MQL4 and MQL5 Programming Articles

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.

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.

In this article, we develop an AB=CD Pattern EA in MQL5 that identifies bullish and bearish AB=CD harmonic patterns using pivot points and Fibonacci ratios, executing trades with precise entry, stop loss, and take-profit levels. We enhance trader insight with visual feedback through chart objects.

In this article, we explore the automation of the Cypher harmonic pattern in MQL5, detailing its detection and visualization on MetaTrader 5 charts. We implement an Expert Advisor that identifies swing points, validates Fibonacci-based patterns, and executes trades with clear graphical annotations. The article concludes with guidance on backtesting and optimizing the program for effective trading.

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.

Learn to build a Harmonic Pattern indicator in MQL5 using chart objects. Discover how to detect swing points, apply Fibonacci retracements, and automate pattern recognition.

Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders.

In the previous article "Multiple indicators on one chart" I presented the concept and the basics of how to use multiple indicators on one chart. In this article, I will provide the source code and will explain it in detail.

Unlock the secrets of MQL5 programming in our latest article! Delve into the essentials of structures, classes, and time functions, empowering your coding journey. Whether you're a beginner or an experienced developer, our guide simplifies complex concepts, providing valuable insights for mastering MQL5. Elevate your programming skills and stay ahead in the world of algorithmic trading!

As a price action observer and trader, I've noticed that when a trend is confirmed by multiple timeframes, it usually continues in that direction. What may vary is how long the trend lasts, and this depends on the type of trader you are, whether you hold positions for the long term or engage in scalping. The timeframes you choose for confirmation play a crucial role. Check out this article for a quick, automated system that helps you analyze the overall trend across different timeframes with just a button click or regular updates.

In this article, we will discuss the hybrid trading system StockFormer, which combines predictive coding and reinforcement learning (RL) algorithms. The framework uses 3 Transformer branches with an integrated Diversified Multi-Head Attention (DMH-Attn) mechanism that improves on the vanilla attention module with a multi-headed Feed-Forward block, allowing it to capture diverse time series patterns across different subspaces.

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.

In this article, we will learn how to make the MetaTrader 5 platform talk. What if we make the EA more fun? Financial market trading is often too boring and monotonous, but we can make this job less tiring. Please note that this project can be dangerous for those who experience problems such as addiction. However, in a general case, it just makes things less boring.

In the forex markets It is very challenging to predict the future trend without having an idea of the past. Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market

The Alligator Indicator, which was the brain child of Bill Williams, is a versatile trend identification indicator that yields clear signals and is often combined with other indicators. The MQL5 wizard classes and assembly allow us to test a variety of signals on a pattern basis, and so we consider this indicator as well.

Market rules are continuously evolving, and many once-reliable principles gradually lose their effectiveness. What worked in the past no longer works consistently over time. Today’s discussion focuses on probability ranges and how they can be used to navigate market irregularities. We will leverage MQL5 to develop an algorithm capable of trading effectively even in the choppiest market conditions. Join this discussion to find out more.

This article outlines a simple strategy for normalizing the market data using the daily range and training a neural network to enhance market predictions. The developed models may be used in conjunction with an existing technical analysis frameworks or on a standalone basis to assist in predicting the overall market direction. The framework outlined in this article may be further refined by any technical analyst to develop models suitable for both manual and automated trading strategies.

Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.

What is the easiest way to implement new functionality? In this article, we will take one step back and then two steps forward.

In this article, we will create an arbitration system that remains legal in the eyes of brokers, creates thousands of synthetic prices on the Forex market, analyzes them, and successfully trades for profit.

In the previous article, we created a tool for creating and editing the architecture of neural networks. Today we will continue working on this tool. We will try to make it more user friendly. This may see, top be a step away form our topic. But don't you think that a well organized workspace plays an important role in achieving the result.

The MQL5 Standard Library plays a vital role in developing trading algorithms for MetaTrader 5. In this discussion series, our goal is to master its application to simplify the creation of efficient trading tools for MetaTrader 5. These tools include custom Expert Advisors, indicators, and other utilities. We begin today by developing a trend-following Expert Advisor using the CTrade, CiMA, and CiATR classes. This is an especially important topic for everyone—whether you are a beginner or an experienced developer. Join this discussion to discover more.

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.

Join us today as we challenge ourselves to build a profitable break-out trading strategy in MQL5. We selected the EURUSD pair and attempted to trade price breakouts on the hourly timeframe. Our system had difficulty distinguishing between false breakouts and the beginning of true trends. We layered our system with filters intended to minimize our losses whilst increasing our gains. In the end, we successfully made our system profitable and less prone to false breakouts.

We continue considering association rules. In the previous article, we have discussed theoretical aspect of this type of problem. In this article, I will show the implementation of the FP Growth method using MQL5. We will also test the implemented solution using real data.