Detecting Price Zones with Time-Gap and Volume Metrics

Summary

The article proposes an MQL5 indicator for identifying price zones that see brief activity followed by extended periods without revisits. It divides a historical price range into adaptive zones, examines bar-by-bar contacts and absences, and calculates volume intensity and exit speed. A volume impact coefficient and a confidence score combine activity, movement speed, and gap duration to decide whether a zone qualifies. The design also tracks zone boundaries, prior tests, an institutional-footprint estimate, decaying memory strength, and possible mitigation when price traverses the zone.

The suggested interpretation is that rapid, high-volume movement may reflect large participants and that later tests of a zone may produce rebounds or continuation after it is filled. The text presents illustrative formulas and code, but it does not provide a reproducible validation method or enough details about the claimed account testing to establish predictive value. Its institutional attribution and rebound claims should therefore be treated as hypotheses; thresholds and behavior may depend on instrument, timeframe, and data quality.

Key ideas

• The indicator scans adaptive price zones and records contacts, absences, volume bursts, and exit speed.
• A confidence score combines volume impact, velocity, and the duration of price absence.
• The system tracks zone tests, an estimated institutional footprint, decaying memory, and full traversal of a zone.
• The article interprets rapid movement and later zone reactions as possible evidence of institutional activity, but does not establish that attribution.
• The reported trading observations lack sufficient validation detail to assess predictive reliability.

Tags

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