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Webinar: John Ehlers on Indicators for Effective Trading Strategies


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Webinar: John Ehlers on Indicators for Effective Trading Strategies

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  #61 (permalink)
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After watching Ehlers presentation one question came to mind:

1) Why would you extract the high frequencies to filter them afterwards?

2) Wouldn't it be easier to just apply the SuperSmoother directly to price and use it as input for Stochastics (or any other indicator) ?

I don't know if anyone has already tested and made a comparison between these two options. It is the underlying concept that I don't understand yet. Maybe someone with Signal Processing background has a good insight.


Thanks!

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  #62 (permalink)
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disco391 View Post
Code for various platforms are in the Traders Tip section.
Specifically the Amibroker code for SuperSmoother function can be found here:
John

Amibroker 5.95 introduced the IIR (Impulse Response Filter), which should provide better performance than looping.
I am a rookie here so I can't provide a link to its readme -- too bad.

Please find the two functions ported.

Thanks,
Gabor



Version(5.95);

PI = 3.1415926;
SQ2 = sqrt( 2 );

function SuperSmoother( array, periods )
{

c1 = SQ2 * PI / periods;
c2 = exp ( -c1 );
a1 = 2 * c2 * cos( c1 );
a2 = -c2^2;
b0 = (1 - a1 - a2)/2;
b1 = b0;

return IIR( Nz( array ), b0, a1, b1, a2 );
}

function HighpassFilter( array, periods )
{
alpha1 = ( cos( SQ2 * PI / periods ) + sin ( SQ2 * PI / periods ) - 1 ) / cos( SQ2 * PI / periods );

C1 = ( 1 - alpha1 / 2 ) ^ 2;
b0 = C1;
b1 = C1 * -2;
b2 = C1;
a1 = 2 * ( 1 - alpha1 );
a2 = - ( ( 1 - alpha1 ) ^ 2 );

return IIR( Nz( array ), b0, a1, b1, a2, b2 );
}

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  #63 (permalink)
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Fat Tails View Post
@krzysiaczek99: If you tell me that a pair of scissors is superior to a pencil, I would certainly respond that my preference depends on the case.

The SMA is a finite response filter which is not really smooth. As all finite response filters it is slow to digest rapid moves or gaps.

The Supersmoother is an infinite response filter, which is generally faster to react and considerably smoother compared to the SMA.

I do not agree that the SuperSmoother has a similar behavior as the SMA, see chart below:


Sorry for 3y delayed response, I haven't notice that anyone response. So on your picture you compare SMA(l/2) and SS(l) because its the formula to calculate period parameter between them ?? (so eg SMA(5) and SS(10) )

Krzysztof

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  #64 (permalink)
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krzysiaczek99 View Post
Sorry for 3y delayed response, I haven't notice that anyone response. So on your picture you compare SMA(l/2) and SS(l) because its the formula to calculate period parameter between them ?? (so eg SMA(5) and SS(10) )

Krzysztof

3 years later. LOL.

The chart compares a SMA(20) to a 3-pole SuperSmoother(20).


However, the bar period has no meaning for many filters.


In order to find comparable bar periods you would need to calculate the lag of the SuperSmoother and compare it to the SMA.


This has been done for SMA and EMA.

Originally, Wilder's (exponential) average has been used with a smoothing constant k=1/N.

However, Wilder's average(N) is much slower than a SMA(N).

Therefore Jack Hutson suggested to use the formula k = 2/(N+1) instead of k = 1/N. (see article in one of the first issues of Stocks & Commodities).

With the introduction of the smoothing constant k = 2/(N+1) an EMA(N) had now a comparable lag to a SMA(N). The EMA is calibrated against the SMA.


This has not been done for the SuperSmoother. But the picture shows that the SS(20) has both a smaller lag and superior smoothing when compared to the SMA(20).

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  #65 (permalink)
stockholm sweden
 
 
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Hi,

I think it just the best to evaluate its performance in trading systems as according to his presentation it should
clean data from aliasing noise and spectral dilation so after denoising SNR ratio should be higher and data much
predictable. Unfortunately its opposite.... more info here

3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machines - Page 36

Krzysztof

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  #66 (permalink)
stockholm sweden
 
 
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and some more comparative analysis of SMA vs Ehler supersmoother

3rd generation NN, deep learning, deep belief nets and Restricted Boltzmann Machines - Page 37

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