MQL5 Programs Market

In this article, we shift from Python research to native MQL5 engineering. We build the first module of the MMAR library: a shared constants header, an SVD-based OLS regression class, a Generalized Hurst Exponent estimator, and the partition analysis engine that computes the partition function, extracts tau(q), estimates H via zero-crossing interpolation, and scores multifractality through three diagnostic tests. Tested on 500,000 bars of EURUSD M10, the engine correctly classifies the data as multifractal in under four seconds. Part 4 of an eight-part series. Part 5 fits the tau(q) curve to four candidate distributions via the Legendre transform.

An SLSQP optimizer is implemented in MQL5 to resolve parameter discrepancies between a volatility library and Python's ARCH module. The article details constraint handling, gradient options, configuration, and convergence controls and shows how to integrate the solver into existing code. Practical examples and comparisons demonstrate matched log‑likelihoods and parameters on shared datasets.

We extend the Part 9 setup wizard to build a canvas-based, in-chart documentation system for MetaTrader 5. The panel is tabbed and scrollable, supports inline styling, images, and interactive controls, and renders with supersampled anti-aliasing. The result is a reusable engine that any MQL5 program can embed to deliver self-contained documentation directly on the chart.

This article demonstrates how to build the persistence foundation of a self-healing Expert Advisor in MQL5 using SQLite. Readers will learn how to create a permanent trade-state storage layer capable of surviving terminal restarts, shutdowns, and unexpected interruptions. The article covers SQLite integration in MetaTrader 5, database lifecycle management, persistent trade-state structures, and runtime state recovery using practical MQL5 implementations.