MQL5 Programs Market

This article presents a multi-symbol execution filter that scores real-time market quality before any trade is allowed. It measures spread behavior, tick velocity, quote gaps, micro-volatility, and a slippage estimate, then classifies the state to block degraded conditions. Once noise settles, a liquidity sweep continuation model evaluates structure shifts so entries occur only when execution is mechanically stable.

We build a compact profiler that records calls, min/max/average times, and slow-call counts to CSV, and a simple test runner that writes deterministic pass/fail reports. The article explains where to place measurements in an EA, how to sample ticks, and how to keep pure calculations testable. Running the script first and the profiling EA second provides repeatable evidence for regression analysis.

We introduce CPyramidBridge, a thin MQL5 layer that maps bet-sizing results to CPyramidEngine. The bridge applies probability to initial lot sizing, enforces a capacity-aware entry gate, promotes add-ons from dynamic divergence, adapts the trailing stop to reserve estimates, and syncs signals on close, allowing an Expert Advisor to convert model confidence and concurrency into a structured, decreasing-risk pyramid.

In this article we present yet another custom MQL5 Signal Class that we are labelling ‘CSignalBTreeBayesian’. We are marrying the algorithm of a balanced tree with a neural network that is built on Bayesian principles to formulate yet another custom signal testable independently or with other signals thanks to the MQL5 Wizard.

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.