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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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.
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.
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.
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.
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.
Data Science and Machine Learning (Part 16): A Refreshing Look at Decision Trees
Dive into the intricate world of decision trees in the latest installment of our Data Science and Machine Learning series. Tailored for traders seeking strategic insights, this article serves as a comprehensive recap, shedding light on the powerful role decision trees play in the analysis of market trends. Explore the roots and branches of these algorithmic trees, unlocking their potential to enhance your trading decisions. Join us for a refreshing perspective on decision trees and discover how they can be your allies in navigating the complexities of financial markets.
Multiple Symbol Analysis With Python And MQL5 (Part I): NASDAQ Integrated Circuit Makers
Join us as we discuss how you can use AI to optimize your position sizing and order quantities to maximize the returns of your portfolio. We will showcase how to algorithmically identify an optimal portfolio and tailor your portfolio to your returns expectations or risk tolerance levels. In this discussion, we will use the SciPy library and the MQL5 language to create an optimal and diversified portfolio using all the data we have.
MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent
The Hurst Exponent is a measure of how much a time series auto-correlates over the long term. It is understood to be capturing the long-term properties of a time series and therefore carries some weight in time series analysis even outside of economic/ financial time series. We however, focus on its potential benefit to traders by examining how this metric could be paired with moving averages to build a potentially robust signal.
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.
Developing a Replay System — Market simulation (Part 08): Locking the indicator
In this article, we will look at how to lock the indicator while simply using the MQL5 language, and we will do it in a very interesting and amazing 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.
Neural networks made easy (Part 74): Trajectory prediction with adaptation
This article introduces a fairly effective method of multi-agent trajectory forecasting, which is able to adapt to various environmental conditions.
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (2)
Join us for our follow-up discussion, where we will merge our first two trading strategies into an ensemble trading strategy. We shall demonstrate the different schemes possible for combining multiple strategies and also how to exercise control over the parameter space, to ensure that effective optimization remains possible even as our parameter size grows.
A Generic Optimization Formulation (GOF) to Implement Custom Max with Constraints
In this article we will present a way to implement optimization problems with multiple objectives and constraints when selecting "Custom Max" in the Setting tab of the MetaTrader 5 terminal. As an example, the optimization problem could be: Maximize Profit Factor, Net Profit, and Recovery Factor, such that the Draw Down is less than 10%, the number of consecutive losses is less than 5, and the number of trades per week is more than 5.
Trading with the MQL5 Economic Calendar (Part 8): Optimizing News-Driven Backtesting with Smart Event Filtering and Targeted Logs
In this article, we optimize our economic calendar with smart event filtering and targeted logging for faster, clearer backtesting in live and offline modes. We streamline event processing and focus logs on critical trade and dashboard events, enhancing strategy visualization. These improvements enable seamless testing and refinement of news-driven trading strategies.
Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS
We invite you to get acquainted with the NAFS (Node-Adaptive Feature Smoothing) method, which is a non-parametric approach to creating node representations that does not require parameter training. NAFS extracts features of each node given its neighbors and then adaptively combines these features to form a final representation.
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.
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.
Population optimization algorithms: Differential Evolution (DE)
In this article, we will consider the algorithm that demonstrates the most controversial results of all those discussed previously - the differential evolution (DE) algorithm.
Neural Networks in Trading: Hybrid Graph Sequence Models (Final Part)
We continue exploring hybrid graph sequence models (GSM++), which integrate the advantages of different architectures, providing high analysis accuracy and efficient distribution of computing resources. These models effectively identify hidden patterns, reducing the impact of market noise and improving forecasting quality.
MQL5 Wizard Techniques you should know (Part 24): Moving Averages
Moving Averages are a very common indicator that are used and understood by most Traders. We explore possible use cases that may not be so common within MQL5 Wizard assembled Expert Advisors.
GUI: Tips and Tricks for creating your own Graphic Library in MQL
We'll go through the basics of GUI libraries so that you can understand how they work or even start making your own.
How can century-old functions update your trading strategies?
This article considers the Rademacher and Walsh functions. We will explore ways to apply these functions to financial time series analysis and also consider various applications for them in trading.
MQL5 Trading Tools (Part 9): Developing a First Run User Setup Wizard for Expert Advisors with Scrollable Guide
In this article, we develop an MQL5 First Run User Setup Wizard for Expert Advisors, featuring a scrollable guide with an interactive dashboard, dynamic text formatting, and visual controls like buttons and a checkbox allowing users to navigate instructions and configure trading parameters efficiently. Users of the program get to have insight of what the program is all about and what to do on the first run, more like an orientation model.
Alternative risk return metrics in MQL5
In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.
Introduction to MQL5 (Part 22): Building an Expert Advisor for the 5-0 Harmonic Pattern
This article explains how to detect and trade the 5-0 harmonic pattern in MQL5, validate it using Fibonacci levels, and display it on the chart.
Neural Networks in Trading: Dual Clustering of Multivariate Time Series (DUET)
The DUET framework offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data. This allows models to adapt to changes over time and improve forecasting quality by eliminating noise.
Triangular and Sawtooth Waves: Analytical Tools for Traders
Wave analysis is one of the methods used in technical analysis. This article focuses on two less conventional wave patterns: triangular and sawtooth waves. These formations underpin a number of technical indicators designed for market price analysis.
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.
Reimagining Classic Strategies (Part X): Can AI Power The MACD?
Join us as we empirically analyzed the MACD indicator, to test if applying AI to a strategy, including the indicator, would yield any improvements in our accuracy on forecasting the EURUSD. We simultaneously assessed if the indicator itself is easier to predict than price, as well as if the indicator's value is predictive of future price levels. We will furnish you with the information you need to decide whether you should consider investing your time into integrating the MACD in your AI trading strategies.
MQL5 Wizard Techniques you should know (Part 49): Reinforcement Learning with Proximal Policy Optimization
Proximal Policy Optimization is another algorithm in reinforcement learning that updates the policy, often in network form, in very small incremental steps to ensure the model stability. We examine how this could be of use, as we have with previous articles, in a wizard assembled Expert Advisor.
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.
Developing a multi-currency Expert Advisor (Part 18): Automating group selection considering forward period
Let's continue to automate the steps we previously performed manually. This time we will return to the automation of the second stage, that is, the selection of the optimal group of single instances of trading strategies, supplementing it with the ability to take into account the results of instances in the forward period.
Data Science and ML (Part 43): Hidden Patterns Detection in Indicators Data Using Latent Gaussian Mixture Models (LGMM)
Have you ever looked at the chart and felt that strange sensation… that there’s a pattern hidden just beneath the surface? A secret code that might reveal where prices are headed if only you could crack it? Meet LGMM, the Market’s Hidden Pattern Detector. A machine learning model that helps identify those hidden patterns in the market.