MQL4 and MQL5 Programming Articles
Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.
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From Novice to Expert: Automating Intraday Strategies
We translate the EMA‑50 retest idea into a behavior‑driven Expert Advisor for intraday trading. The study formalizes trend bias, EMA interaction (pierce and close), reaction confirmation, and optional filters, then implements them in MQL5 with modular functions and resource‑safe handles. Visual testing in the Strategy Tester verifies signal correctness. The result is a clear template for coding discretionary bounces.
From Novice to Expert: Automating Intraday Strategies
We translate the EMA‑50 retest idea into a behavior‑driven Expert Advisor for intraday trading. The study formalizes trend bias, EMA interaction (pierce and close), reaction confirmation, and optional filters, then implements them in MQL5 with modular functions and resource‑safe handles. Visual testing in the Strategy Tester verifies signal correctness. The result is a clear template for coding discretionary bounces.
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 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.
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.
Neural networks made easy (Part 51): Behavior-Guided Actor-Critic (BAC)
The last two articles considered the Soft Actor-Critic algorithm, which incorporates entropy regularization into the reward function. This approach balances environmental exploration and model exploitation, but it is only applicable to stochastic models. The current article proposes an alternative approach that is applicable to both stochastic and deterministic models.
Neural Networks in Trading: Controlled Segmentation (Final Part)
We continue the work started in the previous article on building the RefMask3D framework using MQL5. This framework is designed to comprehensively study multimodal interaction and feature analysis in a point cloud, followed by target object identification based on a description provided in natural language.
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.
Neural Networks in Trading: Contrastive Pattern Transformer
The Contrastive Transformer is designed to analyze markets both at the level of individual candlesticks and based on entire patterns. This helps improve the quality of market trend modeling. Moreover, the use of contrastive learning to align representations of candlesticks and patterns fosters self-regulation and improves the accuracy of forecasts.
Developing a multi-currency Expert Advisor (Part 16): Impact of different quote histories on test results
The EA under development is expected to show good results when trading with different brokers. But for now we have been using quotes from a MetaQuotes demo account to perform tests. Let's see if our EA is ready to work on a trading account with different quotes compared to those used during testing and optimization.
MetaTrader 5 Machine Learning Blueprint (Part 15): How to Calibrate Profit-Taking and Stop-Loss Targets from Synthetic Data
This article applies the Optimal Trading Rule from AFML Chapter 13 to set profit targets and stop-losses without in-sample calibration. We model post-entry P&L with a discrete Ornstein–Uhlenbeck process, run a 100,000-path search, and implement Python, multiprocessing, and a Numba @njit parallel kernel (242× faster). The result is an optimal (PT, SL) under three forecast specifications, constrained by the prop-firm daily loss limit.
Build Self Optimizing Expert Advisors in MQL5 (Part 7): Trading With Multiple Periods At Once
In this series of articles, we have considered multiple different ways of identifying the best period to use our technical indicators with. Today, we shall demonstrate to the reader how they can instead perform the opposite logic, that is to say, instead of picking the single best period to use, we will demonstrate to the reader how to employ all available periods effectively. This approach reduces the amount of data discarded, and offers alternative use cases for machine learning algorithms beyond ordinary price prediction.
MQL5 Trading Tools (Part 11): Correlation Matrix Dashboard (Pearson, Spearman, Kendall) with Heatmap and Standard Modes
In this article, we build a correlation matrix dashboard in MQL5 to compute asset relationships using Pearson, Spearman, and Kendall methods over a set timeframe and bars. The system offers standard mode with color thresholds and p-value stars, plus heatmap mode with gradient visuals for correlation strengths. It includes an interactive UI with timeframe selectors, mode toggles, and a dynamic legend for efficient analysis of symbol interdependencies.
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.
DoEasy. Service functions (Part 3): Outside Bar pattern
In this article, we will develop the Outside Bar Price Action pattern in the DoEasy library and optimize the methods of access to price pattern management. In addition, we will fix errors and shortcomings identified during library tests.
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 Replay System — Market simulation (Part 10): Using only real data for Replay
Here we will look at how we can use more reliable data (traded ticks) in the replay system without worrying about whether it is adjusted or not.
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.
Elements of correlation analysis in MQL5: Pearson chi-square test of independence and correlation ratio
The article observes classical tools of correlation analysis. An emphasis is made on brief theoretical background, as well as on the practical implementation of the Pearson chi-square test of independence and the correlation ratio.
Creating Custom Indicators in MQL5 (Part 8): Adding Volume Integration for Deeper Market Profile Analysis
In this article, we enhance the hybrid Time Price Opportunity (TPO) market profile indicator in MQL5 by integrating volume data to calculate volume-based point of control, value areas, and volume-weighted average price with customizable highlighting options. The system introduces advanced features like initial balance detection, key level extension lines, split profiles, and alternative TPO characters such as squares or circles for improved visual analysis across multiple timeframes.
From "Best Pass" to Robust Solutions: Exploring the Optimization Surface in MetaTrader 5
The article examines an engineering approach to optimizing an Expert Advisor in MetaTrader 5: from collecting custom metrics through Optimization Frames to parameter surface analysis. A simple event-driven EMA/RSI model demonstrates CSV export, smoothing, and local stability assessment in Python. The goal is to find stable areas of configurations and validate them with forward optimization for reliable implementation.
Price Action Analysis Toolkit Development (Part 69): Flag Pattern Detection in MQL5
This article shows how to convert subjective flag recognition into reproducible MQL5 logic for live charts. It combines ATR-normalized pole strength, retracement limits, consolidation structure checks, breakout confirmation, and overlap control. Readers gain a workable approach that renders adaptive channels and zones, updates active setups efficiently, and provides optional alerts for newly confirmed patterns.
Creating Custom Indicators in MQL5 (Part 8): Adding Volume Integration for Deeper Market Profile Analysis
In this article, we enhance the hybrid Time Price Opportunity (TPO) market profile indicator in MQL5 by integrating volume data to calculate volume-based point of control, value areas, and volume-weighted average price with customizable highlighting options. The system introduces advanced features like initial balance detection, key level extension lines, split profiles, and alternative TPO characters such as squares or circles for improved visual analysis across multiple timeframes.
Self Optimizing Expert Advisors in MQL5 (Part 13): A Gentle Introduction To Control Theory Using Matrix Factorization
Financial markets are unpredictable, and trading strategies that look profitable in the past often collapse in real market conditions. This happens because most strategies are fixed once deployed and cannot adapt or learn from their mistakes. By borrowing ideas from control theory, we can use feedback controllers to observe how our strategies interact with markets and adjust their behavior toward profitability. Our results show that adding a feedback controller to a simple moving average strategy improved profits, reduced risk, and increased efficiency, proving that this approach has strong potential for trading applications.
Interview with Sergey Nikitin (ATC 2011)
In the second week of the competition, the Expert Advisor of Sergey Nikitin (VNIK) trading on two currency pairs EURUSD and EURJPY got featured on the very top drawing away from the rest. Multicurrency Expert Advisors always attract attention in the Championship, especially those that show good results. We talked with Sergey about the role of luck in a trader's life and his way of 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.
Interview with Matúš German (ATC 2011)
On the second day of the Championship the Expert Advisor created by Matúš German (gery18) increased its capital by 2.5 times and took the lead being ahead of all competitors. Matúš himself doubts his Expert Advisor will win, he thinks that luck and high risk were the reasons of such unexpected rise.
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.
Interview with Ilnur Khasanov (ATC 2011)
The Expert Advisor of Ilnur Khasanov (aharata) is holding its place in our TOP-10 chart of the Automated Trading Championship 2011 participants from the third week already, though Ilnur's acquaintance with Forex has started only a year ago. The idea that forms the basis of the Expert Advisor is simple but the trading robot contains self-optimization elements. Perhaps, that is the key to its survival? Besides, the author had to change the Expert Advisor planned to be submitted for the Championship...
Risk-Based Trade Placement EA with On-Chart UI (Part 1): Designing the User Interface
Learn how to build a clean and professional on-chart control panel in MQL5 for a Risk-Based Trade Placement Expert Advisor. This step-by-step guide explains how to design a functional GUI that allows traders to input trade parameters, calculate lot size, and prepare for automated order placement.
Post-Factum trading analysis: Selecting trailing stops and new stop levels in the strategy tester
We continue the topic of analyzing completed deals in the strategy tester to improve the quality of trading. Let's see how using different trailing stops can change our existing trading results.
Formulating Dynamic Multi-Pair EA (Part 6): Adaptive Spread Sensitivity for High-Frequency Symbol Switching
In this part, we will focus on designing an intelligent execution layer that continuously monitors and evaluates real-time spread conditions across multiple symbols. The EA dynamically adapts its symbol selection by enabling or disabling trading based on spread efficiency rather than fixed rules. This approach allows high-frequency multi-pair systems to prioritize cost-effective symbols.
DoEasy. Controls (Part 19): Scrolling tabs in TabControl, WinForms object events
In this article, I will create the functionality for scrolling tab headers in TabControl using scrolling buttons. The functionality is meant to place tab headers into a single line from either side of the control.
Neural Networks Made Easy (Part 86): U-Shaped Transformer
We continue to study timeseries forecasting algorithms. In this article, we will discuss another method: the U-shaped Transformer.
Developing a Replay System (Part 76): New Chart Trade (III)
In this article, we'll look at how the code of DispatchMessage, missing from the previous article, works. We will laso introduce the topic of the next article. For this reason, it is important to understand how this code works before moving on to the next topic. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
MQL5 Wizard Techniques you should know (Part 69): Using Patterns of SAR and the RVI
The Parabolic-SAR (SAR) and the Relative Vigour Index (RVI) are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered in the past, is also complementary since SAR defines the trend while RVI checks momentum. As usual, we use the MQL5 wizard to build and test any potential this indicator pairing may have.
Neural Networks Made Easy (Part 85): Multivariate Time Series Forecasting
In this article, I would like to introduce you to a new complex timeseries forecasting method, which harmoniously combines the advantages of linear models and transformers.
Matrix Factorization: A more practical modeling
You might not have noticed that the matrix modeling was a little strange, since only columns were specified, not rows and columns. This looks very strange when reading the code that performs matrix factorizations. If you were expecting to see the rows and columns listed, you might get confused when trying to factorize. Moreover, this matrix modeling method is not the best. This is because when we model matrices in this way, we encounter some limitations that force us to use other methods or functions that would not be necessary if the modeling were done in a more appropriate way.
Sigma Score Indicator for MetaTrader 5: A Simple Statistical Anomaly Detector
Build a practical MetaTrader 5 “Sigma Score” indicator from scratch and learn what it really measures: The z-score of log returns (how many standard deviations the latest move is from the recent average). The article walks through every code block in OnInit(), OnCalculate(), and OnDeinit(), then shows how to interpret thresholds (e.g., ±2) and apply the Sigma Score as a simple “market stress meter” for mean-reversion and momentum trading.
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.