MQL4 and MQL5 Programming Articles

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.

In this article, we develop an enhanced informational dashboard that upgrades the previous part by adding draggable and minimizable features for improved user interaction, while maintaining real-time monitoring of multi-symbol positions and account metrics.

Historical data is far from “trash”—it’s the foundation of any robust market analysis. In this article, we’ll take you step‑by‑step from collecting that history to using it to train a predictive model, and finally deploying that model for live price forecasts. Read on to learn how!

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.

The Gator Oscillator by Bill Williams and the Accumulation/Distribution Oscillator are another indicator pairing that could be used harmoniously within an MQL5 Expert Advisor. We use the Gator Oscillator for its ability to affirm trends, while the A/D is used to provide confirmation of the trends via checks on volume. In exploring this indicator pairing, as always, we use the MQL5 wizard to build and test out their potential.

Sometimes everything is not programmable in the MQL5 language. And even if it is possible to convert existing advanced libraries in MQL5, it would be time-consuming. This article tries to show that we can bypass Windows OS dependency by transporting tick information such as bid, ask and time with MetaTrader services to a Python application using sockets.

Many traders evaluate strategies based on short-term performance, often abandoning profitable systems too early. Long-term profitability, however, depends on positive expectancy through optimized win rate and risk-reward ratio, along with disciplined position sizing. These principles can be validated using Monte Carlo simulation in Python with back-tested metrics to assess whether a strategy is robust or likely to fail over time.

The schedule module in Python offers a simple way to schedule repeated tasks. While MQL5 lacks a built-in equivalent, in this article we’ll implement a similar library to make it easier to set up timed events in MetaTrader 5.

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.