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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Category Theory in MQL5 (Part 5): Equalizers
Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.
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.
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.
Population ADAM (Adaptive Moment Estimation)
The article presents the transformation of the well-known and popular ADAM gradient optimization method into a population algorithm and its modification with the introduction of hybrid individuals. The new approach allows creating agents that combine elements of successful decisions using probability distribution. The key innovation is the formation of hybrid population individuals that adaptively accumulate information from the most promising solutions, increasing the efficiency of search in complex multidimensional spaces.
From Novice to Expert: Animated News Headline Using MQL5 (III) — Indicator Insights
In this article, we’ll advance the News Headline EA by introducing a dedicated indicator insights lane—a compact, on-chart display of key technical signals generated from popular indicators such as RSI, MACD, Stochastic, and CCI. This approach eliminates the need for multiple indicator subwindows on the MetaTrader 5 terminal, keeping your workspace clean and efficient. By leveraging the MQL5 API to access indicator data in the background, we can process and visualize market insights in real-time using custom logic. Join us as we explore how to manipulate indicator data in MQL5 to create an intelligent and space-saving scrolling insights system, all within a single horizontal lane on your trading chart.
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.
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.
From Matrices to Models: How to Build an ML Pipeline in MQL5 and Export It to ONNX
The article describes the arrangement of a coordinated ML pipeline in MetaTrader 5 with separation of roles: Python trains and exports the model to ONNX, MQL5 reproduces normalization and PCA via matrix/vector and performs inference. This approach makes the model's inputs stable and verifiable, and the MetaTrader 5 strategy tester provides metrics for analyzing the system behavior.
Developing a multi-currency Expert Advisor (Part 8): Load testing and handling a new bar
As we progressed, we used more and more simultaneously running instances of trading strategies in one EA. Let's try to figure out how many instances we can get to before we hit resource limitations.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Adaptive Malaysian Engulfing Indicator (Part 2): Optimized Retest Bar Range
The article adds a self-adaptive layer to the Malaysian Engulfing indicator by optimizing the retest bar range with a constrained brute-force search scored by MFE and MAE. It details the data model, helper routines, and an MQL5 implementation that gathers historical setups, computes excursions, and selects the best parameter. Readers learn how to remove manual tuning and run the indicator with context-appropriate settings across symbols and timeframes.
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.
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...
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.
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.
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.
MQL5 Wizard Techniques You should know (Part 86): Speeding Up Data Access with a Sparse Table for a Custom Trailing Class
We revamp our earlier articles on testing trade setups with the MQL5 Wizard by putting a bit more emphasis on input data quality, cleaning, and handling. In the earlier articles we had looked at a lot of custom signal classes, usable by the wizard, so we now shift our focus to a custom trailing class, given that exiting is also a very important part in any trading system. Our broad theme for this particular piece data-efficiency and the O(1) range-query; the core ‘tech’ is MQL5, SQLite, Python-Polars; the Algorithm is the Sparse-Table while we will seek validation from the ATR Indicator.
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 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.
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.
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.
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.
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.
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.
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.
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 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.
Sending Messages from MQL5 to Discord, Creating a Discord-MetaTrader 5 Bot
Similar to Telegram, Discord is capable of receiving information and messages in JSON format using it's communication API's, In this article, we are going to explore how you can use discord API's to send trading signals and updates from MetaTrader 5 to your Discord trading community.
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.
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..