Articles with MQL5 programming examples

Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic methods. Our findings show that while the original strategy produced high accuracy, it suffered from instability and poor risk control. By refining the approach, we demonstrate how discretionary insights can be adapted into more robust, algorithmic trading strategies.

This article explains how to deal with one of the most difficult programming situations you can encounter: using different types in the same function or procedure template. Although we have spent most of our time focusing only on functions, everything covered here is useful and can be applied to procedures.

We created a log suppression system in the Logify library. It details how the CLogifySuppression class reduces console noise by applying configurable rules to avoid repetitive or irrelevant messages. We also cover the external configuration framework, validation mechanisms, and comprehensive testing to ensure robustness and flexibility in log capture during bot or indicator development.

The article considers an optimized version of the Market Profile indicator, where rendering with multiple graphical objects is replaced with rendering on a canvas - an object of the CCanvas class.

Upgrade your market reading with the Candle-Range Theory suite for MetaTrader 5, a fully MQL5-native solution that converts raw price bars into real-time volatility intelligence. The lightweight CRangePattern library benchmarks each candle’s true range against an adaptive ATR and classifies it the instant it closes; the CRT Indicator then projects those classifications on your chart as crisp, color-coded rectangles and arrows that reveal tightening consolidations, explosive breakouts, and full-range engulfment the moment they occur.

In this article, we continue our exploration of building an ensemble of trading strategies and using the MT5 genetic optimizer to tune the strategy parameters. Today, we analyzed the data in Python, showing our model could better predict which strategy would outperform, achieving higher accuracy than forecasting market returns directly. However, when we tested our application with its statistical models, our performance levels fell dismally. We subsequently discovered that the genetic optimizer unfortunately favored highly correlated strategies, prompting us to revise our method to keep vote weights fixed and focus optimization on indicator settings instead.

This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our discussion here sets the stage for developing statistical models to evaluate and improve trading strategies based on our initial ensemble results.

This article is meant as a guide for those unfamiliar with the concept of Singular Spectrum Analysis and who wish to gain enough understanding to be able to apply the built-in tools available in MQL5.

In this article, we will modify the last code shown in this series about Chart Trade. These changes are necessary to adapt the code to the current replay/simulation system model. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.