开始

Poles

Multi Poles Zero-Lag Exponential Moving AverageIntroduction Based on the exponential averaging method with lag reduction, this filter allow for smoother results thanks to a multi-poles approach. Translated and modified from the Non-Linear Kalman Filter from Mladen Rakic 01/07/19 www.mql5.com The Indicator length control the amount of smoothing, the poles can be from 1 to 3, higher values create smoother results. Difference With Classic Exponential Smoothing A classic 1 depth recursion (Single smoothing) exponential moving average is defined as y = αx + (1 - α)y which can be derived into y = y + α(x - y ) 2 depth recursion (Double smoothing) exponential moving average sum y with b in order to reduce the error with x , this method is calculated as follow : y = αx + (1 - α)(y + b) b = β(y - y ) + (1-β)b The initial value for y is x while its 0 for b with α generally equal to 2/(length + 1) The filter use a different approach, from the estimation of α/β/γ to the filter construction.The formula is similar to the one used in the double exponential smoothing method with a difference in y and b y = αx + (1 - α)y d = x - y b = (1-β)b + d output = y + b instead of updating y with b the two components are directly added in a separated variable. Poles help the transition band of the frequency response to get closer to the cutoff point, the cutoff of an exponential moving average is defined as : Cf = F/2π acos(1 - α*α/(2(1 - α))) Also in order to minimize the overshoot of the filter a correction has been added to the output now being output = y + 1/poles * b While this information is far being helpful to you it simply say that poles help you filter a great amount of noise thus removing irregularities of the filter. Conclusion The filter is interesting and while being similar to multi-depth recursion smoothing allow for more varied results thanks to its 3 poles. Feel free to send suggestions :) Thanks for reading
Pine Script™指标
由alexgrover提供
8