Articles with MQL5 programming examples

In this article, I will teach you basic file operations and how to configure a flexible handler for customization. We will update the CLogifyHandlerFile class to write logs directly to the file. We will conduct a performance test by simulating a strategy on EURUSD for a week, generating logs at each tick, with a total time of 5 minutes and 11 seconds. The result will be compared in a future article, where we will implement a caching system to improve performance.

In this article, we continue our exploration of ensemble models by discussing the concept of gates, specifically how they may be useful in combining model outputs to enhance either prediction accuracy or model generalization.

An inverse fair value gap(IFVG) occurs when price returns to a previously identified fair value gap and, instead of showing the expected supportive or resistive reaction, fails to respect it. This failure can signal a potential shift in market direction and offer a contrarian trading edge. In this article, I'm going to introduce my self-developed approach to quantifying and utilizing inverse fair value gap as a strategy for MetaTrader 5 expert advisors.

Successfully employing algorithmic trading requires continuous, interdisciplinary learning. However, the infinite range of possibilities can consume years of effort without yielding tangible results. To address this, we propose a framework that gradually introduces complexity, allowing traders to refine their strategies iteratively rather than committing indefinite time to uncertain outcomes.

Many beginning programmers have a hard time understanding why their code doesn't work as they expect. There are many things that make code truly functional. It's not just a bunch of different functions and operations that make the code work. Today I invite you to learn how to properly create real code, rather than copy and paste fragments of it. The materials presented here are for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

Learn how to complete the creation of the final module in the History Manager EX5 library, focusing on the functions responsible for handling the most recently canceled pending order. This will provide you with the tools to efficiently retrieve and store key details related to canceled pending orders with MQL5.

Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.

Building DLL-free cryptocurrency exchange integrations has long been a challenge, but this solution provides a complete framework for direct market connectivity.

The liquidity grab trading strategy is a key component of Smart Money Concepts (SMC), which seeks to identify and exploit the actions of institutional players in the market. It involves targeting areas of high liquidity, such as support or resistance zones, where large orders can trigger price movements before the market resumes its trend. This article explains the concept of liquidity grab in detail and outlines the development process of the liquidity grab trading strategy Expert Advisor in MQL5.

In this article, we will explore how to create and apply log formatters in the library. We will see everything from the basic structure of a formatter to practical implementation examples. By the end, you will have the necessary knowledge to format logs within the library, and understand how everything works behind the scenes.

Although the modules already interact with each other properly, an error occurs when trying to use the mouse pointer in the replay service. We need to fix this before moving on to the next step. Additionally, we will fix an issue in the mouse indicator code. So this version will be finally stable and properly polished.

Learn how to create an EX5 module of exportable functions that seamlessly query and save data for the most recently filled pending order. In this comprehensive step-by-step guide, we will enhance the History Management EX5 library by developing dedicated and compartmentalized functions to retrieve essential properties of the last filled pending order. These properties include the order type, setup time, execution time, filling type, and other critical details necessary for effective pending orders trade history management and analysis.

In this article, we will implement a control indicator so that it can be integrated into the message system we are developing. Although it is not very difficult, there are some details that need to be understood about the initialization of this module. The material presented here is for educational purposes only. In no way should it be considered as an application for any purpose other than learning and mastering the concepts shown.

As the year approaches its end, long-term traders often reflect on market history to analyze its behavior and trends, aiming to project potential future movements. In this article, we will explore the development of a long-term entry monitoring Expert Advisor (EA) using MQL5. The objective is to address the challenge of missed long-term trading opportunities caused by manual trading and the absence of automated monitoring systems. We'll use one of the most prominently traded pairs as an example to strategize and develop our solution effectively.

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.

Moving average cross-overs are widely known by traders in our community, and yet the core of the strategy has changed very little since its inception. In this discussion, we will present you with a slight adjustment to the original strategy, that aims to minimize the lag present in the trading strategy. All fans of the original strategy, could consider revising the strategy in accordance with the insights we will discuss today. By using 2 moving averages with the same period, we reduce the lag in the trading strategy considerably, without violating the foundational principles of the strategy.

The article presents an artificial electric field algorithm (AEFA) inspired by Coulomb's law of electrostatic force. The algorithm simulates electrical phenomena to solve complex optimization problems using charged particles and their interactions. AEFA exhibits unique properties in the context of other algorithms related to laws of nature.

Discover how to create exportable EX5 functions to efficiently query and save historical position data. In this step-by-step guide, we will expand the History Management EX5 library by developing modules that retrieve key properties of the most recently closed position. These include net profit, trade duration, pip-based stop loss, take profit, profit values, and various other important details.

Exploring advanced techniques to integrate MQL5 with powerful data processing tools, this part focuses on efficient handling of big data to enhance trading analysis and decision-making.

Join us today as we challenge ourselves to build a trading strategy around the USDJPY pair. We will trade candlestick patterns that are formed on the daily time frame because they potentially have more strength behind them. Our initial strategy was profitable, which encouraged us to continue refining the strategy and adding extra layers of safety, to protect the capital gained.

The article reveals the potential of the ANS algorithm as an important step in the development of flexible and intelligent optimization methods that can take into account the specifics of the problem and the dynamics of the environment in the search space.

Welcome to the third installment of our trend series! Today, we’ll delve into the use of divergence as a strategy for identifying optimal entry points within the prevailing daily trend. We’ll also introduce a custom profit-locking mechanism, similar to a trailing stop-loss, but with unique enhancements. In addition, we’ll upgrade the Trend Constraint Expert to a more advanced version, incorporating a new trade execution condition to complement the existing ones. As we move forward, we’ll continue to explore the practical application of MQL5 in algorithmic development, providing you with more in-depth insights and actionable techniques.

Determining market direction can be straightforward, but knowing when to enter can be challenging. As part of the series titled "Price Action Analysis Toolkit Development", I am excited to introduce another tool that provides entry points, take profit levels, and stop loss placements. To achieve this, we have utilized the MQL5 programming language. Let’s delve into each step in this article.

In this article, we will look at how to put together the first of a number of truly functional modules for use in the replay/simulator system that will also be of general purpose to serve other purposes. We are talking about the mouse module.

Today, we delve into incorporating useful trading metrics within a specialized window integrated into the Admin Panel EA. This discussion focuses on the implementation of MQL5 to develop an Analytics Panel and highlights the value of the data it provides to trading administrators. The impact is largely educational, as valuable lessons are drawn from the development process, benefiting both upcoming and experienced developers. This feature demonstrates the limitless opportunities this development series offers in equipping trade managers with advanced software tools. Additionally, we'll explore the implementation of the PieChart and ChartCanvas classes as part of the continued expansion of the Trading Administrator panel’s capabilities.

Learn how to retrieve, process, classify, sort, analyze, and manage closed positions, orders, and deal histories using MQL5 by creating an expansive History Management EX5 Library in a detailed step-by-step approach.

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.

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.

Welcome to the beginning of another journey! This article opens a special series where we will create, step by step, a library for log manipulation, tailored for those who develop in the MQL5 language.

Security prompts, such as those triggered every time you refresh the chart, add a new pair to the chat with the Admin Panel EA, or restart the terminal, can become tedious. In this discussion, we will explore and implement a feature that tracks the number of login attempts to identify a trusted user. After a set number of failed attempts, the application will transition to an advanced login procedure, which also facilitates passcode recovery for users who may have forgotten it. Additionally, we will cover how cryptography can be effectively integrated into the Admin Panel to enhance security.

Algorithmic trading system that combines volume analysis with machine learning techniques, specifically LSTM neural networks. Unlike traditional trading approaches that primarily focus on price movements, this system emphasizes volume patterns and their derivatives to predict market movements. The methodology incorporates three main components: volume derivatives analysis (first and second derivatives), LSTM predictions for volume patterns, and traditional technical indicators.