History of MQL5.community development

This article implements a box‑constrained Truncated Newton Conjugate‑Gradient (TNC) optimizer in MQL5 and details its core components: scaling, projection to bounds, line search, and Hessian‑vector products via finite differences. It provides an objective wrapper supporting analytic or numerical derivatives and validates the solver on the Rosenbrock benchmark. A logistic regression example shows how to use TNC as a drop‑in alternative to LBFGS.

This is an article about a specialized trend-following EA that aims to clearly elaborate how to frame and utilize trading setups that occur from imbalances found in PD arrays. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing PD arrays and imbalances as entry criteria for their trades and trading decisions. It will also explore how to correctly determine and profile premium and discount arrays and how to validate and utilize each of them when they occur in their respective market conditions, thus trying to maximize opportunities that occur from such scenarios.

For maximum reliability and productivity in MetaTrader products built with MQL, this article advocates a development approach based on reusable “packages” managed by KnitPkg, a project manager for MQL5/MQL4. A package can be used as a building block for other packages or as the foundation for final artifacts that run directly on the MetaTrader platform, such as EAs, indicators, and more.

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.

We have developed a system that enforces a daily trade limit to keep you aligned with your trading rules. It monitors all executed trades across the account and automatically intervenes once the defined limit is reached, preventing any further activity. By embedding control directly into the platform, the system ensures discipline is maintained even when market pressure rises.

The article builds a transparent MQL5 Expert Advisor for Larry Williams’ hidden smash day reversals. Signals are generated only on new bars: a setup bar is validated, then confirmed when the next session trades beyond its extreme. Risk is managed via ATR or structural stops with a defined risk-to-reward, position sizing can be fixed or balance-based, and direction filters plus a one-position policy ensure reproducible tests.

We continue to integrate methods proposed by the authors of the Attraos framework into trading models. Let me remind you that this framework uses concepts of chaos theory to solve time series forecasting problems, interpreting them as projections of multidimensional chaotic dynamic systems.

During sideways price movements, traders face excessive signals from multiple moving average crossovers. Today, we discuss how ALGLIB preprocesses raw price data to produce filtered crossover layers, which can also generate alerts when they occur. Join this discussion to learn how a mathematical library can be leveraged in MQL5 programs.

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.