{_FractalDim
 Calculate the approximate Fractal Dimension of a waveform
 Copyright (c) 2003 by Alex Matulich, Unicorn Research Corporation
 January 2006: changed to calculate on n intervals rather than n-1.

 Note: the Hurst exponent H = 2 - D, where D is the fractal dimension.

 See the following article (particularly equation 6):
 http://www.complexity.org.au/ci/vol05/sevcik/sevcik.html
 Cevcik, Carlos, "A procedure to Estimate the Fractal Dimension of
 Waveforms," International Complexity, Vol 5, 1998.

 Because the lookback length n goes from 0 to n, we also have n
 intervals, and not n-1 intervals as described in the paper.
}

Inputs:
    y(NumericSeries),   {price data, e.g. Close of data1}
    n(NumericSimple);   {lookback length}

Vars: j(0), lngth(0), ymax(0), ymin(0), yscl(0), dx2(0), dy(0);

ymin = Lowest(y, n); ymax = Highest(y, n); yscl = ymax - ymin;
if n < 2 Or ymax = ymin then
    lngth = 1
else begin
    { calculate length of curve }
    lngth = 0;
    dx2 = Square(1/n);
    for j = 1 To n begin
        dy = (y[j] - y[j-1]) / yscl;
        lngth = lngth + SquareRoot(dx2 + dy*dy);
    end;
end;
_FractalDim = 1 + (Log(lngth) + Log(2)) / Log(2*n);

{Note: for large n, the result above converges to:
_FractalDim = 1 + Log(lngth) / Log(2*n);
However, because we typically using small values of n (like n<50)
for market analysis, we use the result with the Log(2) term in it.
See the technical article referenced in the opening comments.}