Noxa indicators for Neuroshell

This is a discussion on Noxa indicators for Neuroshell within the Trading Software forums, part of the Commercial category; There is a good algorithm in CSSA Cycles. However, I believe it is Regression Slope of Moving Average, I believe ...

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Old Mar 23, 2008, 4:55am   #22
 
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There is a good algorithm in CSSA Cycles. However, I believe it is Regression Slope of Moving Average, I believe same lag can be achieved with Jurik Research algorithm if not even less. Correct me if I'm wrong..
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Old Mar 24, 2008, 3:46am   #23
P.Z
Joined Mar 2008
I don't think CSSA is based on calculating the slope of a moving average. As a matter of fact the cycles do not lag so it can't be. I know for sure that they extract cycle components from the variance in the data then CSSA allows the reconstruction of market cycles from a selection of these components. Their approach is quite different than having an MA-based tracker that has to lag somehow.

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Old Mar 24, 2008, 4:59am   #24
 
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Considering smoothing algorithm, yes, I believe it is one of the best.
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Old Mar 24, 2008, 7:38am   #25
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Using EigenVectors

Currently I am evaluating the EigenVectors indicator in Noxa. It is used to define the GroupStart and GroupDepth for some Noxa indicators parameters used in Trading Strategy.

Some Lissajous pattern formed differently when shifting the Eigenvectors. Anybody can advice which Lissajous pattern should be selected to provide good trading strategy?

Here samples of Lissajous pattern formed at different GroupStart and GroupDepth values and the respective trading result, the chart with data is inserted on zip file.

On the help it mentioned "The corresponding Lissajous pattern is a circle. Their inherent persistence over time makes them especially worthy of attention." But to get the circle pattern is not easy.

Cheers,
Arry
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compare-eigenvectors.jpg   traderesult.jpg  
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File Type: zip Different eigenvetors with 1-1 opt-rl.zip (26.4 KB, 445 views)
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Old Mar 24, 2008, 4:29pm   #26
P.Z
Joined Mar 2008
Hello Arry,

I had trouble using the Eigenvectors too. In fact they dumped it from the Beta version that I am testing for MetaStock.
Anyway I found the eigenvectors more useful on indicators rather than on price; any indicator for that matter. I look more precisely for a plateau in the spectrum (the wider and the flatter the best) where several pairs of eigenvectors plot a circular Lissajous. The eigenvectors in a pair appear shifted indicating a prediction mode…

Ps. Your optimization came out with about only 100 bars for the training range and a m-histories of 50. I don’t know much about Forex but your eigenvectors look pretty noisy; I wonder whether you can get anything good from this model. I would have expected something like m-histories = 250 or more with a training range at least four times that!

My two cents.

P.Z


Quote:
Originally Posted by arryex View Post
Currently I am evaluating the EigenVectors indicator in Noxa. It is used to define the GroupStart and GroupDepth for some Noxa indicators parameters used in Trading Strategy.

Some Lissajous pattern formed differently when shifting the Eigenvectors. Anybody can advice which Lissajous pattern should be selected to provide good trading strategy?

Here samples of Lissajous pattern formed at different GroupStart and GroupDepth values and the respective trading result, the chart with data is inserted on zip file.

On the help it mentioned "The corresponding Lissajous pattern is a circle. Their inherent persistence over time makes them especially worthy of attention." But to get the circle pattern is not easy.

Cheers,
Arry
Attached Thumbnails
dia-eod.gif   evector.gif   evectors-dia-eod-1.gif  

evectors-dia-eod-2.gif  
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Old Mar 24, 2008, 7:43pm   #27
 
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Have anyone tried to predict cycles?
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Old Mar 24, 2008, 8:10pm   #28
P.Z
Joined Mar 2008
I could write a thesis predicting cycles!

I got very mitigated results trying to predict CSSA and even cycles using NeuroShell. I think that FeedForward neural nets cannot deal well with temporal sequences. I did not give up and developed my own models with some encouraging results but I was barely able to predict more than one full period ahead. However I got some very good results when trying to predict SSA with CSSA as inputs with a memory-based recurrent state space model. Now my predictions go beyond 6 periods with good accuracy of the turning points! There is something very interesting to learn from the CSSA/SSA relationship!

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