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?...
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...
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,...
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...
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...
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!
Applying a window to the filter weights provides sometimes extra control over the characteristics of the filter.In this script an hamming window is applied to the volume before being used as a weight.In general this process smooth the frequency response of a filter.
Lets compare the classic vwma with hamming windowed vwma
Something i noticed is that windowed...
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 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...
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
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 :...
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...
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
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`).
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".
![USDCAD: Kendall Rank Correlation NET on SMA [Loxx]](https://s3.tradingview.com/n/neh5f526_mid.png "Kendall Rank Correlation NET on SMA [Loxx]")
![TSLA: Nadaraya-Watson Envelope [LUX]](https://s3.tradingview.com/i/Iko0E2kL_mid.png "Nadaraya-Watson Envelope [LUX]")
![BTCUSD: Nadaraya-Watson Estimator [LUX]](https://s3.tradingview.com/a/amRCTgFw_mid.png "Nadaraya-Watson Estimator [LUX]")
![BTCUSDT: Adaptive, Double Jurik Filter Moving Average (AJFMA) [Loxx]](https://s3.tradingview.com/3/3sSmbpS7_mid.png "Adaptive, Double Jurik Filter Moving Average (AJFMA) [Loxx]")
