Kendall Rank Correlation NET on SMA is an SMA that uses Kendall Rank Correlation to form a sort of noise elimination technology to smooth out trend shifts. You'll notice that the slope of the SMA line doesn't always match the color of the SMA line. This is behavior is expected and is the NET that removes noise from the SMA. What is Kendall Rank Correlation?...
Adaptive, Double Jurik Filter Moving Average (AJFMA) is moving average like Jurik Moving Average but with the addition of double smoothing and adaptive length (Autocorrelation Periodogram Algorithm) and power/volatility {Juirk Volty) inputs to further reduce noise and identify trends. What is Jurik Volty? One of the lesser known qualities of Juirk smoothing...
This indicator builds upon the previously posted Nadaraya-Watson smoothers. Here we have created an envelope indicator based on Kernel Smoothing with integrated alerts from crosses between the price and envelope extremities. Unlike the Nadaraya-Watson estimator, this indicator follows a contrarian methodology. Please note that by default this indicator can be...
This one is a little different. Instead of layering lots of indicators to filter noise, I'm instead using two different kinds of price averaging to smooth the candles and better define the direction. Just select a smoothing value that fits your chart and timeframe. In theory, this should remove a fair bit of noise (although nothing's perfect) I've managed to...
The following tool smoothes the price data using various methods derived from the Nadaraya-Watson estimator, a simple Kernel regression method. This method makes use of the Gaussian kernel as a weighting function. Users have the option to use a non-repainting as well as a repainting method, see the USAGE section for more information. 🔶 USAGE 🔹 Non...
The Adaptive Relative Strength Index was created by John Ehlers and this is his first version. I will of course publish his updated version at a later date along with publishing the final script from Jim Sloman's Ocean Theory book. I have changed his script to include extra smoothing to provide clear buy and sell signals. This is a version of a RSI that is very...
Introducing HARSI - the RSI based Heikin Ashi candle oscillator. ...that's right, you read it correctly. This is Heikin Ashi candles in an oscillator format derived from RSI calculations, aimed at smoothing out some of the inherent noise seen with standard RSI indicators. Science! We likes it we does. Included plot options for standard RSI plot overlay, and...
This code is based on Smoothed HA candle which will work on all chart types condition for BUY: 1. When close crosses Smoothed HA 2.Close should be in side upper band 3.BBW must be greater than the average vice versa for sell this code takes data from HA chart so that it can be applied on all chart type. Bollinger band and Bollinger band width conditions added...
Description : This SwissArmyKnife - MultiPurposeIndicator allows user to modify the Directional index based on one of filtering tools proposed by John F.Ehlers . Details of each filtering type can be read in Ehlers Technical Papers: "Swiss Army Knife Indicator" and/or his book "Cybernetics Analysis for Stock and Futures" Disclaimer: These study scripts was built...
Introduction Who doesn't like smooth things? I'd like a smooth market price for christmas! But i can't get it, instead its so noisy...so you apply a filter to smooth it, such filters are called low-pass filters, they smooth and its great but they have lag, so nobody really use them, but they are pretty to look at. Its on a childish note that i will introduce...
Introduction Indicators settings have been a major concern in trading strategies, in order to provide the best results each indicators involved in the strategy must have its settings optimized, when using only 1 indicator this task can easily be achieved, but an increasing number of indicators involve more slower computations, lot of softwares will use brute...
Introduction It was one of my most requested post, so here you have it, today i present a way to estimate an LSMA of any degree by using a kernel based on a sine wave series, note that this is originally a paper that i posted that you can find here figshare.com , in the paper you will be able to find the frequency response of the filter as well as both python...
Introduction Today i propose an hybrid filter that use a classical FIR architecture while using recursion. The proposed method aim to reduce the lag generated by fir filters. This particular filter is a sine weighted moving average, but you can change it since the indicator is built with the custom filter template (1). Even if it use recursion it still is a FIR...
Introduction FIR filters (finite impulse response) are widely used in technical analysis, there is the simple or arithmetic moving average, the triangular, the weighted, the least squares...etc. A FIR filter is characterized by the fact that its impulse response (the output of a filter using an impulse as input) is finite, this mean that the impulse response...
Introduction The Hull smoothing method aim to reduce the lag of a moving average by using a simple calculation involving smoothing with a moving average of period √p the subtraction of a moving average of period p/2 multiplied by 2 with another moving average of period p , however it is possible to extend this calculation by introducing more terms thus...
Introduction I already mentioned various problems associated with the lsma, one of them being overshoots, so here i propose to use an lsma using a developed and adaptive form of 1st order polynomial to provide several improvements to the lsma. This indicator will adapt to various coefficient of determinations while also using various recursions. More In Depth ...
Introduction There are tons of filters, way to many, and some of them are redundant in the sense they produce the same results as others. The task to find an optimal filter is still a big challenge among technical analysis and engineering, a good filter is the Kalman filter who is one of the more precise filters out there. The optimal filter theorem state that :...
Smoothed Random Walk Index. It gives slightly slower but less false signal than stochastic. If it draws double bottom with higher low, long entry is considered. If it draws double with lower high, short entry is considered. For more accuracy, another smoothed RWI with slower setting is needed. If fast setting RWI draws lower high AND slower setting RWI is also...