MQL4 and MQL5 Programming Articles

The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.

Moving Average and Stochastic Oscillator are very common indicators whose collective patterns we explored in the prior article, via a supervised learning network, to see which “patterns-would-stick”. We take our analyses from that article, a step further by considering the effects' reinforcement learning, when used with this trained network, would have on performance. Readers should note our testing is over a very limited time window. Nonetheless, we continue to harness the minimal coding requirements afforded by the MQL5 wizard in showcasing this.

When people started creating the first systems capable of computing, everything required the participation of engineers, who had to know the project very well. We are talking about the dawn of computer technology, a time when there were not even terminals for programming. As it developed and more people got interested in being able to create something, new ideas and ways of programming emerged which replaced the previous-style changing of connector positions. This is when the first terminals appeared.

We continue studying the Hierarchical Vector Transformer method. In this article, we will complete the construction of the model. We will also train and test it on real historical data.

In this article, we will try to understand why programming languages like MQL5 have structures, and why in some cases structures are the ideal way to pass values between functions and procedures, while in other cases they may not be the best way to do it.

This article explores the integration of cryptography within MQL5, enhancing the security and functionality of trading algorithms. We’ll cover key cryptographic methods and their practical implementation in automated trading.

In this article, we develop a custom indicator in MQL5 for hybrid Time Price Opportunity (TPO) market profiles, supporting multiple session timeframes such as intraday, daily, weekly, monthly, and fixed periods with timezone adjustments. The indicator quantizes prices into a grid, tracks session data including highs, lows, opens, and closes, and calculates key elements like the point of control and value area based on TPO counts. It renders profiles visually on the chart with customizable colors for TPO letters, single prints, value areas, POC, and close markers, enabling detailed session analysis

In the previous last article within this series, we looked at the Atom-Motif Contrastive Transformer (AMCT) framework, which uses contrastive learning to discover key patterns at all levels, from basic elements to complex structures. In this article, we continue implementing AMCT approaches using MQL5.

In this article, we complete the first phase of developing our replay and simulation system. Dear reader, with this achievement I confirm that the system has reached an advanced level, paving the way for the introduction of new functionality. The goal is to enrich the system even further, turning it into a powerful tool for research and development of market analysis.

Reinforcement Learning is one of the three main tenets in machine learning, alongside supervised learning and unsupervised learning. It is therefore concerned with optimal control, or learning the best long-term policy that will best suit the objective function. It is with this back-drop, that we explore its possible role in informing the learning-process to an MLP of a wizard assembled Expert Advisor.

The risk of whipsaw is extremely high during the first minute following a high-impact economic news release. In that brief window, price movements can be erratic and volatile, often triggering both sides of pending orders. Shortly after the release—typically within a minute—the market tends to stabilize, resuming or correcting the prevailing trend with more typical volatility. In this section, we’ll explore an alternative approach to news trading, aiming to assess its effectiveness as a valuable addition to a trader’s toolkit. Continue reading for more insights and details in this discussion.

This is an improved chaotic optimization algorithm (COA) that combines the effects of chaos with adaptive search mechanisms. The algorithm uses a set of chaotic maps and inertial components to explore the search space. The article reveals the theoretical foundations of chaotic methods of financial optimization.

We follow up on our last article, where we introduced the indicator pair of the Ichimoku and the ADX, by looking at how this duo could be improved with Supervised Learning. Ichimoku and ADX are a support/resistance plus trend complimentary pairing. Our supervised learning approach uses a neural network that engages the Deep Spectral Mixture Kernel to fine tune the forecasts of this indicator pairing. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

We present a timer-based MQL5 EA for Opening Range Breakout aligned to NYSE hours. It screens “Stocks in Play” via opening-range relative volume, enforces price/volume/ATR minimums, sizes positions by risk, and exits at 16:00 ET. A Sharpe-ranked optimization across 30 liquid Nasdaq stocks and a single-symbol test are provided, together with backtest settings and an Excel report for verification.

The article considers a metaheuristic Artificial Ecosystem-based Optimization (AEO) algorithm, which simulates interactions between ecosystem components by creating an initial population of solutions and applying adaptive update strategies, and describes in detail the stages of AEO operation, including the consumption and decomposition phases, as well as different agent behavior strategies. The article introduces the features and advantages of this algorithm.

The Fractal Adaptive Moving Average (FrAMA) and the Force Index Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. These two indicators complement each other a little bit because FrAMA is a trend following indicator while the Force Index is a volume based oscillator. As always, we use the MQL5 wizard to rapidly explore any potential these two may have.

In this discussion, we introduce a Higher-to-Lower Timeframe Synchronizer tool designed to solve the problem of analyzing market patterns that span across higher timeframe periods. The built-in period markers in MetaTrader 5 are often limited, rigid, and not easily customizable for non-standard timeframes. Our solution leverages the MQL5 language to develop an indicator that provides a dynamic and visual way to align higher timeframe structures within lower timeframe charts. This tool can be highly valuable for detailed market analysis. To learn more about its features and implementation, I invite you to join the discussion.

In this article, we will consider the specifics of applying some trend criteria in practice. We will also try to develop several new criteria. The focus will be on the efficiency of applying these criteria to market data analysis and trading.

Trading without session awareness is like navigating without a compass—you're moving, but not with purpose. Today, we're revolutionizing how traders perceive market timing by transforming ordinary charts into dynamic geographical displays. Using MQL5's powerful visualization capabilities, we'll build a live world map that illuminates active trading sessions in real-time, turning abstract market hours into intuitive visual intelligence. This journey sharpens your trading psychology and reveals professional-grade programming techniques that bridge the gap between complex market structure and practical, actionable insight.

​This article integrates the Optuna hyperparameter optimization (HPO) backend into a unified ModelDevelopmentPipeline. It adds joint tuning of model hyperparameters and sample-weight schemes, early pruning with Hyperband, and crash-resistant SQLite study storage. The pipeline auto-detects primary vs. secondary models, prepends a fitted column-dropping preprocessor for safe inference, supports sequential bootstrapping, generates an Optuna report, and includes bid/ask and LearnedStrategy links. Readers get faster, resumable runs and deployable, self-contained models.

The article describes a complete pipeline that uses data analysis for finding low-frequency lead/lag trading opportunities. It goes into building a cross-correlation-based Lead/Lag analyser step-by-step, with special attention to the most common errors beginners may commit while developing cross-asset diffusion queries. After screening dozens of cointegrated and correlated pairs, a trading candidate pair is chosen, and its tradeability is evaluated in a pure SQL backtest. Once it is qualified, the strategy is backtested on the MetaTester for parameter optimization. The Expert Advisor with respective backtest settings and optimization inputs is provided, along with Python and SQL scripts.

Time series forecasting in trading has evolved from traditional statistical models (like ARIMA) to deep learning approaches, but both require heavy tuning and training. Inspired by advances in NLP, Google’s TimesFM introduces a pretrained “foundation model” for time series that can perform strong forecasts even without task-specific training. For traders, this is powerful because it can be efficiently fine-tuned on their own data using lightweight methods like LoRA, reducing overfitting while adapting to changing market conditions.

In this article, we will continue to connect the new strategy to the created auto optimization system. Let's look at what changes need to be made to the optimization project creation EA, as well as the second and third stage EAs.

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

The article examines the theoretical and practical aspects of the singular spectrum analysis (SSA) method, which is an efficient method of time series analysis that allows one to represent the complex structure of a series as a decomposition into simple components, such as trend, seasonal (periodic) fluctuations and noise.

The article focuses on creating a trading system with a real quantum simulator instead of mathematical analogies. The system uses 3 virtual qubits, quantum gates and superposition principles to analyze markets. It is implemented as a trading EA for MetaTrader 5 in MQL5. The main achievement is the transition from simulation to real quantum principles of financial information processing.

When we develop something in xlwings or any other package that allows reading and writing directly to Excel, we must note that all programs, functions, or procedures execute and then complete their task. They do not remain in a loop, no matter how hard we try to do things differently.

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.

The Deep-Q-Network is a reinforcement learning algorithm that engages neural networks in projecting the next Q-value and ideal action during the training process of a machine learning module. We have already considered an alternative reinforcement learning algorithm, Q-Learning. This article therefore presents another example of how an MLP trained with reinforcement learning, can be used within a custom signal class.