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
Adaptive
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.
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.
Adaptive Least SquaresAn 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 slower during low volatility ones.
High smooth parameter will create smoother results, values inferior to 3 are recommended.
You can easily replace the parameter estimation method as long as the one used fluctuate in a range of , for example you can use the efficiency ratio
ER = abs(change(close,length))/sum(abs(change(close)),length)
Or the Fractal Dimension Index , in fact any values will work as long as they are rescaled (stoch(value,value,value,length)/100)
For any suggestions/questions feel free to send me a message :)
Ehlers Smoothed Adaptive MomentumEhlers Smoothed Adaptive Momentum script.
This indicator was developed and described by John F. Ehlers in his book "Cybernetic Analysis for Stocks and Futures" (2004, Chapter 12: Adapting to the Trend).
Ehlers Instantaneous TrendlineEhlers Instantaneous Trendline script.
This indicator was described by John F. Ehlers in his book "Rocket Science for Traders" (2001, Chapter 10: The Instantaneous Trendline).
Kaufman Adaptive Moving AverageKaufman Adaptive Moving Average script.
This indicator was originally developed by Perry J. Kaufman (`Smarter Trading: Improving Performance in Changing Markets`, 1995).
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 MESA Adaptive Moving Averages (MAMA & FAMA)Ehlers MESA Adaptive Moving Averages (MAMA & FAMA) script.
These indicators was originally developed by John F. Ehlers (Stocks & Commodities V. 19:10: MESA Adaptive Moving Averages).
Ehlers Deviation-Scaled Moving Average (DSMA)Ehlers Deviation-Scaled Moving Average indicator script.
This indicator was originally developed by John F. Ehlers (Stocks & Commodities V. 36:8: The Deviation-Scaled Moving Average).
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).
Ahrens Moving AverageAhrens Moving Average indicator script.
This indicator was originally developed by Richard D. Ahrens (Stocks & Commodities V.31:11 (26-30): Build A Better Moving Average).
Adaptive Laguerre FilterAdaptive Laguerre Filter indicator script.
The Adaptive Laguerre Filter was originally developed and described by John Ehlers in his paper `Time Warp – Without Space Travel`.
Thanks to @apozdnyakov for the sorting solution.
Sharp Modified Moving AverageSharp Modified Moving Average indicator script. This indicator was originally developed by Joe Sharp (Stocks & Commodities, V.18:1, More Responsive Moving Averages).
Adaptive Moving AverageAdaptive Moving Average indicator script. This indicator was originally developed by Vitali Apirine (Stocks & Commodities V.36:5: Adaptive Moving Averages).
Laguerre FilterLaguerre Filter indicator based on the John Ehler's article "Time Warp – Without Space Travel" about the Laguerre Transform
Zero Lag Exponential Moving AverageZero Lag Exponential Moving Average indicator script based on the original version by John Ehlers and Ric Way
Regularized Exponential Moving AverageRegularized Exponential Moving Average indicator script based on the original version by Chris Satchwell
T3 Moving AverageT3 Moving Average indicator script based on the article `Smoothing Techniques For More Accurate Signals` by Tim Tillson (Stocks & Commodities V16:1 (33-37))
Variable Index Dynamic Average (VIDYA)Variable Index Dynamic Average indicator script based on the original version by Tushar Chande.
