FRAMA: An Adaptive Moving Average Based on Fractal Dimension

Summary

The document explains the Fractal Adaptive Moving Average (FRAMA), attributed to John Ehlers. FRAMA applies an exponential moving average with a smoothing factor that changes according to an estimate of price fractal dimension. The accompanying ProRealTime example estimates price ranges over two halves of a lookback window and the full window, uses those values to calculate fractal dimension, then updates the adaptive average with bounded smoothing.

The description says FRAMA tends to flatten during sideways markets and respond more quickly to trend changes than conventional moving averages. It suggests using these traits to reduce false signals in moving-average crossover approaches or to identify breaks from horizontal ranges earlier. These are qualitative claims: the document provides no tests, performance results, or guidance for selecting parameters or managing trades. The example is platform-specific, and its behavior depends on the chosen input price and lookback length.

Key ideas

• FRAMA adjusts an exponential moving average's smoothing factor using estimated price fractal dimension.
• The example compares ranges across two subperiods and the full lookback window.
• The document describes a flatter line in ranges and faster response to trend changes.
• It suggests FRAMA may help filter crossover signals or react to range breakouts.
• No empirical evaluation or parameter-selection guidance is included.

Tags

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