A simple form of recursive filtering using an adjustable gain and a memory length. The filter predicts the next sample based on the previous values and the calculated error.

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This is an upgrade to the HMA-Kahlman Trend & Trendlines script (). This version gives more flexibility because you can play around with 2 parameters to Kalman function (Sharpness and K (aka. step size)).

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This script is a simplified version of John Ehlers's adaption of Dr. Kalman's optimum estimator as applied to price action (More can be found on this here: www.dimensionetrading.com). Here I have adapted two of these optimum estimators to work together to provide crossover signals. The user can choose the input of this filter in the 'input source'. The 'Ratio of...

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This indicator plot 3 Kalman filter zero lag EMA lines. It has less lag and is also smoother than the original EMA. It also has an option to show the crossover of two EMAs.

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STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt is a normalized Cardinal Sine Filter Kernel Weighted Fir Filter that uses Ehler's FIR filter calculation instead of the general FIR filter calculation. This indicator has Kalman Velocity lag reduction, a standard deviation filter, a clutter filter, and a kernel noise filter. When calculating the Kernels,...

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Kalman filter is a recursive algorithm that has been invented in the 1960s to track a moving target, remove any noisy measurements of its position and predict its future position. In finance, KF has been used by the asset management industry for various purposes. KF is an optimal choice in many cases and do at least better than a moving average smoothing. A port...

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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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Introduction 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...

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This strategy is an advanced version of the Loft Strategy V1, I shared earlier. (Loft Strategy V1 consists of a kalman filter (by alexgrover ) and a "stop and reverse" line which is following and the kalman filter. If the price goes in the same direction as the position side, the "stop and reverse" line approaches the kalman filter as set on the "Approach...

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Pips-Stepped, R-squared Adaptive T3 is a a T3 moving average with optional adaptivity, trend following, and pip-stepping. This indicator also uses optional flat coloring to determine chops zones. This indicator is R-squared adaptive. This is also an experimental indicator. What is the T3 moving average? Better Moving Averages Tim Tillson November 1,...

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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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Kalman filter on multiple RSI periods. Usefull on higher timeframes to confirm a change of trend.

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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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Introduction Inspired from the Kalman filter this indicator aim to provide a good result in term of smoothness and reactivity while letting the user the option to increase/decrease smoothing. Optimality And Dynamical Adjustment This indicator is constructed in the same manner as many adaptive moving averages by using exponential averaging with a smoothing...

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Price smoothed by a Kalman filter to cutout noisy price.

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This strategy consists of a kalman filter (by alexgrover ) and a "stop and reverse" line which is following the kalman filter. If the price goes in the same direction as the position side, the "stop and reverse" line approaches the kalman filter as set on the "Approach Decrease Step" parameter.

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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 :...

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