Pythagorean Moving Averages for Crossovers, Divergence, and Bands

Summary

The article explores arithmetic, geometric, and harmonic moving averages as components of MQL5 Expert Advisor signals. It describes how the geometric and harmonic means place more weight on smaller values, and how reflected versions shift that emphasis toward larger values. The proposed uses include arithmetic-mean price crossovers, a harmonic-mean divergence based on changing highs and lows, and a geometric-mean approach modeled on Bollinger Bands. The implementation discussion also covers MQL5 price buffers and combining signal classes with different weights.

The article reports that the geometric-mean approach in the bands setting showed promise in its tests, while the other approaches and a combined system were less compelling. It also notes that one-year testing is limited and that the harmonic mean was not evaluated in the same bands setup, so comparisons remain incomplete. These averages are presented as candidate tools for further research, not as established support, resistance, or profitable signals. The author recommends longer testing and identifies other moving-average forms for future investigation.

Key ideas

• Arithmetic, geometric, and harmonic means weight price observations differently.
• Reflected geometric and harmonic means emphasize larger values and can complement their lower-value-weighted forms.
• The proposed strategies include arithmetic-mean crossovers, harmonic-mean divergence, and geometric-mean bands.
• The article reports promise for the geometric-mean bands approach, but the tests are limited and comparisons are incomplete.
• Signal combinations and mean parameters require further evaluation over longer periods.

Tags

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