MQL4 and MQL5 Programming Articles

This article develops an interactive MQL5 plot for the binomial distribution, combining a histogram of simulated outcomes with the theoretical probability mass function. It implements mean, standard deviation, skewness, kurtosis, percentiles, and confidence intervals, along with configurable themes and labels, and supports dragging, resizing, and live parameter changes. Use it to assess expected wins, likely drawdowns, and confidence ranges when validating trading strategies.

One of the directions for increasing the efficiency of the model training and convergence process is the improvement of optimization methods. Adam-mini is an adaptive optimization method designed to improve on the basic Adam algorithm.

This article demonstrates how to automatically identify potentially profitable trading strategies using MetaTrader 5. White-box solutions, powered by unsupervised matrix factorization, are faster to configure, more interpretable, and provide clear guidance on which strategies to retain. Black-box solutions, while more time-consuming, are better suited for complex market conditions that white-box approaches may not capture. Join us as we discuss how our trading strategies can help us carefully identify profitable strategies under any circumstance.

The sqlite3 module in Python offers a straightforward approach for working with SQLite databases, it is fast and convenient. In this article, we are going to build a similar module on top of built-in MQL5 functions for working with databases to make it easier to work with SQLite3 databases in MQL5 as in Python.

In this article, we advance the binomial distribution graphing tool in MQL5 by integrating DirectX for 3D visualization, enabling switchable 2D/3D modes with camera-controlled rotation, zoom, and auto-fitting for immersive analysis. We render 3D histogram bars, ground planes, and axes alongside the theoretical probability mass function curve, while preserving 2D elements like statistics panels, legends, and customizable themes, gradients, and labels

In this article, we will consider another optimization algorithm inspired by inanimate nature - Charged System Search (CSS) algorithm. The purpose of this article is to present a new optimization algorithm based on the principles of physics and mechanics.

Gaussian Process Kernels are the covariance function of the Normal Distribution that could play a role in forecasting. We explore this unique algorithm in a custom signal class of MQL5 to see if it could be put to use as a prime entry and exit signal.

Machine learning is often viewed through statistical or linear algebraic lenses, but this article emphasizes a geometric perspective of model predictions. It demonstrates that models do not truly approximate the target but rather map it onto a new coordinate system, creating an inherent misalignment that results in irreducible error. The article proposes that multi-step predictions, comparing the model’s forecasts across different horizons, offer a more effective approach than direct comparisons with the target. By applying this method to a trading model, the article demonstrates significant improvements in profitability and accuracy without changing the underlying model.

This article will show you how to visualize candle data obtained via the WebRequest function and API in candle format. We'll use MQL5 to read the candle data from a CSV file and display it as custom candles on the chart, since indicators cannot directly use the WebRequest function.

Managing trading account risk is a challenge for all traders. How can we develop trading applications that dynamically learn high, medium, and low-risk modes for various symbols in MetaTrader 5? By using PCA, we gain better control over portfolio variance. I’ll demonstrate how to create applications that learn these three risk modes from market data fetched from MetaTrader 5.

In the latest installment of this series, we move beyond individual machine learning techniques to address the "Research Chaos" that plagues many quantitative traders. This article focuses on the transition from ad-hoc notebook experiments to a principled, production-grade pipeline that ensures reproducibility, traceability, and efficiency.

We wrap our look into the complementary pairing of the MA & Stochastic oscillator by examining what role inference-learning can play in a post supervised-learning & reinforcement-learning situation. There are clearly a multitude of ways one can choose to go about inference learning in this case, our approach, however, is to use variational auto encoders. We explore this in python before exporting our trained model by ONNX for use in a wizard assembled Expert Advisor in MetaTrader.

An in-depth walkthrough of building a Larry Williams inspired volatility breakout Expert Advisor in MQL5, combining swing structure, volatility-based entries, trade day of the week filtering, time filters, and flexible risk management, with a complete implementation and reproducible test setup.

In this article, we'll look at what you need to do to start using Excel to manage MetaTrader 5, but in a very interesting way. To do this, we will use an Excel add-in to avoid using built-in VBA. If you don't know what add-in is meant, read this article and learn how to program in Python directly in Excel.

The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.

In this part, we will integrate a real-time correlation matrix into a multi-symbol Expert Advisor to prevent redundant or risk-stacked trades. By dynamically measuring cross-pair relationships, the EA will filter entries that conflict with existing exposure, improving portfolio balance, reducing systemic risk, and enhancing overall trade quality.