Adaptive Momentum Fusion: Six Ways to Adjust MACD Smoothing

Summary

Adaptive Momentum Fusion modifies MACD by recalculating the smoothing speed of its fast and slow averages on each bar. Six selectable engines use efficiency, volatility, fractal behavior, momentum, volume, or a composite of those measures to adjust responsiveness. The indicator also offers MACD or percentage-based PPO output, several signal-line modes, a histogram, crossing markers, and regular divergence detection. The article explains how to read these features and why its default signal filter can make oscillator-signal crossings behave much like zero-line crossings.

The document reports synthetic-bar comparisons of signal modes and pivot-alignment measurements across liquid US stocks, but these are diagnostics rather than strategy backtests. It warns that the fractal engine is price-scale dependent and can stop adapting on some instruments, and that MACD values are instrument-scale dependent, motivating PPO for cross-market comparisons. The described settings and examples do not establish profitability; signals still require evaluation in a defined trading and risk framework.

Key ideas

• The indicator recalculates fast and slow average smoothing speeds using one of six market-behavior engines.
• Its oscillator, histogram, signal line, and divergence markers provide distinct views of momentum and potential turning points.
• The default signal filter can make signal-line crossings closely track zero-line crossings, so signal mode affects interpretation.
• The article supplies diagnostic comparisons but no strategy-level profitability evidence.
• The fractal engine is price-scale dependent, while PPO is intended to make oscillator readings more comparable across instruments.

Tags

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