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> So much for theory. It's flawed. Simply put, Hans was attempting
> to combine apples and oranges, producing a function that is not
> scale invariant. The apples and oranges are the two axes
> themselves: net bar count and net price change, which he combines
> through mere addition. Yes, the curve goes up and down, but if you
> convert price in the time series, say from dollars to francs, the
> function produces different values.
I see your point, but does that make the calculation useless? I'm no
expert at applying the PFE, but it seems to me that you could use
relative values of the PFE. E.g. compare the current value to the N-
period average, or whatever. That way the absolute value of the
function isn't a problem. Or is the concept so flawed as to be
unusable?
> There's a completely different approach that is mathematically
> sound. Assume that if the market was a random walk, how would its
> net change in price vary as a function of the number of bars? The
> answer is net_price = C x delta_time ^ 0.5, where C is a scaling
> constant.
I believe that's the same concept that Alex Saitta described in his
Aug. 95 TASC article, which was the basis for the "StdDev Trend"
indicator I've posted here a few times. (If you haven't seen it, you
can get it from ftp://ftp.frii.com/pub/fritz. Get the files starting
with "trend".)
> The fractal dimension function in ELA format is posted at
> http://www.jurikres.com/freebies/mainfree.htm. The download file
> includes documentation. Help yourself.
Thanks for sharing this, Mark. I've looked at it a bit and I think
it shows great promise for a congestion filter. I haven't had a
chance to try applying it in any systems yet, but it looks good.
Has anyone out there had success applying Mark's JRC_fractal_dim
function as a filter to keep you out of flat/whipsaw periods?
Gary
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