A better version of Apirine's RS EMA by using a superior MA: Ehlers Super Smoother.
In January 2022 edition of TASC Vitaly Apirine introduced his Relative Strength Exponential Moving Average. A concept not entirely new, as Tushar Chande used a similar calculation for his VIDYA moving average. Both are based on the idea to change EMA length depending on the absolute RSI value, so the moving average would speed up then RSI is going up or down from the center value (when there is a significant directional price movement), and slow down when RSI returns to the center value (when there is a neutral or sideways movement). That way EMA responsiveness would increase where it matters most, but decrease where there is a high probability of whipsaw.
There are only two main differences between VIDYA and RS EMA:
In this script, I try to get the best of all these approaches and present to you Relative Strength Super Smoother. It uses RS EMA algorithm to calculate the SuperSmoother length. Unlike the original RS EMA algorithm, that has an abstract "multiplier" setting to scale the period variance (without this parameter, RSI would only allow it to speed up twice; Vitaly Apirine sets the multiplier to 10 by default), my implementation has explicit lower bound setting, so you can specify the exact range of calculated length.
Settings:
This script is just an interface for my public libraries. Check them out for more information.
In January 2022 edition of TASC Vitaly Apirine introduced his Relative Strength Exponential Moving Average. A concept not entirely new, as Tushar Chande used a similar calculation for his VIDYA moving average. Both are based on the idea to change EMA length depending on the absolute RSI value, so the moving average would speed up then RSI is going up or down from the center value (when there is a significant directional price movement), and slow down when RSI returns to the center value (when there is a neutral or sideways movement). That way EMA responsiveness would increase where it matters most, but decrease where there is a high probability of whipsaw.
There are only two main differences between VIDYA and RS EMA:
- RSI internal smoothing - VIDYA uses SMA, as Chande's CMO is an RSI with SMA; RS EMA uses EMA
- Change direction - VIDYA sets the fastest length; RS EMA sets the slowest length
In this script, I try to get the best of all these approaches and present to you Relative Strength Super Smoother. It uses RS EMA algorithm to calculate the SuperSmoother length. Unlike the original RS EMA algorithm, that has an abstract "multiplier" setting to scale the period variance (without this parameter, RSI would only allow it to speed up twice; Vitaly Apirine sets the multiplier to 10 by default), my implementation has explicit lower bound setting, so you can specify the exact range of calculated length.
Settings:
- Lower Bound - fastest SuperSmoother length (when RSI is +100 or -100)
- Upper Bound - slowest SuperSmoother length (when RSI is 0)
- RSI Length - underlying RSI length. Unlike the original RSI that uses RMA as an internal smoothing algorithm, Vitaly Apirine uses EMA, which is approximately twice as fast (that is needed because he uses a generally long RSI length and RMA would be too slow for this). It is the same as the Upper Bound by default (0), as in the original implementation
This script is just an interface for my public libraries. Check them out for more information.
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