Articles on MetaTrader 5 integration using MQL5
Traders meet interesting challenges which often require an innovative approach. This category features articles that offer the most unexpected solutions for evaluating, analyzing and processing price data and trading results. The articles describe various integration solutions, including connection of databases and ICQ, use of OpenCL and social networks, use of Delphi and C#.
Read on to learn how to use specialized mathematical and neural packages, and much more. Become an author and share unique ideas with the MQL5.community members.
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Cross-validation and basics of causal inference in CatBoost models, export to ONNX format
The article proposes the method of creating bots using machine learning.
From Novice to Expert: Animated News Headline Using MQL5 (X)—Multiple Symbol Chart View for News Trading
Today we will develop a multi-chart view system using chart objects. The goal is to enhance news trading by applying MQL5 algorithms that help reduce trader reaction time during periods of high volatility, such as major news releases. In this case, we provide traders with an integrated way to monitor multiple major symbols within a single all-in-one news trading tool. Our work is continuously advancing with the News Headline EA, which now features a growing set of functions that add real value both for traders using fully automated systems and for those who prefer manual trading assisted by algorithms. Explore more knowledge, insights, and practical ideas by clicking through and joining this discussion.
Time Evolution Travel Algorithm (TETA)
This is my own algorithm. The article presents the Time Evolution Travel Algorithm (TETA) inspired by the concept of parallel universes and time streams. The basic idea of the algorithm is that, although time travel in the conventional sense is impossible, we can choose a sequence of events that lead to different realities.
Evolutionary trading algorithm with reinforcement learning and extinction of feeble individuals (ETARE)
In this article, I introduce an innovative trading algorithm that combines evolutionary algorithms with deep reinforcement learning for Forex trading. The algorithm uses the mechanism of extinction of inefficient individuals to optimize the trading strategy.
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.
Developing a multi-currency Expert Advisor (Part 20): Putting in order the conveyor of automatic project optimization stages (I)
We have already created quite a few components that help arrange auto optimization. During the creation, we followed the traditional cyclical structure: from creating minimal working code to refactoring and obtaining improved code. It is time to start clearing up our database, which is also a key component in the system we are creating.
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.
Propensity score in causal inference
The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
Connexus Helper (Part 5): HTTP Methods and Status Codes
In this article, we will understand HTTP methods and status codes, two very important pieces of communication between client and server on the web. Understanding what each method does gives you the control to make requests more precisely, informing the server what action you want to perform and making it more efficient.
Market Simulation (Part 20): First steps with SQL (III)
Although we can perform operations on a database containing about 10 records, the material is absorbed much better when we work with a file that contains more than 15 thousand records. That is, if we tried to create such a database manually, this task would be enormous. However, it is difficult to find such a database, even for educational purposes, that is available for download. But in reality, we don’t need to resort to that — we can use MetaTrader 5 to create a database for ourselves. In today's article, we will look at how to do this.
Codex Pipelines, from Python to MQL5, for Indicator Selection: A Multi-Quarter Analysis of the XLF ETF with Machine Learning
We continue our look at how the selection of indicators can be pipelined when facing a ‘none-typical’ MetaTrader asset. MetaTrader 5 is primarily used to trade forex, and that is good given the liquidity on offer, however the case for trading outside of this ‘comfort-zone’, is growing bolder with not just the overnight rise of platforms like Robinhood, but also the relentless pursuit of an edge for most traders. We consider the XLF ETF for this article and also cap our revamped pipeline with a simple MLP.
From Novice to Expert: Backend Operations Monitor using MQL5
Using a ready-made solution in trading without concerning yourself with the internal workings of the system may sound comforting, but this is not always the case for developers. Eventually, an upgrade, misperformance, or unexpected error will arise, and it becomes essential to trace exactly where the issue originates to diagnose and resolve it quickly. Today’s discussion focuses on uncovering what normally happens behind the scenes of a trading Expert Advisor, and on developing a custom dedicated class for displaying and logging backend processes using MQL5. This gives both developers and traders the ability to quickly locate errors, monitor behavior, and access diagnostic information specific to each EA.
Fibonacci in Forex (Part I): Examining the Price-Time Relationship
How does the market observe Fibonacci-based relationships? This sequence, where each subsequent number is equal to the sum of the two previous ones (1, 1, 2, 3, 5, 8, 13, 21...), not only describes the growth of the rabbit population. We will consider the Pythagorean hypothesis that everything in the world is subject to certain relationships of numbers...
Python-MetaTrader 5 Strategy Tester (Part 03): MetaTrader 5-Like Trading Operations — Handling and Managing
In this article we introduce Python-MetaTrader5-like ways of handling trading operations such as opening, closing, and modifying orders in the simulator. To ensure the simulation behaves like MetaTrader 5, a strict validation layer for trade requests is implemented, taking into account symbol trading parameters and typical brokerage restrictions.
MQL5 Wizard Techniques you should know (Part 51): Reinforcement Learning with SAC
Soft Actor Critic is a Reinforcement Learning algorithm that utilizes 3 neural networks. An actor network and 2 critic networks. These machine learning models are paired in a master slave partnership where the critics are modelled to improve the forecast accuracy of the actor network. While also introducing ONNX in these series, we explore how these ideas could be put to test as a custom signal of a wizard assembled Expert Advisor.
From Novice to Expert: Animated News Headline Using MQL5 (VIII) — Quick Trade Buttons for News Trading
While algorithmic trading systems manage automated operations, many news traders and scalpers prefer active control during high-impact news events and fast-paced market conditions, requiring rapid order execution and management. This underscores the need for intuitive front-end tools that integrate real-time news feeds, economic calendar data, indicator insights, AI-driven analytics, and responsive trading controls.
MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning
We follow up on our last article, where we introduced the indicator pair of the MACD and the OBV, by looking at how this pairing could be enhanced with Machine Learning. MACD and OBV are a trend and volume complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, 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.
From Novice to Expert: Animated News Headline Using MQL5 (I)
News accessibility is a critical factor when trading on the MetaTrader 5 terminal. While numerous news APIs are available, many traders face challenges in accessing and integrating them effectively into their trading environment. In this discussion, we aim to develop a streamlined solution that brings news directly onto the chart—where it’s most needed. We'll accomplish this by building a News Headline Expert Advisor that monitors and displays real-time news updates from API sources.
Combinatorially Symmetric Cross Validation In MQL5
In this article we present the implementation of Combinatorially Symmetric Cross Validation in pure MQL5, to measure the degree to which a overfitting may occure after optimizing a strategy using the slow complete algorithm of the Strategy Tester.
The MQL5 Standard Library Explorer (Part 3): Expert Standard Deviation Channel
In this discussion, we will develop an Expert Advisor using the CTrade and CChartObjectStdDevChannel classes, while applying several filters to enhance profitability. This stage puts our previous discussion into practical application. Additionally, I’ll introduce another simple approach to help you better understand the MQL5 Standard Library and its underlying codebase. Join the discussion to explore these concepts in action.
Graph Theory: Traversal Depth-First Search (DFS) Applied in Trading
This article applies Depth-First Search to market structure by modeling swing highs and lows as graph nodes and tracking one structural path as deeply as conditions remain valid. When a key swing is broken, the algorithm backtracks and explores an alternative branch. Readers gain a practical framework to formalize structural bias and test whether the current path aligns with targets like liquidity pools or supply and demand zones.
From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights
In today's discussion, we explore how to self-host open-source AI models and use them to generate market insights. This forms part of our ongoing effort to expand the News Headline EA, introducing an AI Insights Lane that transforms it into a multi-integration assistive tool. The upgraded EA aims to keep traders informed through calendar events, financial breaking news, technical indicators, and now AI-generated market perspectives—offering timely, diverse, and intelligent support to trading decisions. Join the conversation as we explore practical integration strategies and how MQL5 can collaborate with external resources to build a powerful and intelligent trading work terminal.
Across Neighbourhood Search (ANS)
The article reveals the potential of the ANS algorithm as an important step in the development of flexible and intelligent optimization methods that can take into account the specifics of the problem and the dynamics of the environment in the search space.
Developing a multi-currency Expert Advisor (Part 24): Adding a new strategy (I)
In this article, we will look at how to connect a new strategy to the auto optimization system we have created. Let's see what kind of EAs we need to create and whether it will be possible to do without changing the EA library files or minimize the necessary changes.
Category Theory in MQL5 (Part 10): Monoid Groups
This article continues the series on category theory implementation in MQL5. Here we look at monoid-groups as a means normalising monoid sets making them more comparable across a wider span of monoid sets and data types..
Package-based approach with KnitPkg for MQL5 development
For maximum reliability and productivity in MetaTrader products built with MQL, this article advocates a development approach based on reusable “packages” managed by KnitPkg, a project manager for MQL5/MQL4. A package can be used as a building block for other packages or as the foundation for final artifacts that run directly on the MetaTrader platform, such as EAs, indicators, and more.
MQL5 Wizard Techniques you should know (Part 21): Testing with Economic Calendar Data
Economic Calendar Data is not available for testing with Expert Advisors within Strategy Tester, by default. We look at how Databases could help in providing a work around this limitation. So, for this article we explore how SQLite databases can be used to archive Economic Calendar news such that wizard assembled Expert Advisors can use this to generate trade signals.
Blood inheritance optimization (BIO)
I present to you my new population optimization algorithm - Blood Inheritance Optimization (BIO), inspired by the human blood group inheritance system. In this algorithm, each solution has its own "blood type" that determines the way it evolves. Just as in nature where a child's blood type is inherited according to specific rules, in BIO new solutions acquire their characteristics through a system of inheritance and mutations.
Developing a multi-currency Expert Advisor (Part 10): Creating objects from a string
The EA development plan includes several stages with intermediate results being saved in the database. They can only be retrieved from there again as strings or numbers, not objects. So we need a way to recreate the desired objects in the EA from the strings read from the database.
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.
Forex arbitrage trading: Analyzing synthetic currencies movements and their mean reversion
In this article, we will examine the movements of synthetic currencies using Python and MQL5 and explore how feasible Forex arbitrage is today. We will also consider ready-made Python code for analyzing synthetic currencies and share more details on what synthetic currencies are in Forex.
Creating a Trading Administrator Panel in MQL5 (Part VI): Multiple Functions Interface (I)
The Trading Administrator's role goes beyond just Telegram communications; they can also engage in various control activities, including order management, position tracking, and interface customization. In this article, we’ll share practical insights on expanding our program to support multiple functionalities in MQL5. This update aims to overcome the current Admin Panel's limitation of focusing primarily on communication, enabling it to handle a broader range of tasks.
Developing a multi-currency Expert Advisor (Part 11): Automating the optimization (first steps)
To get a good EA, we need to select multiple good sets of parameters of trading strategy instances for it. This can be done manually by running optimization on different symbols and then selecting the best results. But it is better to delegate this work to the program and engage in more productive activities.
MQL5 Wizard Techniques you should know (Part 22): Conditional GANs
Generative Adversarial Networks are a pairing of Neural Networks that train off of each other for more accurate results. We adopt the conditional type of these networks as we look to possible application in forecasting Financial time series within an Expert Signal Class.
Population optimization algorithms: Changing shape, shifting probability distributions and testing on Smart Cephalopod (SC)
The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.
Comet Tail Algorithm (CTA)
In this article, we will look at the Comet Tail Optimization Algorithm (CTA), which draws inspiration from unique space objects - comets and their impressive tails that form when approaching the Sun. The algorithm is based on the concept of the motion of comets and their tails, and is designed to find optimal solutions in optimization problems.
Developing an MQTT client for MetaTrader 5: a TDD approach — Final
This article is the last part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. Although the library is not production-ready yet, in this part, we will use our client to update a custom symbol with ticks (or rates) sourced from another broker. Please, see the bottom of this article for more information about the library's current status, what is missing for it to be fully compliant with the MQTT 5.0 protocol, a possible roadmap, and how to follow and contribute to its development.
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.
Twitter Sentiment Analysis with Sockets
This innovative trading bot integrates MetaTrader 5 with Python to leverage real-time social media sentiment analysis for automated trading decisions. By analyzing Twitter sentiment related to specific financial instruments, the bot translates social media trends into actionable trading signals. It utilizes a client-server architecture with socket communication, enabling seamless interaction between MT5's trading capabilities and Python's data processing power. The system demonstrates the potential of combining quantitative finance with natural language processing, offering a cutting-edge approach to algorithmic trading that capitalizes on alternative data sources.
MQL5 Wizard Techniques you should know (Part 79): Using Gator Oscillator and Accumulation/Distribution Oscillator with Supervised Learning
In the last piece, we concluded our look at the pairing of the gator oscillator and the accumulation/distribution oscillator when used in their typical setting of the raw signals they generate. These two indicators are complimentary as trend and volume indicators, respectively. We now follow up that piece, by examining the effect that supervised learning can have on enhancing some of the feature patterns we had reviewed. Our supervised learning approach is a CNN that engages with kernel regression and dot product similarity to size its kernels and channels. As always, we do this in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.