Thank you very much kvak, this ATR Line is very interesting!
Furthermore, I did a study on the averages in the Chat gpt pro about counter-trend indicators and it signaled the new Kalman averages as promising. Something I had also noticed
The Study:
Z-Score: which average makes more sense as a “mean”?
Z-Score depends on (i) mean/anchor and (ii) lookback standard deviation.
Recommendation of families (from your list) for the Z-Score anchor:
KAMA (Kaufman Adaptive Moving Average) as an adaptive anchor when you want the center to “move away” in noise/volatility and “move closer” when the movement is efficient. This adaptability is part of the KAMA design.
ALMA (Arnaud Legoux Moving Average) as a “smooth and responsive” anchor (good smoothing/lag ratio). The ALMA text itself emphasizes that averages are filters and discusses smoothness and responsiveness as conflicting requirements; furthermore, it compares SMA/EMA/HMA/ALMA.
Super Smoother (Ehlers) as a low-lag DSP anchor to smooth noise while preserving response. The documentation describes Super Smoother as a smoothing technique linked to analog (aerospace) filters and aimed at reducing noise, with configurable cutoff length.
Kalman (adaptive/Kalman smoothed) as an anchor when you want an estimator that adjusts "how much to trust" the measure (price) vs. the state (trend). This is the essence of the Kalman filter in a smoothing/adaptation context.
Thank you very much for this excellent upgrade to eAverages.
