MQL5 Programs Market

The TQNet framework opens up new possibilities for modeling and forecasting financial time series by combining modularity, flexibility, and high performance. The article explores the possibility of implementing complex mechanisms for handling global correlations, including advanced parameter initialization methods.

The article outlines the complete process of creating a multi-currency Expert Advisor using the Adwizard library for MetaTrader 5: from setting up the environment for creating optimization projects to obtaining the final multi-currency Expert Advisors, which combine multiple instances of a simple trading strategy. We will walk through setting up the necessary input parameters, conventions for convenient file names, and launching three instances of the final Expert Advisors on different trading accounts with different parameters.

We turn eigenvalues from a covariance matrix into a normalized spectral‑entropy score that measures how evenly variance is spread across factors. SpectralEntropyCalculator.mq5 compares two portfolios in one run, using native vector summation, ArraySort()-based ordering, element‑wise division, and the Shannon entropy formula. The report makes dominant factors visible and enables quick, repeatable checks of diversification quality.

This part implements PortfolioRisk.mqh, a shared library that shifts risk management to the account level. It scans positions and pending orders, computes margin and floating results, counts symbols, and decomposes pairs into currencies to detect concentration, then validates each new trade against portfolio limits. The Series EA example illustrates configuring scope (account-wide or magic-filtered), registering magics, and integrating the pre-trade gate.

We present an MQL5 script that converts closed trade history into comparable metrics: expectancy in currency, pips, and R-multiples, plus a sample-aware win rate via the Wilson interval. These inputs form a conservative, dimensionless Trade Quality Score. The tool draws a CCanvas panel, prints an Experts-tab report, and supports an hour-based session filter to analyze a defined trading window alongside full-history results.

We consider the methods with which Isotonic Regression calibrates raw RSI, Stochastic and price-action signal scores into probabilities that are sorted, while a separate Probability based Neural Network evaluates similar historical market states. This article uses both approaches in a ready-made MQL5 custom signal class that is compatible with MQL5 Wizard and provides up to 7 selectable entry modes. Reproducible tests compare isotonic-only signals with the combined Isotonic-PNN model to assess whether the network adds useful information beyond the simpler baseline.

In this article, we will return to the tree implementation. Now that we are familiar with the basic principles of constructors and destructors, we can finally fix the code presented in the previous article. Get ready for a real adventure in MQL5 programming.