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Hi Jo,
<span
> You might find the work of <font
color=navy face=Arial>Jim<font
color=navy face=Arial> <span
class=GramE>Curry of interest
<a
href="">http://cyclewave.homestead.com/MarketTurns.html
Michael B
<span
>-----Original Message-----
From: manohohman
[mailto:kelols@xxxxxxxxxxx]
Sent: Monday, 20 October 2003 2:17
AM
To: Metastockusers@xxxxxxxxxxxxxxx
Subject: [Metastockusers] Re:
Hurst Exponent
<font size=3
face="Times New Roman">
<font size=2
face="Courier New">For all of those MS'er's who
are going to ask, what's a Hurst <font size=2
face="Courier New">
Exponent. It's math concept from fractals and
chaos theory. Here's
the simplest explanation I've seen, and I've added
some translation
for you.
The Hurst Exponent is a measure of the smoothness
of fractal time
series based on the asymptotic behaviour of the
rescaled range of the
process. (Translation: it measures correlatons in
a data series on
any time scale. Simpler Translation: it measures the
fractal
dimension of a data series. Even Simpler
Translation: it measures how
much fractals jump around--well sort of.)
The Hurst exponent, H, is defined as:
H:=log(R/S)/log(T)
where T is the duration of the sample of data, and
R/S the
corresponding value of rescaled range.
Hurst generalized an equation valid for the
Brownian motion in order
to include a broader class of time series. In
fact, Einstein studied
the properties of the Brownian motion and found
that the distance R
covered by a particle undergoing random collisions
is directly
proportional to the square-root of time T:
R=k*T0.5
where k is a constant which depends on the
time-series. The
generalization proposed by Hurst was:
R/S=k*TH
where H is the Hurst exponent.
If H=0.5, the behaviour of the time-series is
similar to a random
walk;
if H<0.5, the time-series covers less
"distance" than a random walk
(i.e., if the time-series increases, it is more
probable that then it
will decrease, and vice-versa);
if H>0.5, the time-series covers more
"distance" than a random walk
(if the time-series increases, it is more probable
that it will
continue to increase).
Given a time series x(n), n=1,....N, H can be
estimated by taking the
slope of (R/S) plotted vs. n in a log-log scale.
H is related to the fractal dimension D:
H=E+1-D
where E is the Euclidean dimension (E=0 for a
point, 1 for a line, 2
for a surface). For one-dimensional signals, H=2-D
H is also related to the "1/f" spectral
slope:
=2H+1
I think this has to be programmed in C++ and then
imported into MS as
a dll. Erik Long used to have a MS product for
this. He may be out of
business but you can call and find out. His
product for MS was called
Fractal Finance.
He wrote an article in the May 2003 S&C called
Making Sense of
Fractals. Some code may be in there that's useful
to you, but I don't
think so.
Tetrahex
555 W. Madison Street
Chicago, Illinois 60661
Contact: Erik Long
Contact number: 312.775.7468
Some MS users have programmed a fractal noise
measurement that
approximates the Hurst and have used that
successfully.
Join this group and ask them for John Connors C++
code for <font size=2
face="Courier New">Hurst<font
size=2 face="Courier New">, or <font
size=2 face="Courier New">
for an approximation of <font
size=2 face="Courier New">Hurst<font
size=2 face="Courier New"> or if they know of
any conversions. <span
>
This group really loves that stuff. Find
Igor. He knows about
approximations in MS.
<a
href="">http://groups.yahoo.com/group/Behavioral-Finance/
I haven't really progressed past moving averages
so this stuff is way
beyond me. I'm still trying to figure out cloning.
However, many, many people have gone crazy trying
to program the
Hurst Exponent in MS, so <font
size=2 face="Courier New">Roy<font
size=2 face="Courier New"> leave this one alone.<font
size=2 face="Courier New">
JO
--- In Metastockusers@xxxxxxxxxxxxxxx, karile
<karile@xxxx> wrote:
>
> Hi,
>
> Can someone code the Hurst Exponent in
Metastock ?
>
> Thanks for your help,
>
> Karile
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