What is Nadaraya–Watson Regression? Nadaraya–Watson Regression is a type of Kernel Regression, which is a non-parametric method for estimating the curve of best fit for a dataset. Unlike Linear Regression or Polynomial Regression, Kernel Regression does not assume any underlying distribution of the data. For estimation, it uses a kernel function, which is a...

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This is a combination of the Lux Algo Nadaraya-Watson Estimator and Envelope. Please note the repainting issue. In addition, I've added a plot of the actual values of the current barstate of the Nadaraya-Watson windows as they are computed (lines 92-95). It only plots values for the current data at each time update. It is interesting to compare the trajectory...

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Frequently asked question is to explain how Gain parameter works in kalman funtion. This script serves as a visual representation of Gain parameter of Kalman function used in HMA-Kalman & Trendlines script. (The function creator's name was misspeled in that script as Kahlman) To see better results set your Chart's timeframe to Daily.

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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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It is a simple indicator that provides buy or sell signals based on the intersection of two EMAs and a simple moving average (SMA). once the Relative Strength Index has confirmed it. For greater accuracy, add additional indicators like stochastic RSI, MACD, etc. Use only for intraday trading, Not for Positional Trading

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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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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 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 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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Ehlers Stochastic script. This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 32:1: Predictive And Successful Indicators).

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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...

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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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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...

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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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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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