Adaptive StochasticAdapt To The Right Situation
There are already some Adaptive Stochastic scripts out there, but i didn't see the concept of using different periods highest/lowest for their calculations. What we want
for such oscillator is to be active when price is trending and silent during range periods. Like that the information we will see will be clear and easy to use.
Switching between a long term highest/lowest during range periods and a short term highest/lowest during trending periods is what will create the adaptive stochastic.
The switching is made thanks to the Efficiency Ratio , the period of the efficiency ratio is determined by the length parameter.
The period of the highest and lowest will depend on the slow and fast parameters, if our efficiency ratio is close to one (trending market) then the indicator will use highest and lowest of period fast , making the indicator more reactive, if our efficiency ratio is low (ranging market) then the indicator will use highest and lowest of period slow , making the indicator less reactive.
The source of the indicator is a running line ( lsma ) of period slow-fast .
it is also possible to switch the parameters values, making the indicator reactive during ranging market and less reactive during trending ones.
Hope you enjoy
For any questions/demands feel free to pm me, i would be happy to help you
Filter
Retention-Acceleration FilterAnother Adaptive Filter
This indicator share the same structure as a classic adaptive filter using an exponential window with a smoothing constant.
However the smoothing constant used is different than any previously made (Kalman Gain, Efficiency ratio, Scaled Fractal Dimension Index) ,
here the smoothing constant is inspired by the different formulations for parameters resolution used in HPLC S. Said (J. High Resolution Chromatograpy &Chromatography Communciations, (1979) 193).
Different assumptions can be made which lead to different expressions for resolution in chromatographic parameters, therefore we will use highest's and lowest's in order to estimate an optimal smoothing constant based on if the market is trending or not. It can be complicated at first but the goal is to provide both smoothness at the right time and a fast estimation of the market center.
Handling Noise
In Red a Pure Sinewave. In White Sinewave + Noise. In Blue our filter of Period 3
Handling stationary signals is not the best thing to do since we need highest's and lowest's and for that non stationary signals with trend + cycle + noise are more suitable.
It is also possible to make it act faster by quiting the pow() function of AltK with sqrt(length) and smoothing the remaining constant.
Supertrend FilterA derivation of the famous SuperTrend indicator.
My motivation for such indicator was to use more recursion in the original SuperTrend code, this work was made quite fast but feel free to modify it, as always my work is more for inspirational use than anything else so i hope it will inspire you to get more involved with the SuperTrend code or to start coding with Pine.
The indicator no longer act as a trailing-stop but more like a filter, this is due to the fact that the indicator conditions are swapped and that the output is reused many times in the calculation.
Parameters change as well and involve particular gestion. The Factor Parameter is no longer an integer but decimals such that 0 < Factor < 1 .
When the Period is high the indicator tend to become less linear/static and look more like a classic moving average.So it is important to have higher Factor values when the Period is high and reciprocally.Here a table to help you with parameters settings :
for Period = 1 to 50 Factor = 0.5
for Period = 50 to 100 Factor = 0.6
for Period = 100 to 150 Factor = 0.7
for Period = 150 to 200 Factor = 0.8
for Period = 200 to 300 Factor = 0.9
There could be a formula to scale the Factor depending on the Period but there would be no proof that the scaling method used is optimal.
Hope you enjoy
For any questions/demands feel free to pm me, i would be happy to help you
[ALERTS] Range Filter"This is an experimental study designed to filter out minor price action for a clearer view of trends.
Inspired by the QQE's volatility filter, this filter applies the process directly to price rather than to a smoothed RSI .
First, a smooth average price range is calculated for the basis of the filter and multiplied by a specified amount.
Next, the filter is calculated by gating price movements that do not exceed the specified range.
Lastly the target ranges are plotted to display the prices that will trigger filter movement.
Custom bar colors are included. The color scheme is based on the filtered price trend."
Thanks to Donovan Wall...
Enjoy!
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
Auto-Line With DriftA variation on the Auto-Line indicator, we allow it to get closer to the price thanks to a drift , this also allow the line to be more directional .
This indicator can be used with moving averages using crosses as signals or as a band indicator by ploting a + dev as the upper band and a - dev as the lower one.
For any help or suggestions feel free to send a message :)
Range Filter [DW]This is an experimental study designed to filter out minor price action for a clearer view of trends.
Inspired by the QQE's volatility filter, this filter applies the process directly to price rather than to a smoothed RSI.
First, a smooth average price range is calculated for the basis of the filter and multiplied by a specified amount.
Next, the filter is calculated by gating price movements that do not exceed the specified range.
Lastly the target ranges are plotted to display the prices that will trigger filter movement.
Custom bar colors are included. The color scheme is based on the filtered price trend.
Jurik Moving AverageThis indicator was originally developed by Mark Jurik.
NOTE: If Mr. Jurik ask me to remove this indicator from public access then I will do it.
Butterworth FilterButterworth Filter script.
This indicator was described by John F. Ehlers in his book "Rocket Science for Traders" (2001, Chapter 15: Infinite Impulse Response Filters).
Gaussian FilterGaussian Filter script.
This indicator was described by John F. Ehlers in his book "Rocket Science for Traders" (2001, Chapter 15: Infinite Impulse Response Filters).
Hampel FilterHampel Filter script.
This indicator was originally developed by Frank Rudolf Hampel (Journal of the American Statistical Association, 69, 382–393, 1974: The influence curve and its role in robust estimation).
The Hampel filter is a simple but effective filter to find outliers and to remove them from data. It performs better than a median filter.
Ehlers FilterThis is the Adaptive Ehlers Filter.
I had to unroll the for loops and array because TV is missing crucial data structures and data conversions (Arrays and series to integer conversion for values).
I'm in the process of releasing some scripts. This is a very old script I had. This contains volatility ranges and can be used as trading signals. You can also see how the EF moves up or down, the direction, when price is sideways, and use price breaks up and down as signals from the line.
Have fun, because I didn't making this script hahaha
NOTE : There is an issue with the script where at certain time frames it positions itself below or above. I think its due to calculations. If anyone knows the fix before I get the chance to take a look at it, please let me know.
books.google.com
Ehlers Super Passband FilterEhlers Super Passband Filter script.
This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 34:8: The Super Passband Filter).
Ehlers StochasticEhlers Stochastic script.
This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 32:1: Predictive And Successful Indicators).
Ehlers Roofing FilterEhlers Roofing Filter script.
This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 32:1: Predictive And Successful Indicators).
Bandpass Filter Strategy ver 2.0 The related article is copyrighted material from
Stocks & Commodities Mar 2010
You can use in the xPrice any series: Open, High, Low, Close, HL2, HLC3, OHLC4 and ect...
You can change long to short in the Input Settings
WARNING:
- For purpose educate only
- This script to change bars colors.
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`).
Bandpass Filter Strategy ver 2.0 The related article is copyrighted material from
Stocks & Commodities Mar 2010
You can use in the xPrice any series: Open, High, Low, Close, HL2, HLC3, OHLC4 and ect...
WARNING:
- This script to change bars colors.
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
Price FlowFor those who like to trade with the trend instead of against it. This little script shows you what side of the daily/weekly/monthly timeframe open, price is currently trading at so that you dont accidentally trade against the higher timeframe momentum. Timeframes are customizable through the indicator settings panel.
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.
Rate of Change w/ Butterworth FilterIt passes the Rate of Change data through a Butterworth filter which creates a smooth line that can allow for easier detection of slope changes in the data over various periods of times.
The butterworth filter line and the rate of change are plotted together by default. The values for the lengths, for both the butterworth filter and the raw ROC data, can be changed from the format menu (through a toggle).
The shorter the Butterworth length, the closer the line is fitted to the raw ROC data, however you trade of with more frequent slope changes.
The longer the Butterworth length, the smoother the line and less frequent the slope changes, but the Butterworth line is farther of center from the raw ROC data.
