Discussing the article: "How to Detect Round-Number Liquidity in MQL5"
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Check out the new article: How to Detect Round-Number Liquidity in MQL5.
The article presents an MQL5 method for detecting psychological round numbers by converting prices to strings and counting trailing zeros (ZeroSize). It outlines the theory of institutional liquidity at integers, explains the GetZeroCount logic with tick-size normalization to avoid floating‑point errors, and details hierarchical visualization. Case studies across forex, metals, and crypto, plus timeframe filters and inputs, show how to use confluence and basic risk controls in practice.
Price often clusters and reverses at whole integers (psychological round-number levels). In practice, this creates operational issues. Some timeframes become cluttered with trivial lines, while on others, strong levels are indistinguishable from noise. Simple modulo tests also fail due to floating‑point artifacts and heterogeneous quote formats (Digits, TickSize) across FX, metals, indices, and crypto.
This article presents a practical, testable solution for MQL5 that addresses these real-world pain points. We formalize a quality criterion for “correct detection” across instruments and timeframes. We introduce the ZeroSize metric as a numeric measure of level strength. We also describe a deterministic detection pipeline (tick-size normalization plus string-suffix analysis) that is invariant to IEEE‑754 artifacts and symbol formatting. Finally, we provide visualization rules to reduce chart clutter. We also include setup and risk-management guidance so the output can be used in indicators, alerts, and semi-automated systems.
Author: Mostafa Ghanbari