As prelude to this new investigation I have below plotted two charts of real ES Mini closes in cyan, along with a Savitzky-Golay filter in red and the "Super Smoother" in green for comparative purposes.
filter = sgolayfilt( close , 3 , 11 ) ;
which is the actual call that the above are plots of, which is an 11 bar cubic polynomial fit. As can be seen, the Savitzky-Golay filter is generally as smooth as the super smoother but additionally seems to have less lag and doesn't overshoot at turning points like the super smoother. Given a choice between the two, I'd prefer to use the Savitzsky-Golay filter.
However, there is a serious caveat to the use of the S-G filter - it's a centred filter, which means that its calculation requires "future" values. This feature is well demonstrated by the animated figure at the top right of the above linked Wikipedia page. Although I can't accurately recall the reason(s) I abandoned the S-G filter all those years ago, I'm pretty sure this was an important factor. The Wikipedia page suggests how this might be overcome in its "Treatment of first and last points" section. Another way to approach this could be to adapt the methodology I employed in creating my perfect oscillator indicator, and this is in fact what I intend to do over the coming days and weeks. In addition to this I shall endeavour to make the length of the filter adaptive rather than having a fixed bar length polynomial calculation.
And the reason for setting myself this task? Apart from perhaps creating an improved smoother following my recent disappointment with frequency domain smoothing, there is the possibility of creating useful inputs for my Neural Net trading algorithm: the framework of S-G filters allows for first, second, third and fourth derivatives to be easily calculated and I strongly suspect that, if my investigations and coding turn out to be successful, these will be unique and informative trading inputs.
However, there is a serious caveat to the use of the S-G filter - it's a centred filter, which means that its calculation requires "future" values. This feature is well demonstrated by the animated figure at the top right of the above linked Wikipedia page. Although I can't accurately recall the reason(s) I abandoned the S-G filter all those years ago, I'm pretty sure this was an important factor. The Wikipedia page suggests how this might be overcome in its "Treatment of first and last points" section. Another way to approach this could be to adapt the methodology I employed in creating my perfect oscillator indicator, and this is in fact what I intend to do over the coming days and weeks. In addition to this I shall endeavour to make the length of the filter adaptive rather than having a fixed bar length polynomial calculation.
And the reason for setting myself this task? Apart from perhaps creating an improved smoother following my recent disappointment with frequency domain smoothing, there is the possibility of creating useful inputs for my Neural Net trading algorithm: the framework of S-G filters allows for first, second, third and fourth derivatives to be easily calculated and I strongly suspect that, if my investigations and coding turn out to be successful, these will be unique and informative trading inputs.
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