Scoring Pullback Quality with Retracement Depth, Range Context, and Momentum

Summary

This article presents an MQL5 framework for rating pullbacks within an EMA-defined trend. It measures retracement depth against the recent swing high and low, assigns quality categories from shallow to structurally broken, and adds two context measures: price position within a rolling 60-bar range and lag-1 autocorrelation of returns. A weighted composite score combines these inputs, which the author recommends using as a position-sizing scalar rather than a standalone entry trigger. The rolling swing anchors and range are approximations that can migrate; the range proxy also does not align to actual hourly boundaries.

The article reports an empirical study of 514 NQ one-minute New York sessions spanning May 2024 to May 2026. It finds average retracement depth near the boundary between the shallowest categories, with low cross-session variation; stressed sessions had the shallowest depths, contrary to the stated hypothesis about trending sessions. Autocorrelation was negative across regimes, leaving its positive-only score component inactive in most sessions. The composite weights are described as heuristics, and the author calls for strategy-specific calibration and out-of-sample validation.

Key ideas

• Retracement depth measures how much of a recent directional move price has surrendered; it does not predict a reversal level.
• EMA alignment establishes trend direction, while retracement depth maps pullbacks to ordinal quality categories.
• A rolling range position and positive return autocorrelation add context to the depth measure.
• The composite score uses heuristic weights and is proposed as a position-sizing input, not a binary entry signal.
• The reported NQ study found negative lag-1 autocorrelation across regimes and did not support the hypothesis that trending sessions had shallower pullbacks.

Tags

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