An adaptive filtering technique allowing permanent re-evaluation of the filter parameters according to price volatility. The construction of this filter is based on the formula of moving ordinary least squares or lsma, the period parameter is estimated by dividing the true range with its highest. The filter will react faster during high volatility periods and...
Scaled and smoothed oscillators can provide easy to read/use information regarding price, therefore i will introduce a new oscillator who create smooth results and use a fast and practical scaling method. In order to allow for even more smoothness the option to smooth the input with a lsma has been added.
Scaling Using Changes
In this indicator...
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 :...
The ability to reduce lag while keeping a good level of stability has been a major challenge for smoothing filters in technical analysis. Stability involve many parameters, one of them being overshoots. Overshoots are a common effect induced by low-lagging filters, they are defined as the ability of a signal output to exceed a target input. This...
One stop shop for multiple MA duplicates over different resolutions.
A veritable banquet of MA's to choose from.
Set up you MA variables, and then plot up to 4 duplicates all using different time frames.
A 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 :)
Even Shorter Estimation
I know that i'am insistent with the lsma but i really like it and i'm happy to deconstruct it like a mad pinescript user. But if you have an idea about some kind of indicator then dont hesitate to contact me, i would be happy to help you if its feasible.
My motivation for such indicator was to use back the correlation function (that i had...
Estimating the LSMA Without Classics Parameters
I already mentioned various methods in order to estimate the LSMA in the idea i published. The parameter who still appeared on both the previous estimation and the classic LSMA was the sample correlation coefficient. This indicator will use an estimate of the correlation coefficient using the standard score thus...
The fast z-score is a modification of the classic z-score that allow for smoother and faster results by using two least squares moving averages, however oscillators of this kind can be hard to read and modifying its shape to allow a better interpretation can be an interesting thing to do.
I already talked about the fisher transform,...
This is an experimental study inspired by Goichi Hosoda's Ichimoku Kinkō Hyō.
In this study, a McGinley Dynamic replaces the Tenkan-Sen and Kaufman's Adaptive Moving Average replaces the Kijun-Sen.
The cloud is calculated by taking the mean of the highest high and lowest low, adding a golden mean standard deviation above and below, and offsetting it over the...
Another lsma estimate, i don't think you are surprised, the lsma is my favorite low-lag filter and i derived it so many times that our relationship became quite intimate. So i already talked about the classical method, the line-rescaling method and many others, but we did not made to many IIR estimate, the only one was made using a general filter...
A veritable banquet of MA basis calculations to choose from.
3 separate sets of bands to tinker with.
Optional toggle-able time resolution.
Optional breakout shapes with their own separate multiplier.
A fart, some love and kisses. ...and I may have dribbled a bit. Sorry.
Go on. Have a tinker. You know you want to.
This is an experimental study that takes a moving average of price, then offsets the average by up to 11 consecutive Fibonacci numbers from 1 to 144.
Choose between Kaufman's Adaptive Moving Average, Hull Moving Average, Fractal Adaptive Moving Average, Geometric Moving Average, or Exponential Moving Average.
This study is an experiment based off the concept used in my Dynamic Range Channel indicator.
Rather than using a McGinley Dynamic, a moving average of your choice is used in this calculation.
There are eight different moving average types to choose from in this script:
- Kaufman's Adaptive Moving Average
- Geometric Moving Average
- Hull Moving...