MQL5 Programs Market

Raw feature correlations contain estimation noise and a shared market-mode component that distort clustering. We fit the Marcenko–Pastur noise ceiling (with an effective sample size correction), apply constant-residual denoising and market detonation, and run the Optimal Number of Clusters routine. The result is a cleaned correlation matrix and stable cluster labels that avoid substitution effects and feed clustered MDI/MDA in the next article.

This article builds a Hull Moving Average Momentum indicator in MQL5 by combining raw price momentum with Hull MA smoothing. We compute momentum as the close-to-close difference over a user-defined length, form 2×Fast WMA − Slow WMA, then apply a final WMA with a square‑root period. The implementation covers inputs, buffers, warm-up/recalculation, and visualization with a color-coded line and zero-line filling, helping interpret positive/negative momentum without treating zero crossings as signals.

We implement a History Navigator for MetaTrader 5 that jumps the chart to an exact historical period by date and time. The dialog validates inputs, converts them to datetime, and searches bar times with a binary-search routine before centering the selected candle. The navigation logic is separated from chart control, improving testability and maintenance, and a one-click return restores the live market view.

We are pleased to present the final part of our series on GinAR — a neural network framework for time series forecasting. In this article, we analyze the results of testing the model on new data and assess its robustness under real-market conditions.

The Dendritic Cell Algorithm (DCA) is a metaheuristic inspired by the mechanisms of the innate immune system. Dendritic cells patrol the search space, accumulate signals about the quality of positions, and reach a collective decision: whether to exploit what they have found or to continue exploration. Let's take a look at how a biological model for detecting pathogens is transformed into an optimization algorithm.

This article implements a self-contained Isolation Forest library for MetaTrader 5 with no labels, no distribution assumptions and no external dependencies. It details a reproducible 64‑bit generator, tree/forest construction, scoring and feature design, then verifies results against Python and market data with two null models. The package includes an indicator that plots the decision variable and a gate example. Readers get a validated library, clear limits of applicability and a practical way to calibrate thresholds.