Using a Nonlinear Kalman Filter to Estimate Price Deviation

Summary

This indicator applies Ehlers’ nonlinear Kalman filter to deviation estimation as an alternative to standard-deviation measures based on moving averages. The description emphasizes responsiveness as an important property of volatility estimates and offers optional pre-smoothing with simple, exponential, smoothed, or linearly weighted moving averages. It presents the measure as a way to assess volatility, using it in a manner similar to a standard deviation indicator.

The indicator is explicitly non-directional: its value alone does not identify trend direction. The description provides no formula details, parameter guidance, comparative performance results, or trading rules, so it does not establish that this filter is faster or more useful than other volatility estimators in any particular market. It is best understood here as a volatility-measurement concept that requires separate evaluation and directional context before use in a strategy.

Key ideas

• The indicator uses an Ehlers nonlinear Kalman filter to calculate deviation.
• Several moving-average methods are available for pre-smoothing the input.
• The indicator measures volatility and does not reveal trend direction by itself.
• The description provides no empirical comparison or parameter guidance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.