Introduction Edge-preserving smoothing is often used in image processing in order to preserve edge information while filtering the remaining signal. I introduce two concepts in this indicator, edge preservation and an adaptive cumulative average allowing for fast edge-signal transition with period increase over time. This filter have nothing to do with classic...
Important ! The indicator is for experimental purpose only, it must not be used as a decisional tool but only as a visual one (like Zig-Zag, Fractal etc). The information this indicator display is uncertain and subject to drastic changes over time. If you have further question feel free to pm me. Introduction Most of the filters you will find are causal,...
This type of moving average was originally developed by Bruno Pio in 2010. I just ported the original code from MetaTrader 5. The method uses a linear combination of EMA cascades to achieve better smoothness. Well, actually you can create your own X-uple EMA, but be sure that the combination' coefficients are valid.
This type of moving average was originally developed by Bruno Pio in 2010. I just ported the original code from MetaTrader 5.
The weights of this moving average are powers of the weights of the standard weighted moving average WMA . Remember: When parameter Power = 0, you will get SMA . When parameter Power = 1, you will get WMA . Good luck!
A derivation of the Kalman Filter. Lower Gain values create smoother results.The ratio Smoothing/Lag is similar to any Low Lagging Filters. The Gain parameter can be decimal numbers. Kalman Smoothing With Gain = 20 For any questions/suggestions feel free to contact me
A One Dimensional Kalman Filter, the particularity of Kalman Filtering is the constant recalculation of the Error between the measurements and the estimate.This version is modified to allow more/less filtering using an alternative calculation of the error measurement. Camparison of the Kalman filter Red with a moving average Black of both period 50 Can...
This indicator was originally developed by Marc Chaikin.
This indicator was originally described by Joseph E. Granville in his book "Granville's New Key To Stock Market Profits" (1963).
Moving Average 3.0 (3rd Generation) script. This indicator was originally developed and described by Dr. Manfred G. Dürschner in his paper "Gleitende Durchschnitte 3.0".
Ehlers Stochastic script. This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 32:1: Predictive And Successful Indicators).
Ehlers Super Smoother Filter script. This indicator was originally developed by John F. Ehlers (see his book `Cybernetic Analysis for Stocks and Futures`, Chapter 13: `Super Smoothers`).
Ehlers Leading Indicator script. This indicator was originally developed by John F. Ehlers (see his book `Cybernetic Analysis for Stocks and Futures`, Chapter 16: `Leading Indicators`).
A quadratic regression is the process of finding the equation that best fits a set of data.This form of regression is mainly used for smoothing data shaped like a parabola. Because we can use short/midterm/longterm periods we can say that we use a Quadratic Least Squares Moving Average or a Moving Quadratic Regression. Like the Linear Regression (LSMA) a...
Applying a window to the filter weights provides sometimes extra control over the characteristics of the filter.In this script an hamming window is applied to the volume before being used as a weight.In general this process smooth the frequency response of a filter. Lets compare the classic vwma with hamming windowed vwma Something i noticed is that windowed...
Single Exponential Smoothing ( ema ) does not excel in following the data when there is a trend. This situation can be improved by the introduction of a second equation with a second constant gamma . The gamma constant cant be lower than 0 and cant be greater than 1, higher values of gamma create less lag while preserving smoothness.Higher values of length ...
Holt Exponential Moving Average indicator script. This indicator was originally developed by Charles C. Holt (International Journal of Forecasting 20(1):5-10, March 2004: Forecasting seasonals and trends by exponentially weighted moving averages).