volatility itself.

Well, it will be wine for me. I am getting intimate with my nets. I figure they are happy when they make me happy. For sure DBNs need a lot of training samples before the features exhibit some structure. They settle very slowly...........
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Old Oct 20, 2010, 10:58pm   #37
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Re: Some TradeFX results

Quote:
Originally Posted by Krzysiaczek99 View Post
Here is some TradeFX results obtained using attached EURUSD 1m file for instantPip strategy. I was changing the value of cutpoint variable between 2500 - 400.

For explanation: cutpoint 400 means 400 OOS bars and 400 training bars, cutpoint 1000 means 1000 OOS bars and 1000 training bars

cutpoint accuracy, total profit, number of trades

2500 62.7 0 0

2000 64.2 -0.8 54

1500 62.86 -82.7 276

1000 58.5 12.9 244

400 64 50.8 92

so intial conclusion is that for using SVM in predicting trade strategy results accuracy is quite high 58-64 %. It also shows that the prediction time horizon is quite short. As this strategy makes sell orders only more tests like this on different data sets must be done to avoid 'lucky' results.

Krzysztof
Interesting post! It would be interesting to see how your system behaves on ranging markets... My experience with SVMs is that they give similar results than NN-based classifiers. SVMs are good though because they are less prone to overfitting. That might partly explain your results.
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Old Oct 20, 2010, 11:00pm   #38
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Re: 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machi

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Originally Posted by intradaybill View Post
NNs of any kind are elaborate schemes for curve-fitting and nothing more than that.
Let me quote StratOpt

There is absolutely nothing wrong with curve fitting. The only danger in optimizing is if you allow an overfit. Optimizing is only one part of a testing equation. In all reality, If you develop a strategy of any kind and it has a parameter or any other rule in which you make a choice as to what setting or rule to use then you have already curve fit before you begin any optimization. So yes you are curve fitting and yes genetic optimizers are a "fancy" way of doing such, and yes that is what they are designed to do. Again, there is nothing wrong with any of that. I have been very successfully incorporating such things into my development and testing and trading for quite a long while now and can't imagine not using them and remaining successful with any sort of programmatic strategy trading.

http://www.trade2win.com/boards/trad...ml#post1229142
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Old Oct 21, 2010, 1:16am   #39
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Re: 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machi

Krzysiaczek99 started this thread
Quote:
Originally Posted by Highfreq View Post
I fed the net with 2D image representations of the patterns. For the example below I used cycles that fit price well in the first part of the pattern but degrade in the second part. Next I attached a label to each of these patterns by looking for cycles that fit well the second part. These classes of patterns capture a switch of the cycle. To make the network happy I had to generate tons of surrogates from these patterns making sure there were no ambiguities between the classes. Below, I show 2 features from the net. See how the feature on the right has rotated clockwise. They indicate that price has switched to a larger time-frame. Now the remaining problem is not an implementation issue but I did not figure out yet how to make an online version for the training phase that would provide nicely evolving features.
So actually how do you model the time series ?? Using RBM ?? I think RBM was designed
for static picture recognition not dynamic time series. Or maybe you just feeding nets with static picture of the pattern ??? But in this case all info about dynamics of time series is lost.

Do you measure any error of recognition ?

Krzysztof
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Old Oct 21, 2010, 3:55am   #40
 
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Re: 3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machi

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Originally Posted by fralo View Post
Here is a question for philosophers and thread poets. Care to answer?

There is inherent structure in the images and speech that have been the targets of most DBN research... most children can do better than most machines because they have learned the structure. The machines try to extract the structure. Some do it well for images and speech. Can they do it for markets?

Is there structure to some representation of the markets? Certainly not recognizable by most adults. Probably not recognizable by majority of traders. Do supertraders recognize structure, or are they just defying the odds?

DBN machines make sense for robotic applications. Do they make sense for the market?
No.

Let's say you have a bad knee. You can see a number of different types of specialist and depending on the area of specialisation, you will either have a muscle problem, bone problem or even immune system problem. Therapy will be physio, drugs or operation.

What you won't get much of is "this is not within my area of specialisation". After all, if the only tool you have is a hammer...

And so it comes to trading. Take someone with a background in statistics and they will probably use statistical models. Take someone with a background in programming and they will be writing strategies based on existing specifications (i.e. trading books). Take someone with a background in Neural Nets.....

So - this is more of the same. Attempting to use knowledge gained outside of trading within trading. Trying to apply your own comfort zone where you are not comfortable.

Ultimately - if you want to make money trading, you have to learn how to trade yourself. If you can't do that, you have zero chance of getting a computer to learn for you.
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