Zero-Lag Data Smoothers by John Ehlers
Here’s a technique that can reduce lag to nearly zero.
A causal filter can never predict the future.
As a matter of fact, the laws of nature
demand that all filters must have lag.
However, if we assume steady-state
conditions — that is, no new, disturbing events — there are techniques we can use to reduce
the lag of these filters to nearly zero. It turns out that
such filters are useful for technical analysts with
which to smooth data, and perhaps create some fast-
acting indicators. This is possible because the steadystate
assumptions are almost, but not quite, satisfied
in the short run. These techniques are not applicable
to longer moving averages, because steady-state
conditions do not continue over a long time span.
There are superior techniques for creating longerterm
averages, such as nonlinear filters or by
removing undesirable cycling components from a
composite price waveform.