Discussing the article: "MQL5 Wizard Techniques You should know (Part 86): Speeding Up Data Access with a Sparse Table for a Custom Trailing Class"

[MetaQuotes]

Check out the new article: MQL5 Wizard Techniques You should know (Part 86): Speeding Up Data Access with a Sparse Table for a Custom Trailing Class.

We revamp our earlier articles on testing trade setups with the MQL5 Wizard by putting a bit more emphasis on input data quality, cleaning, and handling. In the earlier articles we had looked at a lot of custom signal classes, usable by the wizard, so we now shift our focus to a custom trailing class, given that exiting is also a very important part in any trading system. Our broad theme for this particular piece data-efficiency and the O(1) range-query; the core ‘tech’ is MQL5, SQLite, Python-Polars; the Algorithm is the Sparse-Table while we will seek validation from the ATR Indicator.

Many Expert Advisors with custom trailing stops run into a practical performance and data‑quality bottleneck: on each bar or even on every tick they scan long historical windows (typically 500–5,000+ bars) to locate highs/lows, which makes the EA's processing time grow linearly and causes hidden execution drift on VPSs or in live trading. At the same time, relying on MetaTrader's built‑in history access can expose the EA to gaps and inconsistencies that change behavior between the tester and live runs.

This article reframes the problem more narrowly:

• (1) when a trailing routine recalculates range extrema repeatedly across large lookback windows and high update frequency, O(N) queries become a measurable cause of latency and delayed exits;
• and (2) an EA needs a reproducible, cleaned price feed instead of ad‑hoc terminal reads.

To address these two, we build an O(1) Range Minimum/Maximum Query (Sparse Table) for constant‑time extreme lookups and pair it with a Python (Polars) → SQLite pipeline that cleans, interpolates and timestamps ticks/bars so the EA reads deterministic data.

Author: Stephen Njuki