Articles on manual and algorithmic trading in MetaTrader 5

A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.

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.

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.

Let's discuss how we can make our Expert Advisors speech‑capable using text‑to‑speech technology, partnering Python and MQL5. After reading this article, you will walk away with a working example of an EA that speaks dynamic market information. You will master the application of TTS, the WebRequest function, and learn how Python libraries integrate with the MQL5 language to create a truly voice‑aware trading tool.

We continue studying the chaotic optimization algorithm. The second part of the article deals with the practical aspects of the algorithm implementation, its testing and conclusions.

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 this discussion, we follow up on the previously developed multi-signal Expert Advisor with the objective of exploring and applying available optimization methods. The aim is to determine whether the trading performance of the EA can be meaningfully improved through systematic optimization based on historical data.

This article shows how to build rounded speech bubbles in MQL5 by combining a rounded rectangle with a pointer triangle and controlling orientation (up, down, left, right). It details geometry precomputation, supersampled filling, rounded apex arcs, and segmented borders with an extension ratio for seamless joins. Readers get configurable code for size, radii, colors, opacity, and thickness, ready for alerts or tooltips in trading interfaces.

This article explores an accessibility-focused enhancement that goes beyond default terminal alerts by leveraging MQL5 resource management to deliver contextual voice feedback. Instead of generic tones, the indicator communicates what has occurred and why, allowing traders to understand market events without relying solely on visual observation. This approach is especially valuable for visually impaired traders, but it also benefits busy or multitasking users who prefer hands-free interaction.

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.