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Mark, I have also found that adding variable smoothing does help
things - sometimes considerably. Even MS has a volatility based
adjustable moving average with the VAR switch in the 'mov' function.
But, as I'm sure you know, there are many ways to do the adjustments.
ReefBreak
PS
The formula MS gives for VAR in the user guide(p473) is wrong (as of
Ver9) better go to the source in TASC.
--- In equismetastock@xxxxxxxxxxxxxxx, "Mark Jurik"
<mark_yahoo_equismetastock@xxx> wrote:
>
> The code is simply a combination of a simple moving average and one
> way of approximating overall slope:
>
> Result = SMA + slope
>
> You can get the desired slope calc in MS by using the built-in linear
> regression slope function. This way, ...
>
> Result = mov( data array, periods, simple )
> +
> linregslope( data array, periods ) * scaling_factor
>
> You set the scaling factor as in input parameter, which allows you to
> adjust the amount of lag reduction. This approach to achieving low
> lag smoothing is NOT adaptive and has a significant overshoot penalty
> in proportion to the amount of lag reduction you request.
>
> This formula poorly approximates JMA, which is adaptive, non-linear
> and has better lag/overshoot tradeoff properties. If you want the
> real thing, go to http://www.jurikres.com .
>
> Mark Jurik
> Jurik Research
>
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