Adaptive Laguerre Price Filter for Smoothing Market Noise

Summary

This document presents a price-smoothing indicator based on a Laguerre filter attributed to John Ehlers. It first averages the open, high, low, and close, then measures the absolute difference between that price and the prior filtered value. The difference is normalized over a rolling lookback, and a short-window median supplies the adaptive alpha parameter. That parameter feeds four recursive Laguerre stages, which are combined into the final smoothed price series. The stated purpose is to clarify price direction while reducing noise; the author notes that similar filtering could be adapted to other indicators.

The page supplies an implementation with example parameter settings, but no chart, comparison, backtest, or trading rules. It does not establish that the filter improves signals or avoids lag, and implementation details such as initialization and behavior when the normalization range is zero may affect results. The document describes this version as intended for price, so use on other series would require adaptation and validation.

Key ideas

• The indicator smooths an average of the open, high, low, and close through four recursive Laguerre stages.
• It adapts the smoothing parameter using a normalized price-difference measure and a rolling median.
• The final series combines the four stages with greater weight on the middle stages.
• The post gives implementation details but no evidence of trading performance or comparison with other filters.

Tags

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