Parabolic Weighted Moving AverageThe 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!
Smoothing
Kalman SmootherA 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
One Dimensional Parametric Kalman FilterA 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 be used as source for others indicators such as stochastic/rsi/moving averages...etc
For any questions/suggestions feel free to contact me
On Balance VolumeThis indicator was originally described by Joseph E. Granville in his book "Granville's New Key To Stock Market Profits" (1963).
Moving Average 3.0 (3rd Generation)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".
Ehlers StochasticEhlers Stochastic script.
This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 32:1: Predictive And Successful Indicators).
Ehlers Super Smoother FilterEhlers 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`).
Ehlers Leading IndicatorEhlers 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`).
Quadratic RegressionA 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 Quadratic regression attempt to minimize the sum of squares (sum of the squared difference between a set of data and an estimator), this is why
those kinds of filters have low lag .
Here the difference between a Least Squared Moving Average ( green ) and a Quadratic Regression ( red ) of both period 500
Here it look like the Quadratic Regression have a best fit than the LSMA
Hamming Windowed Volume Weighted Moving AverageApplying 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 filters depending on their period ( high ones in general ) tend to make less bad crosses with the price ( at least with the hamming window )
Here are some data regarding number of crosses with period 50 with the hamming vwma in orange and the classic vwma in purple
Feel free to use the hamming window when using weighted filter.
Double Exponential SmoothingSingle Exponential Smoothing ( ema ) does not excel in following the data when there is a trend. This situation can be improved by the introduction of a second equation with a second constant gamma .
The gamma constant cant be lower than 0 and cant be greater than 1, higher values of gamma create less lag while preserving smoothness.Higher values of length must be followed by higher values of gamma in order to keep the lag low.
The first smoothing part consist of a classic ema but we add s-s1 to the previous smoothed value, this will help decrease lag.The second smoothing part then updates the trend, which is expressed as the difference between the last two values.
Holt Exponential Moving AverageHolt Exponential Moving Average indicator script.
This indicator was originally developed by Charles C. Holt (International Journal of Forecasting 20(1):5-10, March 2004: Forecasting seasonals and trends by exponentially weighted moving averages).
Auto-FilterA least squares filter using the Auto line as source, practical for noise removal without higher phase shift.
Its possible to create another parameter for the auto-line length, just add a parameter Period or whatever you want.
r = round(close/round)*round
dev = stdev(close,Period)
Hope you enjoy :)
