Articles with MQL5 programming examples

In this article, we will look at the use of a trailing stop in trading. We will assess how useful and effective it is, and how it can be used. The efficiency of a trailing stop largely depends on price volatility and the selection of the stop loss level. A variety of approaches can be used to set a stop loss.

The multi-currency expert advisor in this article is an expert advisor or trading robot that uses two RSI indicators with crossing lines, the Fast RSI which crosses with the Slow RSI.

We have previously considered various types of neural networks along with their implementations. In all cases, the neural networks were trained using the gradient decent method, for which we need to choose a learning rate. In this article, I want to show the importance of a correctly selected rate and its impact on the neural network training, using examples.

This article is the beginning of a series of developments for a library called “Connexus” to facilitate HTTP requests with MQL5. The goal of this project is to provide the end user with this opportunity and show how to use this helper library. I intended to make it as simple as possible to facilitate study and to provide the possibility for future developments.

This project aims to leverage the MQL5 algorithm to develop a comprehensive set of analysis tools for MetaTrader 5. These tools—ranging from scripts and indicators to AI models and expert advisors—will automate the market analysis process. At times, this development will yield tools capable of performing advanced analyses with no human involvement and forecasting outcomes to appropriate platforms. No opportunity will ever be missed. Join me as we explore the process of building a robust market analysis custom tools' chest. We will begin by developing a simple MQL5 program that I have named, Chart Projector.

The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.

Regression is a task of predicting a real value from an unlabeled example. The so-called regression metrics are used to assess the accuracy of regression model predictions.

In Part 38, we build a production-grade MT5 monitoring panel that converts raw ticks into actionable signals. The EA buffers tick data to compute tick-level VWAP, a short-window imbalance (flow) metric, and ATR-based position sizing. It then visualizes spread, ATR, and flow with low-flicker bars. The system calculates a suggested lot size and a 1R stop, and issues configurable alerts for tight spreads, strong flow, and edge conditions. Auto-trading is intentionally disabled; the focus remains on robust signal generation and a clean user experience.

Moving from a simple trading script to a fully functioning Expert Advisor (EA) can significantly enhance your trading experience. Imagine having a system that automatically monitors your charts, performs essential calculations in the background, and provides regular updates every two hours. This EA would be equipped to analyze key metrics that are crucial for making informed trading decisions, ensuring that you have access to the most current information to adjust your strategies effectively.

In the previous article, we started considering methods aimed at improving neural network training quality. In this article, we will continue this topic and will consider another approach — batch data normalization.

This article presents Fractal Reaction System, a compact MQL5 system that converts fractal pivots into actionable market-structure signals. Using closed-bar logic to avoid repainting, the EA detects Change-of-Character (ChoCH) warnings and confirms Breaks-of-Structure (BOS), draws persistent chart objects, and logs/alerts every confirmed event (desktop, mobile and sound). Read on for the algorithm design, implementation notes, testing results and the full EA code so you can compile, test and deploy the detector yourself.

Aligned with our goal of developing practical price-action tools, this article explores the creation of an EA that detects pin bar and engulfing patterns, using RSI divergence as a confirmation trigger before generating any trading signals.

In this article, we will share insights on how to leverage MQL5 programming to pinpoint market levels—differentiating between weaker and strongest price levels. We will fully develop a working, Support and Resistance Strength Indicator (SRSI).