Relative Moving Average Fractiles and Cross Strategies

Summary

The document explains the Relative Moving Average framework, which describes a current price’s position within a trailing window rather than treating a smoothed average as a signal. It computes a local simple moving average, expresses window landmarks as normalized deviations from that average, and maps them to fractile ranks on a zero-to-one scale. The rank is intended to make readings comparable across assets and volatility conditions.

The implementation adds a regime detector for expansion, contraction, and transition, then applies four strategies that trade crossings between RMA curves and fractiles. It also describes the indicator, display, and Expert Advisor architecture, along with risk controls and an adverse-movement monitor. The article reports Strategy Tester results, but the supplied excerpt omits their details, so performance cannot be assessed here. The author also notes limitations in the underlying approach, including lag from a directional-consistency filter and the need to calibrate window size for each asset. This is an implementation of a research framework, not evidence that its signals will be profitable across markets.

Key ideas

• RMA represents price landmarks as normalized deviations from a rolling simple moving average.
• Fractile ranks place current price within its recent window on a zero-to-one scale.
• The system classifies market conditions as expansion, contraction, or transition.
• Four cross strategies use fractile movements to enter and exit positions.
• Window calibration and filter lag are practical limitations that require further evaluation.

Tags

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