MQL5 Programming Articles

This article continues the series for beginners. Here we will discuss the basic principles of developing Expert Advisors (EAs). We will create two EAs: the first one will trade without indicators, using pending orders, and the second one will be based on the standard MA indicator, opening deals at the current price. Here I assume that you are no longer a complete beginner and have a relatively good command of the material from the previous articles.

These advanced gradient-boosted decision tree techniques offer superior performance and flexibility, making them ideal for financial modeling and algorithmic trading. Learn how to leverage these tools to optimize your trading strategies, improve predictive accuracy, and gain a competitive edge in the financial markets.

In this article, we automate a custom market sentiment indicator that classifies market conditions into bullish, bearish, risk-on, risk-off, and neutral. The Expert Advisor delivers real-time insights into prevailing sentiment while streamlining the analysis process for current market trends or direction.

The article considers the probabilistic price field evolution equation and the upcoming price spike criterion. It also reveals the essence of price values on charts and the mechanism for the occurrence of a random walk of these values.

This article explores optimizing RSI levels and periods for better trading signals. We introduce methods to estimate optimal RSI values and automate period selection using grid search and statistical models. Finally, we implement the solution in MQL5 while leveraging Python for analysis. Our approach aims to be pragmatic and straightforward to help you solve potentially complicated problems, with simplicity.

In this discussion we will Automate Swing Extremes and the Pullback Indicator, which transforms raw lower-timeframe (LTF) price action into a structured map of market intent, precisely identifying swing highs, swing lows, and corrective phases in real time. By programmatically tracking microstructure shifts, it anticipates potential reversals before they fully unfold—turning noise into actionable insight.

The multi-currency expert advisor in this article is an expert advisor or automated trading that uses ZigZag indicator which are filtered with the Awesome Oscillator or filter each other's signals.

In this article, we will explore the application of all classification models available in the Scikit-Learn library to solve the classification task of Fisher's Iris dataset. We will attempt to convert these models into ONNX format and utilize the resulting models in MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions on the full Iris dataset.

This article outlines the design of a double moving average crossover strategy that uses signals from a higher timeframe (D1) to guide entries on a lower timeframe (M15), with stop-loss levels calculated from an intermediate risk timeframe (H4). It introduces system constants, custom enumerations, and logic for trend-following and mean-reverting modes, while emphasizing modularity and future optimization using a genetic algorithm. The approach allows for flexible entry and exit conditions, aiming to reduce signal lag and improve trade timing by aligning lower-timeframe entries with higher-timeframe trends.

Before we can even begin to make use of ML in our trading on MetaTrader 5, it’s crucial to address one of the most overlooked pitfalls—data leakage. This article unpacks how data leakage, particularly the MetaTrader 5 timestamp trap, can distort our model's performance and lead to unreliable trading signals. By diving into the mechanics of this issue and presenting strategies to prevent it, we pave the way for building robust machine learning models that deliver trustworthy predictions in live trading environments.

Knowing how to input data from the Web into an Expert Advisor is not so obvious. It is not so easy to do without understanding all the possibilities offered by MetaTrader 5.

In this article, we will explore what pair trading is and how correlation trading works. We will also create an EA for automating pair trading and add the ability to automatically optimize this trading algorithm based on historical data. In addition, as part of the project, we will learn how to calculate the differences between two pairs using the z-score.

Financial markets are typically classified as either in a range mode or a trending mode. This static view of the market may make it easier for us to trade in the short run. However, it is disconnected from the reality of the market. In this article, we look to better understand how exactly financial markets move between these 2 possible modes and how we can use our new understanding of market behavior to gain confidence in our algorithmic trading strategies.

Learn to create and customize chart objects in MQL5 using current and historical data. This project-based guide helps you visualize trades and apply MQL5 concepts practically, making it easier to build tools tailored to your trading needs.

There are quite a lot of different trading strategies. So, it might be useful to apply several strategies working in parallel to diversify risks and increase the stability of trading results. But if each strategy is implemented as a separate Expert Advisor (EA), then managing their work on one trading account becomes much more difficult. To solve this problem, it would be reasonable to implement the operation of different trading strategies within a single EA.

This phase fine-tunes your multi-pair EA to adapt trade size and risk in real time using volatility metrics like ATR boosting consistency, protection, and performance across diverse market conditions.

Fibonacci tools are among the most popular instruments used by technical analysts. In this article, we’ll build an Interactive Fibonacci EA that draws retracement and extension levels that react dynamically to price movement, delivering real‑time alerts, stylish lines, and a scrolling news‑style headline. Another key advantage of this EA is flexibility; you can manually type the high (A) and low (B) swing values directly on the chart, giving you exact control over the market range you want to analyze.

How to access online data via MetaTrader 5? There are a lot of websites and places on the web, featuring a huge amount information. What you need to know is where to look and how best to use this information.

In this article, I will make an attempt to consider some ways of building non-linear indicators and their use in trading. There are quite a few indicators in the MetaTrader trading platform that use non-linear approaches.

This article describes four methods for connecting MQL5 code to a Postgres database and provides a step-by-step tutorial for setting up a development environment for one of them, a REST API, using the Windows Subsystem For Linux (WSL). A demo app for the API is provided along with the corresponding MQL5 code to insert data and query the respective tables, as well as a demo Expert Advisor to consume this data.

This study introduces a novel methodology for the development of trend-following trading strategies. This section describes the process of annotating training data and using it to train classifiers. This process yields fully operational trading systems designed to run on MetaTrader 5.