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...

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This indicator builds upon the previously posted Nadaraya-Watson Estimator. 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. For more information on the Nadaraya-Watson Estimator...

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The following tool smooths the price data using the Nadaraya-Watson estimator, a simple Kernel regression method. We make use of the Gaussian kernel as a weighting function. Kernel smoothing allows the estimating of underlying trends in the price and has found certain applications in stock prices pattern detection. Note that results are subject to repainting,...

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This indicator was originally developed by Marc Chaikin.

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This indicator was originally described by Joseph E. Granville in his book "Granville's New Key To Stock Market Profits" (1963).

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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

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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...

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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...

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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!

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Why use CLAM? Because candle length may be difficult to discern in fast, choppy markets. CLAM plots current price activity against previous trends. The calculation is similar to Know Sure Thing (KST) without the lag. CLAM uses Triple EMAs (TEMA) instead of Simple Moving Averages (SMAs), and raw open - close instead of clunky Rate of Change (ROC). CLAM...

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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`).

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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...

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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".

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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...

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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`).

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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...

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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...

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