Articles on the MQL5 programming and use of trading robots
Expert Advisors created for the MetaTrader platform perform a variety of functions implemented by their developers. Trading robots can track financial symbols 24 hours a day, copy deals, create and send reports, analyze news and even provide specific custom graphical interface.
The articles describe programming techniques, mathematical ideas for data processing, tips on creating and ordering of trading robots.
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Neural Networks in Trading: Hyperbolic Latent Diffusion Model (Final Part)
The use of anisotropic diffusion processes for encoding the initial data in a hyperbolic latent space, as proposed in the HypDIff framework, assists in preserving the topological features of the current market situation and improves the quality of its analysis. In the previous article, we started implementing the proposed approaches using MQL5. Today we will continue the work we started and will bring it to its logical conclusion.
Pure implementation of RSA encryption in MQL5
MQL5 lacks built-in asymmetric cryptography, making secure data exchange over insecure channels like HTTP difficult. This article presents a pure MQL5 implementation of RSA using PKCS#1 v1.5 padding, enabling safe transmission of AES session keys and small data blocks without external libraries. This approach provides HTTPS-like security over standard HTTP and even more, it fills an important gap in secure communication for MQL5 applications.
Neural networks made easy (Part 47): Continuous action space
In this article, we expand the range of tasks of our agent. The training process will include some aspects of money and risk management, which are an integral part of any trading strategy.
Timeseries in DoEasy library (part 57): Indicator buffer data object
In the article, develop an object which will contain all data of one buffer for one indicator. Such objects will be necessary for storing serial data of indicator buffers. With their help, it will be possible to sort and compare buffer data of any indicators, as well as other similar data with each other.
Neural Networks in Trading: An Agent with Layered Memory (Final Part)
We continue our work on creating the FinMem framework, which uses layered memory approaches that mimic human cognitive processes. This allows the model not only to effectively process complex financial data but also to adapt to new signals, significantly improving the accuracy and effectiveness of investment decisions in dynamically changing markets.
Practicing the development of trading strategies
In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.
Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (Final Part)
SAMformer offers a solution to the key drawbacks of Transformer models in long-term time series forecasting, such as training complexity and poor generalization on small datasets. Its shallow architecture and sharpness-aware optimization help avoid suboptimal local minima. In this article, we will continue to implement approaches using MQL5 and evaluate their practical value.
MQL5 Wizard Techniques you should know (Part 17): Multicurrency Trading
Trading across multiple currencies is not available by default when an expert advisor is assembled via the wizard. We examine 2 possible hacks traders can make when looking to test their ideas off more than one symbol at a time.
MQL5 Wizard Techniques you should know (Part 46): Ichimoku
The Ichimuko Kinko Hyo is a renown Japanese indicator that serves as a trend identification system. We examine this, on a pattern by pattern basis, as has been the case in previous similar articles, and also assess its strategies & test reports with the help of the MQL5 wizard library classes and assembly.
MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference
Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.
Mastering PD Arrays: Optimizing Trading from Imbalances in PD Arrays
This is an article about a specialized trend-following EA that aims to clearly elaborate how to frame and utilize trading setups that occur from imbalances found in PD arrays. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing PD arrays and imbalances as entry criteria for their trades and trading decisions. It will also explore how to correctly determine and profile premium and discount arrays and how to validate and utilize each of them when they occur in their respective market conditions, thus trying to maximize opportunities that occur from such scenarios.
Developing a multi-currency Expert Advisor (Part 5): Variable position sizes
In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.
Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?
Convolutional Neural Networks (CNNs) are renowned for their prowess in detecting patterns in images and videos, with applications spanning diverse fields. In this article, we explore the potential of CNNs to identify valuable patterns in financial markets and generate effective trading signals for MetaTrader 5 trading bots. Let us discover how this deep machine learning technique can be leveraged for smarter trading decisions.
Neural Networks in Trading: Using Language Models for Time Series Forecasting
We continue to study time series forecasting models. In this article, we get acquainted with a complex algorithm built on the use of a pre-trained language model.
Category Theory in MQL5 (Part 20): A detour to Self-Attention and the Transformer
We digress in our series by pondering at part of the algorithm to chatGPT. Are there any similarities or concepts borrowed from natural transformations? We attempt to answer these and other questions in a fun piece, with our code in a signal class format.
Utilizing CatBoost Machine Learning model as a Filter for Trend-Following Strategies
CatBoost is a powerful tree-based machine learning model that specializes in decision-making based on stationary features. Other tree-based models like XGBoost and Random Forest share similar traits in terms of their robustness, ability to handle complex patterns, and interpretability. These models have a wide range of uses, from feature analysis to risk management. In this article, we're going to walk through the procedure of utilizing a trained CatBoost model as a filter for a classic moving average cross trend-following strategy.
Neural Networks in Trading: A Multi-Agent Self-Adaptive Model (Final Part)
In the previous article, we introduced the multi-agent self-adaptive framework MASA, which combines reinforcement learning approaches and self-adaptive strategies, providing a harmonious balance between profitability and risk in turbulent market conditions. We have built the functionality of individual agents within this framework. In this article, we will continue the work we started, bringing it to its logical conclusion.
Developing a trading Expert Advisor from scratch (Part 26): Towards the future (I)
Today we will take our order system to the next level. But before that, we need to solve a few problems. Now we have some questions that are related to how we want to work and what things we do during the trading day.
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part I)
We will breakdown the main MQL5 code into specified code snippets to illustrate the integration of Telegram and WhatsApp for receiving signal notifications from the Trend Constraint indicator we are creating in this article series. This will help traders, both novices and experienced developers, grasp the concept easily. First, we will cover the setup of MetaTrader 5 for notifications and its significance to the user. This will help developers in advance to take notes to further apply in their systems.
From Novice to Expert: Parameter Control Utility
Imagine transforming the traditional EA or indicator input properties into a real-time, on-chart control interface. This discussion builds upon our foundational work in the Market Periods Synchronizer indicator, marking a significant evolution in how we visualize and manage higher-timeframe (HTF) market structures. Here, we turn that concept into a fully interactive utility—a dashboard that brings dynamic control and enhanced multi-period price action visualization directly onto the chart. Join us as we explore how this innovation reshapes the way traders interact with their tools.
Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators
Did you know that we can gain more accuracy forecasting certain technical indicators than predicting the underlying price of a traded symbol? Join us to explore how to leverage this insight for better trading strategies.
Mastering Kagi Charts in MQL5 (Part I): Creating the Indicator
Learn how to build a complete Kagi Chart engine in MQL5—constructing price reversals, generating dynamic line segments, and updating Kagi structures in real time. This first part teaches you how to render Kagi charts directly on MetaTrader 5, giving traders a clear view of trend shifts and market strength while preparing for automated Kagi-based trading logic in Part 2.
Developing a multi-currency Expert Advisor (Part 14): Adaptive volume change in risk manager
The previously developed risk manager contained only basic functionality. Let's try to consider possible ways of its development, allowing us to improve trading results without interfering with the logic of trading strategies.
Chaos theory in trading (Part 1): Introduction, application in financial markets and Lyapunov exponent
Can chaos theory be applied to financial markets? In this article, we will consider how conventional Chaos theory and chaotic systems are different from the concept proposed by Bill Williams.
Neural Networks Made Easy (Part 93): Adaptive Forecasting in Frequency and Time Domains (Final Part)
In this article, we continue the implementation of the approaches of the ATFNet model, which adaptively combines the results of 2 blocks (frequency and time) within time series forecasting.
Neural Networks in Trading: Transformer with Relative Encoding
Self-supervised learning can be an effective way to analyze large amounts of unlabeled data. The efficiency is provided by the adaptation of models to the specific features of financial markets, which helps improve the effectiveness of traditional methods. This article introduces an alternative attention mechanism that takes into account the relative dependencies and relationships between inputs.
Price Action Analysis Toolkit Development (Part 60): Objective Swing-Based Trendlines for Structural Analysis
We present a rule-based approach to trendlines that avoids indicator pivots and uses ordered swings derived from raw prices. The article walks through swing detection, size qualification via ATR or fixed thresholds, and validation of ascending and descending structures, then implements these rules in MQL5 with non-repainting drawing and selective output. You get a clear, repeatable way to track structural support and resistance that holds up across market conditions.
Neural Networks Made Easy (Part 92): Adaptive Forecasting in Frequency and Time Domains
The authors of the FreDF method experimentally confirmed the advantage of combined forecasting in the frequency and time domains. However, the use of the weight hyperparameter is not optimal for non-stationary time series. In this article, we will get acquainted with the method of adaptive combination of forecasts in frequency and time domains.
Price Action Analysis Toolkit Development (Part 57): Developing a Market State Classification Module in MQL5
This article develops a market state classification module for MQL5 that interprets price behavior using completed price data. By examining volatility contraction, expansion, and structural consistency, the tool classifies market conditions as compression, transition, expansion, or trend, providing a clear contextual framework for price action analysis.
Neural networks made easy (Part 34): Fully Parameterized Quantile Function
We continue studying distributed Q-learning algorithms. In previous articles, we have considered distributed and quantile Q-learning algorithms. In the first algorithm, we trained the probabilities of given ranges of values. In the second algorithm, we trained ranges with a given probability. In both of them, we used a priori knowledge of one distribution and trained another one. In this article, we will consider an algorithm which allows the model to train for both distributions.
Developing a multi-currency Expert Advisor (Part 21): Preparing for an important experiment and optimizing the code
For further progress it would be good to see if we can improve the results by periodically re-running the automatic optimization and generating a new EA. The stumbling block in many debates about the use of parameter optimization is the question of how long the obtained parameters can be used for trading in the future period while maintaining the profitability and drawdown at the specified levels. And is it even possible to do this?
Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN)
In this article, I will get acquainted with the GTGAN algorithm, which was introduced in January 2024 to solve complex problems of generation architectural layouts with graph constraints.
MQL5 Wizard Techniques you should know (Part 56): Bill Williams Fractals
The Fractals by Bill Williams is a potent indicator that is easy to overlook when one initially spots it on a price chart. It appears too busy and probably not incisive enough. We aim to draw away this curtain on this indicator by examining what its various patterns could accomplish when examined with forward walk tests on all, with wizard assembled Expert Advisor.
Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
MQL5 Wizard Techniques you should know (Part 75): Using Awesome Oscillator and the Envelopes
The Awesome Oscillator by Bill Williams and the Envelopes Channel are a pairing that could be used complimentarily within an MQL5 Expert Advisor. We use the Awesome Oscillator for its ability to spot trends, while the envelopes channel is incorporated to define our support/resistance levels. In exploring this indicator pairing, we use the MQL5 wizard to build and test any potential these two may possess.
Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis
In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.
Price Action Analysis Toolkit Development (Part 54): Filtering Trends with EMA and Smoothed Price Action
This article explores a method that combines Heikin‑Ashi smoothing with EMA20 High and Low boundaries and an EMA50 trend filter to improve trade clarity and timing. It demonstrates how these tools can help traders identify genuine momentum, filter out noise, and better navigate volatile or trending markets.
Trading with the MQL5 Economic Calendar (Part 4): Implementing Real-Time News Updates in the Dashboard
This article enhances our Economic Calendar dashboard by implementing real-time news updates to keep market information current and actionable. We integrate live data fetching techniques in MQL5 to update events on the dashboard continuously, improving the responsiveness of the interface. This update ensures that we can access the latest economic news directly from the dashboard, optimizing trading decisions based on the freshest data.
Neural Networks in Trading: State Space Models
A large number of the models we have reviewed so far are based on the Transformer architecture. However, they may be inefficient when dealing with long sequences. And in this article, we will get acquainted with an alternative direction of time series forecasting based on state space models.
Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model
A multi-task learning framework based on ResNeXt optimizes the analysis of financial data, taking into account its high dimensionality, nonlinearity, and time dependencies. The use of group convolution and specialized heads allows the model to effectively extract key features from the input data.