Kernel by @jdehorty huge shoutout to him! This is only an idea for how I use it when trading All credit for the kernel goes to him, I did not make the kernel! I don't know how to make it more clear. I use this to assist with top-down analysis. timeframe I want to trade : timeframe to analyse with white noise and kernel: 1m : 1H 5m : 2H 15m : 4H 1H :...
Overview This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown...
Kernel Regression Ribbon is a flexible, visually pleasing trend identification tool. Plotting 8 different kernel regressions of different types and parameters allows the user to see where levels of support and resistance are being tested, retested and broken. What’s Kernel Regression? A statistical method for estimating the best fitting curve for a dataset, in...
Ticker: AMEX:SPY , Timeframe: 1m, Indicator settings: default General Purpose This script is an upgrade to the classic Bollinger Bands. The idea behind Bollinger bands is the detection of price movements outside of a stock's typical fluctuations. Bollinger Bands use a moving average over period n plus/minus the standard deviation over period n times a...
Overview: AI Momentum is a kernel function based momentum Indicator. It uses Rational Quadratics to help smooth out the Moving Averages, this may give them a more accurate result. This Indicator has 2 main uses, first it displays ‘Zones’ that help you visualize the potential movement areas and when the price is out of bounds (Overvalued or Undervalued). Secondly...
The Relational Quadratic Kernel Channel (RQK-Channel-V) is designed to provide more valuable potential price extremes or continuation points in the price trend. Example: Usage: Lookback Window: Adjust the "Lookback Window" parameter to control the number of previous bars considered when calculating the Rational Quadratic Estimate. Longer windows capture...
This toolkit provides filters and extra functionality for non-repainting Nadaraya-Watson estimator implementations made by @jdehorty. For the sake of ease I have nicknamed it "kreg". Filters include a smoothing formula and zero lag formula. The purpose of this script is to help traders test, experiment and develop different regression lines. Regression lines are...
This indicator is based on the work of @jdehorty and his amazing Nadaraya-Watson Kernel Envelope, which you can see here: General Description The Nadaraya-Watson Oscillator (NWO) will give the same information as the Nadaraya-Watson Envelope, but as an oscillator off the main chart, by plotting the relationship between price and the Kernel and its bands....
█ OVERVIEW WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some of the inherent shortcomings associated with the traditional WT algorithm. █ BACKGROUND The WaveTrend (WT) indicator has become a widely popular tool for traders in recent years. WT was first...
Due to popular request, this is an envelope implementation of my non-repainting Nadaraya-Watson indicator using the Rational Quadratic Kernel. For more information on this implementation, please refer to the original indicator located here: What is an Envelope? In technical analysis, an "envelope" typically refers to a pair of upper and lower bounds that...
// ENGLISH The problem of the wonderfuls Nadaraya-Watson indicators is that they repainting, @jdehorty made an aproximation of the Nadaraya-Watson Estimator using raational Quadratic Kernel so i used this indicator as inspiration i just added the Upper and lower band using ATR with this we get an aproximation of Nadaraya-Watson Envelope without repainting ...
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...
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...
STD-Filtered, Gaussian-Kernel-Weighted Moving Average is a moving average that weights price by using a Gaussian kernel function to calculate data points. This indicator also allows for filtering both source input price and output signal using a standard deviation filter. Purpose This purpose of this indicator is to take the concept of Kernel estimation and...
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...
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...
Returns a moving average allowing the user to control the amount of lag as well as the amplitude of its overshoots thanks to a parametric kernel. The indicator displays alternating extremities and aims to provide potential points where price might reverse. Due to user requests, we added the option to display the moving average as candles instead of a solid...
"In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable." from wikipedia.com KDE function with optional kernel: Uniform Triangle Epanechnikov Quartic Triweight Gaussian Cosinus Republishing due to change of function. deprecated script: