MQL4 and MQL5 Programming Articles

This article is about moral expectation. We will look at several examples of its use in trading, as well as the results that can be achieved with its help.

There is still one task which our order system is not up to, but we will FINALLY figure it out. The MetaTrader 5 provides a system of tickets which allows creating and correcting order values. The idea is to have an Expert Advisor that would make the same ticket system faster and more efficient.

We continue to consider the clustering method. In this article, we will create a new CKmeans class to implement one of the most common k-means clustering methods. During tests, the model managed to identify about 500 patterns.

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.

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.

Today we are going to use Chart Trade again, but this time it will be an on-chart indicator which may or may not be present on the chart.

Learn about the object-oriented programming paradigm and its application in MQL5 code. This second article goes deeper into the specifics of object-oriented programming, offering hands-on experience through a practical example. You'll learn how to convert our earlier developed procedural price action expert advisor using the EMA indicator and candlestick price data to object-oriented code.

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.

We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks.

This article presents the design and MetaTrader 5 implementation of the Candle Pressure Index (CPI)—a CLV-based overlay that visualizes intra-Bar buying and selling pressure directly on price charts. The discussion focuses on candle structure, pressure classification, visualization mechanics, and a non-repainting, transition-based alert system designed for consistent behavior across timeframes and instruments.

In this article, we will look at standard indicators of the Volume and Bill Williams' indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.

Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.

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.

We will consider the creation and updating of USD index (USDX) and EUR index (EURX) charts using a MetaTrader 5 service as an example. When launching the service, we will check for the presence of the required synthetic instrument, create it if necessary, and place it in the Market Watch window. The minute and tick history of the synthetic instrument is to be created afterwards followed by the chart of the created instrument.

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.

This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.

Implementation of the Logger class for unifying and structuring messages which are printed to the Experts log. Connection to the Seq log collection and analysis system. Monitoring log messages online.

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.

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

Larry Connors is a renowned trader and author, best known for his work in quantitative trading and strategies like the 2-period RSI (RSI2), which helps identify short-term overbought and oversold market conditions. In this article, we’ll first explain the motivation behind our research, then recreate three of Connors’ most famous strategies in MQL5 and apply them to intraday trading of the S&P 500 index CFD.

In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.

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 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 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.