ALGLIB library optimization methods (Part II)
In this article, we will continue to study the remaining optimization methods from the ALGLIB library, paying special attention to their testing on complex multidimensional functions. This will allow us not only to evaluate the efficiency of each algorithm, but also to identify their strengths and weaknesses in different conditions.
MQL5 Wizard Techniques you should know (Part 68): Using Patterns of TRIX and the Williams Percent Range with a Cosine Kernel Network
We follow up our last article, where we introduced the indicator pair of TRIX and Williams Percent Range, by considering how this indicator pairing could be extended with Machine Learning. TRIX and William’s Percent are a trend and support/ resistance complimentary pairing. Our machine learning approach uses a convolution neural network that engages the cosine kernel in its architecture when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python
So far we have considered the automation of launching sequential procedures for optimizing EAs exclusively in the standard strategy tester. But what if we would like to perform some handling of the obtained data using other means between such launches? We will attempt to add the ability to create new optimization stages performed by programs written in Python.
Building MQL5-Like Trade Classes in Python for MetaTrader 5
MetaTrader 5 python package provides an easy way to build trading applications for the MetaTrader 5 platform in the Python language, while being a powerful and useful tool, this module isn't as easy as MQL5 programming language when it comes to making an algorithmic trading solution. In this article, we are going to build trade classes similar to the one offered in MQL5 to create a similar syntax and make it easier to make trading robots in Python as in MQL5.
MQL5 Wizard Techniques you should know (Part 66): Using Patterns of FrAMA and the Force Index with the Dot Product Kernel
The FrAMA Indicator and the Force Index Oscillator are trend and volume tools that could be paired when developing an Expert Advisor. We continue from our last article that introduced this pair by considering machine learning applicability to the pair. We are using a convolution neural network that uses the dot-product kernel in making forecasts with these indicators’ inputs. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Advanced Order Execution Algorithms in MQL5: TWAP, VWAP, and Iceberg Orders
An MQL5 framework that brings institutional-grade execution algorithms (TWAP, VWAP, Iceberg) to retail traders through a unified execution manager and performance analyzer for smoother, more precise order slicing and analytics.
Custom Debugging and Profiling Tools for MQL5 Development (Part I): Advanced Logging
Learn how to implement a powerful custom logging framework for MQL5 that goes beyond simple Print() statements by supporting severity levels, multiple output handlers, and automated file rotation—all configurable on‐the‐fly. Integrate the singleton CLogger with ConsoleLogHandler and FileLogHandler to capture contextual, timestamped logs in both the Experts tab and persistent files. Streamline debugging and performance tracing in your Expert Advisors with clear, customizable log formats and centralized control.
MQL5 Wizard Techniques you should know (Part 64): Using Patterns of DeMarker and Envelope Channels with the White-Noise Kernel
The DeMarker Oscillator and the Envelopes' indicator are momentum and support/ resistance tools that can be paired when developing an Expert Advisor. We continue from our last article that introduced these pair of indicators by adding machine learning to the mix. We are using a recurrent neural network that uses the white-noise kernel to process vectorized signals from these two indicators. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Economic forecasts: Exploring the Python potential
How to use World Bank economic data for forecasts? What happens when you combine AI models and economics?
Creating a Trading Administrator Panel in MQL5 (Part XI): Modern feature communications interface (I)
Today, we are focusing on the enhancement of the Communications Panel messaging interface to align with the standards of modern, high-performing communication applications. This improvement will be achieved by updating the CommunicationsDialog class. Join us in this article and discussion as we explore key insights and outline the next steps in advancing interface programming using MQL5.
Finding custom currency pair patterns in Python using MetaTrader 5
Are there any repeating patterns and regularities in the Forex market? I decided to create my own pattern analysis system using Python and MetaTrader 5. A kind of symbiosis of math and programming for conquering Forex.
MQL5 Wizard Techniques you should know (Part 62): Using Patterns of ADX and CCI with Reinforcement-Learning TRPO
The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We continue where we left off in the last article by examining how in-use training, and updating of our developed model, can be made thanks to reinforcement-learning. We are using an algorithm we are yet to cover in these series, known as Trusted Region Policy Optimization. And, as always, Expert Advisor assembly by the MQL5 Wizard allows us to set up our model(s) for testing much quicker and also in a way where it can be distributed and tested with different signal types.
MQL5 Wizard Techniques you should know (Part 61): Using Patterns of ADX and CCI with Supervised Learning
The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We look at how this can be systemized by using all the 3 main training modes of Machine Learning. Wizard Assembled Expert Advisors allow us to evaluate the patterns presented by these two indicators, and we start by looking at how Supervised-Learning can be applied with these Patterns.
Websockets for MetaTrader 5: Asynchronous client connections with the Windows API
This article details the development of a custom dynamically linked library designed to facilitate asynchronous websocket client connections for MetaTrader programs.

MQL5 Wizard Techniques you should know (Part 60): Inference Learning (Wasserstein-VAE) with Moving Average and Stochastic Oscillator Patterns
We wrap our look into the complementary pairing of the MA & Stochastic oscillator by examining what role inference-learning can play in a post supervised-learning & reinforcement-learning situation. There are clearly a multitude of ways one can choose to go about inference learning in this case, our approach, however, is to use variational auto encoders. We explore this in python before exporting our trained model by ONNX for use in a wizard assembled Expert Advisor in MetaTrader.

Building a Custom Market Regime Detection System in MQL5 (Part 1): Indicator
This article details creating an MQL5 Market Regime Detection System using statistical methods like autocorrelation and volatility. It provides code for classes to classify trending, ranging, and volatile conditions and a custom indicator.

Creating a Trading Administrator Panel in MQL5 (Part X): External resource-based interface
Today, we are harnessing the capabilities of MQL5 to utilize external resources—such as images in the BMP format—to create a uniquely styled home interface for the Trading Administrator Panel. The strategy demonstrated here is particularly useful when packaging multiple resources, including images, sounds, and more, for streamlined distribution. Join us in this discussion as we explore how these features are implemented to deliver a modern and visually appealing interface for our New_Admin_Panel EA.

Integrating AI model into already existing MQL5 trading strategy
This topic focuses on incorporating a trained AI model (such as a reinforcement learning model like LSTM or a machine learning-based predictive model) into an existing MQL5 trading strategy.

Price Action Analysis Toolkit Development (Part 20): External Flow (IV) — Correlation Pathfinder
Correlation Pathfinder offers a fresh approach to understanding currency pair dynamics as part of the Price Action Analysis Toolkit Development Series. This tool automates data collection and analysis, providing insight into how pairs like EUR/USD and GBP/USD interact. Enhance your trading strategy with practical, real-time information that helps you manage risk and spot opportunities more effectively.

Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (V): AnalyticsPanel Class
In this discussion, we explore how to retrieve real-time market data and trading account information, perform various calculations, and display the results on a custom panel. To achieve this, we will dive deeper into developing an AnalyticsPanel class that encapsulates all these features, including panel creation. This effort is part of our ongoing expansion of the New Admin Panel EA, introducing advanced functionalities using modular design principles and best practices for code organization.

Quantitative approach to risk management: Applying VaR model to optimize multi-currency portfolio using Python and MetaTrader 5
This article explores the potential of the Value at Risk (VaR) model for multi-currency portfolio optimization. Using the power of Python and the functionality of MetaTrader 5, we demonstrate how to implement VaR analysis for efficient capital allocation and position management. From theoretical foundations to practical implementation, the article covers all aspects of applying one of the most robust risk calculation systems – VaR – in algorithmic trading.

Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (IV): Trade Management Panel class
This discussion covers the updated TradeManagementPanel in our New_Admin_Panel EA. The update enhances the panel by using built-in classes to offer a user-friendly trade management interface. It includes trading buttons for opening positions and controls for managing existing trades and pending orders. A key feature is the integrated risk management that allows setting stop loss and take profit values directly in the interface. This update improves code organization for large programs and simplifies access to order management tools, which are often complex in the terminal.

MQL5 Wizard Techniques you should know (Part 59): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
We continue our last article on DDPG with MA and stochastic indicators by examining other key Reinforcement Learning classes crucial for implementing DDPG. Though we are mostly coding in python, the final product, of a trained network will be exported to as an ONNX to MQL5 where we integrate it as a resource in a wizard assembled Expert Advisor.

Advanced Memory Management and Optimization Techniques in MQL5
Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.

MQL5 Wizard Techniques you should know (Part 58): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
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.

Tabu Search (TS)
The article discusses the Tabu Search algorithm, one of the first and most well-known metaheuristic methods. We will go through the algorithm operation in detail, starting with choosing an initial solution and exploring neighboring options, with an emphasis on using a tabu list. The article covers the key aspects of the algorithm and its features.

MQL5 Wizard Techniques you should know (Part 57): Supervised Learning with Moving Average and Stochastic Oscillator
Moving Average and Stochastic Oscillator are very common indicators that some traders may not use a lot because of their lagging nature. In a 3-part ‘miniseries' that considers the 3 main forms of machine learning, we look to see if this bias against these indicators is justified, or they might be holding an edge. We do our examination in wizard assembled Expert Advisors.

Artificial Algae Algorithm (AAA)
The article considers the Artificial Algae Algorithm (AAA) based on biological processes characteristic of microalgae. The algorithm includes spiral motion, evolutionary process and adaptation, which allows it to solve optimization problems. The article provides an in-depth analysis of the working principles of AAA and its potential in mathematical modeling, highlighting the connection between nature and algorithmic solutions.

Anarchic Society Optimization (ASO) algorithm
In this article, we will get acquainted with the Anarchic Society Optimization (ASO) algorithm and discuss how an algorithm based on the irrational and adventurous behavior of participants in an anarchic society (an anomalous system of social interaction free from centralized power and various kinds of hierarchies) is able to explore the solution space and avoid the traps of local optimum. The article presents a unified ASO structure applicable to both continuous and discrete problems.

MQL5 Wizard Techniques you should know (Part 55): SAC with Prioritized Experience Replay
Replay buffers in Reinforcement Learning are particularly important with off-policy algorithms like DQN or SAC. This then puts the spotlight on the sampling process of this memory-buffer. While default options with SAC, for instance, use random selection from this buffer, Prioritized Experience Replay buffers fine tune this by sampling from the buffer based on a TD-score. We review the importance of Reinforcement Learning, and, as always, examine just this hypothesis (not the cross-validation) in a wizard assembled Expert Advisor.

MQL5 Wizard Techniques you should know (Part 54): Reinforcement Learning with hybrid SAC and Tensors
Soft Actor Critic is a Reinforcement Learning algorithm that we looked at in a previous article, where we also introduced python and ONNX to these series as efficient approaches to training networks. We revisit the algorithm with the aim of exploiting tensors, computational graphs that are often exploited in Python.

Building a Keltner Channel Indicator with Custom Canvas Graphics in MQL5
In this article, we build a Keltner Channel indicator with custom canvas graphics in MQL5. We detail the integration of moving averages, ATR calculations, and enhanced chart visualization. We also cover backtesting to evaluate the indicator’s performance for practical trading insights.

Mastering JSON: Create Your Own JSON Reader from Scratch in MQL5
Experience a step-by-step guide on creating a custom JSON parser in MQL5, complete with object and array handling, error checking, and serialization. Gain practical insights into bridging your trading logic and structured data with this flexible solution for handling JSON in MetaTrader 5.

Price Action Analysis Toolkit Development (Part 12): External Flow (III) TrendMap
The flow of the market is determined by the forces between bulls and bears. There are specific levels that the market respects due to the forces acting on them. Fibonacci and VWAP levels are especially powerful in influencing market behavior. Join me in this article as we explore a strategy based on VWAP and Fibonacci levels for signal generation.

Price Action Analysis Toolkit Development (Part 11): Heikin Ashi Signal EA
MQL5 offers endless opportunities to develop automated trading systems tailored to your preferences. Did you know it can even perform complex mathematical calculations? In this article, we introduce the Japanese Heikin-Ashi technique as an automated trading strategy.

Artificial Bee Hive Algorithm (ABHA): Theory and methods
In this article, we will consider the Artificial Bee Hive Algorithm (ABHA) developed in 2009. The algorithm is aimed at solving continuous optimization problems. We will look at how ABHA draws inspiration from the behavior of a bee colony, where each bee has a unique role that helps them find resources more efficiently.

Trend Prediction with LSTM for Trend-Following Strategies
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) designed to model sequential data by effectively capturing long-term dependencies and addressing the vanishing gradient problem. In this article, we will explore how to utilize LSTM to predict future trends, enhancing the performance of trend-following strategies. The article will cover the introduction of key concepts and the motivation behind development, fetching data from MetaTrader 5, using that data to train the model in Python, integrating the machine learning model into MQL5, and reflecting on the results and future aspirations based on statistical backtesting.

Price Action Analysis Toolkit Development (Part 10): External Flow (II) VWAP
Master the power of VWAP with our comprehensive guide! Learn how to integrate VWAP analysis into your trading strategy using MQL5 and Python. Maximize your market insights and improve your trading decisions today.

Price Action Analysis Toolkit Development (Part 9): External Flow
This article explores a new dimension of analysis using external libraries specifically designed for advanced analytics. These libraries, like pandas, provide powerful tools for processing and interpreting complex data, enabling traders to gain more profound insights into market dynamics. By integrating such technologies, we can bridge the gap between raw data and actionable strategies. Join us as we lay the foundation for this innovative approach and unlock the potential of combining technology with trading expertise.

MQL5 Trading Toolkit (Part 7): Expanding the History Management EX5 Library with the Last Canceled Pending Order Functions
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.