Articles on MetaTrader 5 integration using MQL5

In this article, I will show how to use an indicator to track open positions on the trading server in the simplest and most practical way possible. I am doing this step by step to show that you do not necessarily have to move all of this into an Expert Advisor. Many of you have probably become used to doing that for one reason or another. In fact, that is not really justified, because as this implementation evolves, it will become clear that you can create or implement different types of indicators for this purpose.

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.

A new optimization method, the Bison Algorithm (BIA), uses two strategies, inspired by the behavior of bison, for solving continuous problems with a single objective function. The key features of BIA are two fundamental principles borrowed from the behavior of bison: the ability to move dynamically and a defensive strategy.

The article discusses the Cricket Algorithm, a metaheuristic optimization method that combines elements of the Bat Algorithm and the Firefly Algorithm with the physical laws governing the propagation of sound in the atmosphere. The algorithm simulates the behavior of crickets that navigate by the chirping of their conspecifics, using Dolbear's law and acoustic formulas to guide the search for best solutions.

In this article, we look at the process of developing a table model in MQL5 using the MVC (Model-View-Controller) architectural pattern to separate data logic, presentation, and control, enabling structured, flexible, and scalable code. We consider implementation of classes for building a table model, including the use of linked lists for storing data.

This article introduces ap.mqh, the ALGLIB port for MQL5, and demonstrates its use in multi‑asset workflows that require robust linear algebra. It covers why built-in indicators fall short, then implements polynomial regression, a rolling correlation matrix indicator, and an adaptive hedge ratio estimator using ridge regression with Cholesky. Practical code shows how to compute spread z‑scores and execute coordinated pairs trades entirely within MetaTrader 5.

What if your trading strategies could learn from each other, like real fighters? Duelist Algorithm is a new optimization method where trading system parameters literally duel for the right to be called the best.

This work presents an end-to-end pipeline: collect MetaTrader 5 data, engineer entropy/volatility/trend features, train a PyTorch classifier, and expose predictions through a Flask API. An MQL5 EA posts rolling prices each tick, receives probability and regime, and applies adaptive position sizing and stop distances. The result is a clear recipe for integrating ML inference with MetaTrader 5.

We continue developing the Connexus library. In this chapter, we explore the concept of headers in the HTTP protocol, explaining what they are, what they are for, and how to use them in requests. We cover the main headers used in communications with APIs, and show practical examples of how to configure them in the library.

A new original population-based algorithm, ECEA, is presented. Inspired by the process of water freezing, it adapts ideas from the Crystal Energy Optimizer (CEO) algorithm, which uses graph-based search, for general optimization problems. The algorithm uses a dynamic elite group, three search strategies, and a periodic diversification mechanism.

In this article, we show how to send authenticated requests to the Binance API using MQL5 to retrieve your account balance for all assets. Learn how to use your API key, server time, and signature to securely access account data, and how to save the response to a file for future use.

This article adds a regression gate to the MQL5 debugging and profiling workflow. It keeps the Part II profiler, TestLite runner, and trading math helper as contracts, then compares current profiler evidence with an accepted baseline. The workflow also adds symbol-aware assertions, compact status files, and report tables so performance drift, missing tests, and broker-assumption problems are visible before a build is accepted.

This article is the sixth part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. In this part we comment on the main changes in our first refactoring, how we arrived at a viable blueprint for our packet-building classes, how we are building PUBLISH and PUBACK packets, and the semantics behind the PUBACK Reason Codes.

We implement a symbol resolution framework that abstracts broker naming differences in MetaTrader 5. Using a persistent mapping store, layered resolution with validation, a hash-indexed registry, and a cache, it returns selectable symbols with live market data and logs unresolved cases. Practically, you can deploy the same EA across brokers and keep symbol access consistent at low runtime cost.

We are beginning to implement the connection between Excel and MetaTrader 5, but first we need to understand some key points. This way, you won't have to rack your brains trying to figure out why something works or doesn't. And before you frown at the prospect of integrating Python and Excel, let's see how we can (to some extent) control MetaTrader 5 through Excel using xlwings. What we demonstrate here will primarily focus on educational objectives. However, don't think that we can only do what will be covered here.

Downloading international monetary fund data in Python: Mining IMF data for use in macroeconomic currency strategies. How can macroeconomics help an ordinary and an algorithmic trader?

With the multifractal parameters from Part 2 in hand, this article builds the full MMAR process. We construct the multiplicative cascade for trading time, generate Fractional Brownian Motion via Davies-Harte FFT, and combine both into X(t) = B_H[theta(t)]. A 100-path Monte Carlo simulation produces the volatility forecast, which we then pit against GARCH on the same EURUSD M5 data. Does Mandelbrot's fractal architecture outforecast Engle's conditional variance framework? Part 3 of a eight-part series leading to a native MQL5 library and Expert Advisor.

How can we conveniently monitor multiple terminals running Expert Advisors, especially when they are on different computers? Let's try to create a web interface for managing the launch of MetaTrader 5 trading terminals and viewing detailed information about the operation of each instance.

In previous articles, we mentioned that sometimes we need to set a value for the ZOrder property. But why? The reason is that many pieces of code that add objects to a chart simply do not use, or more precisely do not define, a value for this property. The point is that I am not here to say what every programmer should or should not do, or how they should or should not write their code. I am here to show you, dear reader, and everyone who truly wants to understand how these processes work internally, what actually happens behind the scenes.

The article discusses the ECO optimization algorithm, which is based on ecological concepts: populations are grouped into habitats based on territorial proximity, exchange genetic material within habitats, and migrate between them. Despite its wide range of operators and elegant biological metaphor, the algorithm produced a certain result discussed below.

What if lunar cycles and seasonal patterns influence the foreign exchange markets? This article shows how to translate astrological concepts into the language of mathematics and machine learning. I built a Python system with 88 features based on astronomical cycles, trained CatBoost on 15 years of EURUSD data, and obtained some intriguing results. The code is open-source, the methods are verifiable, and the conclusions are unexpected — ancient wisdom meets gradient boosting.

This article presents a reproducible MetaTrader 5 workflow: collect history, engineer nine context features, label simulated EMA crossover trades, train with FLAML, and export to ONNX with fixed opset and plain probabilities. The Expert Advisor loads the model natively, mirrors the Python feature contract, and uses a tunable confidence threshold as a trade filter. Readers can swap signals and features to reuse the same pipeline.

Experimental evaluation on standard benchmark functions reveals the advantages and limitations of directly adapting combinatorial algorithms. The article provides a detailed description of the ECEA algorithm's mechanisms and test results.

The Dendritic Cell Algorithm (DCA) is a metaheuristic inspired by the mechanisms of the innate immune system. Dendritic cells patrol the search space, accumulate signals about the quality of positions, and reach a collective decision: whether to exploit what they have found or to continue exploration. Let's take a look at how a biological model for detecting pathogens is transformed into an optimization algorithm.

In this article, you will learn how to create a visual signal on your trading platform so you can determine directly on the chart whether a position is long or short, without having to open the Terminal. In addition, the article also explains how to implement a feature that improves the display when moving Take Profit and Stop Loss lines by hiding the horizontal line that follows the mouse cursor while these lines are being moved, to avoid confusion. The article provides practical insight into setting up market simulation systems.

In this article, we will see how to visualize a database and, from that, understand how it is structured. This is done by analyzing the database’s internal structure. Although this may seem unnecessary at first, it is fully justified if we really want to become database administrators. After all, some people make a living maintaining and designing databases.

In this turning-point article, we will begin to explore in greater depth the interaction between the applications we are developing to ensure full support for the replay/simulation system. Here we will analyze a problem that, on the one hand, is quite unpleasant, but on the other hand, is very interesting to explain and solve. The problem is this: how can we restore the take-profit and stop-loss lines after they have been deleted, and do so without using the terminal by performing the operation directly on the chart? At first glance, it seems simple. However, there are several obstacles that must be overcome.

The Dandelion Optimizer (DO) turns the simple flight of a seed carried by the wind into a mathematical search strategy. The three phases — vortex rising, drift toward the center of the population, and landing along a Lévy-flight trajectory — form an elegant metaphor that yields interesting results in practice.

In this article, we will look at how to easily implement an indicator that shows whether a position is generating a profit or a loss. The procedure is simple and effective. Even without in-depth expertise, this indicator will allow you to easily recognize when to close a position. This way, you will avoid unexpected results, since the calculation reflects the actual outcome you would get if you closed the position.