Articles on trading system automation in MQL5

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.

Hidden Markov Models (HMMs) are a powerful class of probabilistic models designed to analyze sequential data, where observed events depend on some sequence of unobserved (hidden) states that form a Markov process. The main assumptions of HMM include the Markov property for hidden states, meaning that the probability of transition to the next state depends only on the current state, and the independence of observations given knowledge of the current hidden state.

We continue to discuss the family of Decision Transformer methods. From previous article, we have already noticed that training the transformer underlying the architecture of these methods is a rather complex task and requires a large labeled dataset for training. In this article we will look at an algorithm for using unlabeled trajectories for preliminary model training.

This article presents a Time-of-Day capital rotation engine for MQL5 that allocates risk by trading session instead of using uniform exposure. We detail session budgets within a daily risk cap, dynamic lot sizing from remaining session risk, and automatic daily resets. Execution uses session-specific breakout and fade logic with ATR-based volatility confirmation. Readers gain a practical template to deploy capital where session conditions are statistically strongest while keeping exposure controlled throughout the day.

Today, we explore another component of ALGLIB, leveraging its mathematical capabilities to develop a Polynomial Regression Channel indicator. By the end of this discussion, you will gain practical insights into indicator development using the MQL5 Standard Library, along with a fully functional, mathematically driven indicator source code.

The article presents the Artificial Showering Algorithm (ASHA), a new metaheuristic method developed for solving general optimization problems. Based on simulation of water flow and accumulation processes, this algorithm constructs the concept of an ideal field, in which each unit of resource (water) is called upon to find an optimal solution. We will find out how ASHA adapts flow and accumulation principles to efficiently allocate resources in a search space, and see its implementation and test results.

Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.

This article tests three common filters on a standard MACD crossover for US_TECH100 H1 using five years of broker-native data. Filters are layered incrementally: regime, higher timeframe (HTF) alignment, and US session timing, to isolate each one's marginal impact. Results show session timing contributes far more than indicator refinements, while regime and HTF add little on their own. Includes a reproducible MQL5 regime classifier.

In this article, we present the Arithmetic Optimization Algorithm (AOA) based on simple arithmetic operations: addition, subtraction, multiplication and division. These basic mathematical operations serve as the foundation for finding optimal solutions to various problems.

We design a simple external trade analytics pipeline for MetaTrader 5 and implement its backend in Python with Flask and SQLite. The article defines the architecture, data model, and versioned API, and shows how to configure the environment, initialize the database, and run the server locally. As a result, you get a clean base to capture closed-trade records from MetaTrader 5 and store them for later analysis.

In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.

Preprocessing is a powerful yet quickly overlooked tuning parameter. It lives in the shadows of its bigger brothers: optimizers and shiny model architectures. Small percentage improvements here can have disproportionately large, compounding effects on profitability and risk. Too often, this largely unexplored science is boiled down to a simple routine, seen only as a means to an end, when in reality it is where signal can be directly amplified, or just as easily destroyed.

One point to note: although the service code is not included in this article and will only be provided in the next one, I'll explain it since we'll be using that same code as a springboard for what we're actually developing. So, be attentive and patient. Wait for the next article, because every day everything becomes more interesting.

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.

This article shows how to configure a black-box model to automatically uncover strong trading strategies using a data-driven approach. By using Mutual Information to prioritize the most learnable signals, we can build smarter and more adaptive models that outperform conventional methods. Readers will also learn to avoid common pitfalls like overreliance on surface-level metrics, and instead develop strategies rooted in meaningful statistical insight.

In this article, we explore vector-based methods for drawing rounded rectangles and triangles in MQL5 using canvas, with supersampling for anti-aliased rendering. We implement scanline filling, geometric precomputations for arcs and tangents, and border drawing to create smooth, customizable shapes. This approach lays the groundwork for modern UI elements in future trading tools, supporting inputs for sizes, radii, borders, and opacities.

In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.

The main purpose of this article is to introduce and explain the C_ChartFloatingRAD class. We have a Chart Trade indicator that works in a rather interesting way. As you may have noticed, we still have a fairly small number of objects on the chart, and yet we get the expected functionality. The values present in the indicator can be edited. The question is, how is this possible? This article will start to make things clearer.

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.

We introduce an MQL5 discipline engine that enforces risk consistently at the account level. It continuously scans positions from any source, validates SL/TP, equity-based exposure, and target R:R, and automatically corrects deviations by setting levels or adjusting volume. The result is uniform risk structure across manual and EA trades, supported by on-chart feedback and mode-based control.

Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.

In this article, we will complete the first phase of construction. Although this part is fairly quick to complete, I will cover details that were not discussed previously. I will explain some points that many do not understand. Do you know why you have to press the Shift or Ctrl key?