TRADING SYSTEMS
FRAMA
Fractal Adaptive Moving Averages
by John F. Ehlers
We all want to eliminate bad whipsaw trades. Here's a weapon
you can add to your arsenal of technical indicators for just that purpose.
The objective of using filters is to separate
the desirable signals from the undesirable ones. The practical application
of moving averages often involves a tradeoff between the amount of smoothness
required and the amount of lag that can be tolerated. Moving averages have
this problem because the price data is not stationary and may have different
bandwidths over different time intervals.
Various momentum-adaptive filtering techniques have been developed to
take advantage of the nonstationary structure of prices. Adaptive filters
have also been developed based on price statistics and the cyclic content
of the price data. In this article, I will describe a different class of
filters that monitors a measure of temporal nonstationarity and alters
their bandwidth in response to this measure.
ARE MARKETS FRACTAL?
There is no argument that market prices are fractal. Fractal shapes
are self-similar -- that is, a particular fractal has the same roughness
and sparseness no matter how closely you view them. For example, if you
remove the labels from a five-minute chart, a daily chart, and a weekly
chart, you would have difficulty telling them apart. This is the characteristic
that makes them fractal. The fractal dimension that describes the sparseness
at all magnification levels defines the self-similarity.
...Continued in the October issue of Technical Analysis of
STOCKS & COMMODITIES
Excerpted from an article originally published in the October 2005
issue of Technical Analysis of STOCKS & COMMODITIES magazine. All rights
reserved. © Copyright 2005, Technical Analysis, Inc.
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