Using Moving Average Transformations to Filter Trailing Stop Adjustments

Summary

The article presents a trading application of horizontal composition of natural transformations, using price, moving-average, and smoothed moving-average time series. It defines differences between pairs of moving-average outputs as time-series buffers, then uses their correlation together with the direction of moving-average trends to filter trailing-stop adjustments. The example pairs the trailing logic with an Awesome Oscillator entry signal in an MQL5 expert advisor. The author also suggests applying the same construction to high-low ranges when a more direct volatility measure is desired.

The method is framed as a filter for managing open positions, not as a stand-alone entry strategy. The article reports positive in-sample results but unsuccessful out-of-sample or walk-forward results, so it does not establish efficacy. It notes that performance may depend on the entry signal, input series, and test period, and calls for broader evaluation before drawing conclusions.

Key ideas

• Differences between moving-average time series are treated as natural-transformation buffers for analysis.
• Correlation between those buffers filters whether a trailing stop should move with the trend.
• The worked example combines the trailing logic with an Awesome Oscillator signal in MQL5.
• Using high-low ranges instead of closes may make the inputs more directly sensitive to volatility.
• The reported in-sample results were positive, while out-of-sample and walk-forward results were not.

Tags

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