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V. 20:7 (26-31): Zero-Lag Data Smoothers by John F. Ehlers
V. 20:7 (26-31): Zero-Lag Data Smoothers by John F. Ehlers
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Zero-Lag Data Smoothers by John Ehlers
Heres 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.
Heres 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.
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