Kalman Filter Smoothing for Price Trend Extraction

Summary

The document describes a Kalman Filter indicator intended to smooth price noise and extract an underlying trend. It distinguishes this recursive approach from filters that rely on a history of observations: each update uses the prior state estimate and its error together with the current price measurement. The indicator exposes three inputs: a Kalman factor, a sharpness setting, and the applied price series.

Its update equations combine the distance between the current price and prior estimate with adjustments to the estimated error and velocity. The Kalman factor scales the velocity adjustment, while sharpness and the Kalman factor determine a square-root scaling term for the error update. The description supplies no parameter guidance, market examples, comparison with other smoothing methods, or backtest results. It also does not discuss initialization or how settings affect lag and responsiveness, so practical use requires further specification and testing.

Key ideas

• The filter recursively updates a price estimate to smooth noise and reveal a trend.
• Each update uses the previous estimate and velocity together with the current price.
• The Kalman factor, sharpness, and applied price are the three stated inputs.
• The description provides no initialization method, tuning advice, or evidence of trading performance.

Tags

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