MQL4 and MQL5 Programming Articles

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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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The View and Controller components for tables in the MQL5 MVC paradigm: Simple controls

The View and Controller components for tables in the MQL5 MVC paradigm: Simple controls

The article covers simple controls as components of more complex graphical elements of the View component within the framework of table implementation in the MVC (Model-View-Controller) paradigm. The basic functionality of the Controller is implemented for interaction of elements with the user and with each other. This is the second article on the View component and the fourth one in a series of articles on creating tables for the MetaTrader 5 client terminal.
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Integrating MQL5 with data processing packages (Part 1): Advanced Data analysis and Statistical Processing

Integrating MQL5 with data processing packages (Part 1): Advanced Data analysis and Statistical Processing

Integration enables seamless workflow where raw financial data from MQL5 can be imported into data processing packages like Jupyter Lab for advanced analysis including statistical testing.
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MetaTrader 5 Machine Learning Blueprint (Part 10): Bet Sizing for Financial Machine Learning

MetaTrader 5 Machine Learning Blueprint (Part 10): Bet Sizing for Financial Machine Learning

Fixed fractions and raw probabilities misallocate risk under overlapping labels and induce overtrading. This article delivers four AFML-compliant sizers: probability-based (z-score → CDF, active-bet averaging, discretization), forecast-price (sigmoid/power with w calibration and limit price), budget-constrained (direction-only), and reserve (mixture-CDF via EF3M). You get a signed, bounded position series with documented conditions of use.
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Optimizing Liquidity Raids: Mastering the Difference Between Liquidity Raids and Market Structure Shifts

Optimizing Liquidity Raids: Mastering the Difference Between Liquidity Raids and Market Structure Shifts

This is an article about a specialized trend-following EA that aims to clearly elaborate how to utilize trading setups after liquidity raids. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing liquidity raids and purges as entry criteria for their trades and trading decisions. It will also explore how to correctly differentiate between liquidity raids and market structure shifts and how to validate and utilize each of them when they occur, thus trying to mitigate losses that occur from traders confusing the two.
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MQL5 Wizard Techniques you should know (Part 07): Dendrograms

MQL5 Wizard Techniques you should know (Part 07): Dendrograms

Data classification for purposes of analysis and forecasting is a very diverse arena within machine learning and it features a large number of approaches and methods. This piece looks at one such approach, namely Agglomerative Hierarchical Classification.
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Neuro-Structural Trading Engine — NSTE (Part I): How to Build a Prop-Firm-Safe Multi-Account System

Neuro-Structural Trading Engine — NSTE (Part I): How to Build a Prop-Firm-Safe Multi-Account System

This article lays the system architecture for a multi‑account algorithmic trading setup that operates cryptocurrency CFDs on MetaTrader 5 while respecting prop‑firm constraints. It defines three core principles—fixed dollar risk, one script per account, and centralized configuration—then details the Python–MQL5 split, the 60‑second processing loop, and JSON-based signaling. Readers get practical lot‑size computation, safety checks, and position management patterns for reliable deployment.
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MetaTrader 5 Machine Learning Blueprint (Part 9): Integrating Bayesian HPO into the Production Pipeline

MetaTrader 5 Machine Learning Blueprint (Part 9): Integrating Bayesian HPO into the Production Pipeline

​This article integrates the Optuna hyperparameter optimization (HPO) backend into a unified ModelDevelopmentPipeline. It adds joint tuning of model hyperparameters and sample-weight schemes, early pruning with Hyperband, and crash-resistant SQLite study storage. The pipeline auto-detects primary vs. secondary models, prepends a fitted column-dropping preprocessor for safe inference, supports sequential bootstrapping, generates an Optuna report, and includes bid/ask and LearnedStrategy links. Readers get faster, resumable runs and deployable, self-contained models.
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Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer

Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer

Imagine a malicious actor infiltrating the Trading Administrator room, gaining access to the computers and the Admin Panel used to communicate valuable insights to millions of traders worldwide. Such an intrusion could lead to disastrous consequences, such as the unauthorized sending of misleading messages or random clicks on buttons that trigger unintended actions. In this discussion, we will explore the security measures in MQL5 and the new security features we have implemented in our Admin Panel to safeguard against these threats. By enhancing our security protocols, we aim to protect our communication channels and maintain the trust of our global trading community. Find more insights in this article discussion.
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From Basic to Intermediate: The Include Directive

From Basic to Intermediate: The Include Directive

In today's article, we will discuss a compilation directive that is widely used in various codes that can be found in MQL5. Although this directive will be explained rather superficially here, it is important that you begin to understand how to use it, as it will soon become indispensable as you move to higher levels of programming. 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.
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Developing a Replay System (Part 44): Chart Trade Project (III)

Developing a Replay System (Part 44): Chart Trade Project (III)

In the previous article I explained how you can manipulate template data for use in OBJ_CHART. In that article, I only outlined the topic without going into details, since in that version the work was done in a very simplified way. This was done to make it easier to explain the content, because despite the apparent simplicity of many things, some of them were not so obvious, and without understanding the simplest and most basic part, you would not be able to truly understand the entire picture.
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Developing a Replay System (Part 66): Playing the service (VII)

Developing a Replay System (Part 66): Playing the service (VII)

In this article, we will implement the first solution that will allow us to determine when a new bar may appear on the chart. This solution is applicable in a wide variety of situations. Understanding its development will help you grasp several important aspects. 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.
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MQL5 Trading Tools (Part 22): Graphing the Histogram and Probability Mass Function (PMF) of the Binomial Distribution

MQL5 Trading Tools (Part 22): Graphing the Histogram and Probability Mass Function (PMF) of the Binomial Distribution

This article develops an interactive MQL5 plot for the binomial distribution, combining a histogram of simulated outcomes with the theoretical probability mass function. It implements mean, standard deviation, skewness, kurtosis, percentiles, and confidence intervals, along with configurable themes and labels, and supports dragging, resizing, and live parameter changes. Use it to assess expected wins, likely drawdowns, and confidence ranges when validating trading strategies.
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Developing Market Entropy Indicator: Trading System Based on Information Theory

Developing Market Entropy Indicator: Trading System Based on Information Theory

This article explores the development of a Market Entropy Indicator based on principles from Information Theory to measure the uncertainty and information content within financial markets. By applying concepts such as Shannon Entropy to price movements, the indicator quantifies whether the market is structured (trending), transitioning, or chaotic.
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MetaTrader 5: Build a Market to Suit Your Strategy — Renko/Range/Volume, Synthetics, and Stress Tests on Custom Symbols

MetaTrader 5: Build a Market to Suit Your Strategy — Renko/Range/Volume, Synthetics, and Stress Tests on Custom Symbols

In this article, we demonstrate how to use API of the MetaTrader 5 custom symbols to transform your terminal into a data constructor for generating timeless Renko, Range, and Equal-Volume charts and assembling synthetic instruments. We will analyze tick aggregation and history modification for stress tests (spread widening, stop level changes) taking into account platform limitations. Besides, you will get some practice of handling CiCustomSymbol and routing orders to a real symbol through the CustomOrder wrapper with ready-made code fragments.
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Mastering Log Records (Part 4): Saving logs to files

Mastering Log Records (Part 4): Saving logs to files

In this article, I will teach you basic file operations and how to configure a flexible handler for customization. We will update the CLogifyHandlerFile class to write logs directly to the file. We will conduct a performance test by simulating a strategy on EURUSD for a week, generating logs at each tick, with a total time of 5 minutes and 11 seconds. The result will be compared in a future article, where we will implement a caching system to improve performance.
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MQL5 Trading Tools (Part 23): Camera-Controlled, DirectX-Enabled 3D Graphs for Distribution Insights

MQL5 Trading Tools (Part 23): Camera-Controlled, DirectX-Enabled 3D Graphs for Distribution Insights

In this article, we advance the binomial distribution graphing tool in MQL5 by integrating DirectX for 3D visualization, enabling switchable 2D/3D modes with camera-controlled rotation, zoom, and auto-fitting for immersive analysis. We render 3D histogram bars, ground planes, and axes alongside the theoretical probability mass function curve, while preserving 2D elements like statistics panels, legends, and customizable themes, gradients, and labels
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Reimagining Classic Strategies (Part 13): Taking Our Crossover Strategy to New Dimensions (Part 2)

Reimagining Classic Strategies (Part 13): Taking Our Crossover Strategy to New Dimensions (Part 2)

Join us in our discussion as we look for additional improvements to make to our moving-average cross over strategy to reduce the lag in our trading strategy to more reliable levels by leveraging our skills in data science. It is a well-studied fact that projecting your data to higher dimensions can at times improve the performance of your machine learning models. We will demonstrate what this practically means for you as a trader, and illustrate how you can weaponize this powerful principle using your MetaTrader 5 Terminal.
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Category Theory in MQL5 (Part 6): Monomorphic Pull-Backs and Epimorphic Push-Outs

Category Theory in MQL5 (Part 6): Monomorphic Pull-Backs and Epimorphic Push-Outs

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.
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Creating Custom Indicators in MQL5 (Part 7): Hybrid Time Price Opportunity (TPO) Market Profiles for Session Analysis

Creating Custom Indicators in MQL5 (Part 7): Hybrid Time Price Opportunity (TPO) Market Profiles for Session Analysis

In this article, we develop a custom indicator in MQL5 for hybrid Time Price Opportunity (TPO) market profiles, supporting multiple session timeframes such as intraday, daily, weekly, monthly, and fixed periods with timezone adjustments. The indicator quantizes prices into a grid, tracks session data including highs, lows, opens, and closes, and calculates key elements like the point of control and value area based on TPO counts. It renders profiles visually on the chart with customizable colors for TPO letters, single prints, value areas, POC, and close markers, enabling detailed session analysis
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Data Science and ML (Part 45): Forex Time series forecasting using PROPHET by Facebook Model

Data Science and ML (Part 45): Forex Time series forecasting using PROPHET by Facebook Model

The Prophet model, developed by Facebook, is a robust time series forecasting tool designed to capture trends, seasonality, and holiday effects with minimal manual tuning. It has been widely adopted for demand forecasting and business planning. In this article, we explore the effectiveness of Prophet in forecasting volatility in forex instruments, showcasing how it can be applied beyond traditional business use cases.
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Feature Engineering for ML (Part 3): Session-Aware Time Features for Forex Machine Learning

Feature Engineering for ML (Part 3): Session-Aware Time Features for Forex Machine Learning

The article addresses the loss of temporal information in ML pipelines by encoding periodic time variables with Fourier harmonics and adding forex session structure. It implements session and overlap flags, lagged session volatility, and calendar effects, then prunes features by timeframe. The get time features function returns an index‑aligned, ML‑ready set of time features suitable for integration with price‑based signals.
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Population optimization algorithms: Simulated Isotropic Annealing (SIA) algorithm. Part II

Population optimization algorithms: Simulated Isotropic Annealing (SIA) algorithm. Part II

The first part was devoted to the well-known and popular algorithm - simulated annealing. We have thoroughly considered its pros and cons. The second part of the article is devoted to the radical transformation of the algorithm, which turns it into a new optimization algorithm - Simulated Isotropic Annealing (SIA).
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ALGLIB library optimization methods (Part I)

ALGLIB library optimization methods (Part I)

In this article, we will get acquainted with the ALGLIB library optimization methods for MQL5. The article includes simple and clear examples of using ALGLIB to solve optimization problems, which will make mastering the methods as accessible as possible. We will take a detailed look at the connection of such algorithms as BLEIC, L-BFGS and NS, and use them to solve a simple test problem.
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Permuting price bars in MQL5

Permuting price bars in MQL5

In this article we present an algorithm for permuting price bars and detail how permutation tests can be used to recognize instances where strategy performance has been fabricated to deceive potential buyers of Expert Advisors.
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Developing a multi-currency Expert Advisor (Part 23): Putting in order the conveyor of automatic project optimization stages (II)

Developing a multi-currency Expert Advisor (Part 23): Putting in order the conveyor of automatic project optimization stages (II)

We aim to create a system for automatic periodic optimization of trading strategies used in one final EA. As the system evolves, it becomes increasingly complex, so it is necessary to look at it as a whole from time to time in order to identify bottlenecks and suboptimal solutions.
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Forex Arbitrage Trading: Relationship Assessment Panel

Forex Arbitrage Trading: Relationship Assessment Panel

This article presents the development of an arbitrage analysis panel in MQL5. How to get fair exchange rates on Forex in different ways? Create an indicator to obtain deviations of market prices from fair exchange rates, as well as to assess the benefits of arbitrage ways of exchanging one currency for another (as in triangular arbitrage).
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Neural Network in Practice: Secant Line

Neural Network in Practice: Secant Line

As already explained in the theoretical part, when working with neural networks we need to use linear regressions and derivatives. Why? The reason is that linear regression is one of the simplest formulas in existence. Essentially, linear regression is just an affine function. However, when we talk about neural networks, we are not interested in the effects of direct linear regression. We are interested in the equation that generates this line. We are not that interested in the line created. Do you know the main equation that we need to understand? If not, I recommend reading this article to understanding it.
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Developing a Replay System (Part 49): Things Get Complicated (I)

Developing a Replay System (Part 49): Things Get Complicated (I)

In this article, we'll complicate things a little. Using what was shown in the previous articles, we will start to open up the template file so that the user can use their own template. However, I will be making changes gradually, as I will also be refining the indicator to reduce the load on MetaTrader 5.
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Developing a Replay System — Market simulation (Part 16): New class system

Developing a Replay System — Market simulation (Part 16): New class system

We need to organize our work better. The code is growing, and if this is not done now, then it will become impossible. Let's divide and conquer. MQL5 allows the use of classes which will assist in implementing this task, but for this we need to have some knowledge about classes. Probably the thing that confuses beginners the most is inheritance. In this article, we will look at how to use these mechanisms in a practical and simple way.
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Matrix Factorization: The Basics

Matrix Factorization: The Basics

Since the goal here is didactic, we will proceed as simply as possible. That is, we will implement only what we need: matrix multiplication. You will see today that this is enough to simulate matrix-scalar multiplication. The most significant difficulty that many people encounter when implementing code using matrix factorization is this: unlike scalar factorization, where in almost all cases the order of the factors does not change the result, this is not the case when using matrices.
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Neural Networks in Trading: Mask-Attention-Free Approach to Price Movement Forecasting

Neural Networks in Trading: Mask-Attention-Free Approach to Price Movement Forecasting

In this article, we will discuss the Mask-Attention-Free Transformer (MAFT) method and its application in the field of trading. Unlike traditional Transformers that require data masking when processing sequences, MAFT optimizes the attention process by eliminating the need for masking, significantly improving computational efficiency.
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MQL5 Wizard Techniques you should know (Part 82): Using Patterns of TRIX and the WPR with DQN Reinforcement Learning

MQL5 Wizard Techniques you should know (Part 82): Using Patterns of TRIX and the WPR with DQN Reinforcement Learning

In the last article, we examined the pairing of Ichimoku and the ADX under an Inference Learning framework. For this piece we revisit, Reinforcement Learning when used with an indicator pairing we considered last in ‘Part 68’. The TRIX and Williams Percent Range. Our algorithm for this review will be the Quantile Regression DQN. As usual, we present this as a custom signal class designed for implementation with the MQL5 Wizard.
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MQL5 Wizard Techniques you should know (Part 62): Using Patterns of ADX and CCI with Reinforcement-Learning TRPO

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.
All about Automated Trading Championship: Registration
All about Automated Trading Championship: Registration

All about Automated Trading Championship: Registration

This article comprises useful materials that will help you learn more about the procedure of registration for participation in the Automated Trading Championship.
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MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial

MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial

Newton’s polynomial, which creates quadratic equations from a set of a few points, is an archaic but interesting approach at looking at a time series. In this article we try to explore what aspects could be of use to traders from this approach as well as address its limitations.
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Statistical Arbitrage Through Cointegrated Stocks (Part 8): Rolling Windows Eigenvector Comparison for Portfolio Rebalancing

Statistical Arbitrage Through Cointegrated Stocks (Part 8): Rolling Windows Eigenvector Comparison for Portfolio Rebalancing

This article proposes using Rolling Windows Eigenvector Comparison for early imbalance diagnostics and portfolio rebalancing in a mean-reversion statistical arbitrage strategy based on cointegrated stocks. It contrasts this technique with traditional In-Sample/Out-of-Sample ADF validation, showing that eigenvector shifts can signal the need for rebalancing even when IS/OOS ADF still indicates a stationary spread. While the method is intended mainly for live trading monitoring, the article concludes that eigenvector comparison could also be integrated into the scoring system—though its actual contribution to performance remains to be tested.
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Developing a Replay System — Market simulation (Part 07): First improvements (II)

Developing a Replay System — Market simulation (Part 07): First improvements (II)

In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.
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MetaTrader 5 Machine Learning Blueprint (Part 14): Transaction Cost Modeling for Triple-Barrier Labels in MQL5

MetaTrader 5 Machine Learning Blueprint (Part 14): Transaction Cost Modeling for Triple-Barrier Labels in MQL5

The article replaces hardcoded cost assumptions in triple-barrier labeling with measured inputs. An MQL5 script captures spread distribution, swap rates, and symbol metadata from your broker, and a Python model converts them into a broker-calibrated min ret you can pass to get events. Labels then reflect the actual round-trip friction for your instrument and holding period.
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Visualizing deals on a chart (Part 1): Selecting a period for analysis

Visualizing deals on a chart (Part 1): Selecting a period for analysis

Here we are going to develop a script from scratch that simplifies unloading print screens of deals for analyzing trading entries. All the necessary information on a single deal is to be conveniently displayed on one chart with the ability to draw different timeframes.
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Introduction to MQL5 (Part 28): Mastering API and WebRequest Function in MQL5 (II)

Introduction to MQL5 (Part 28): Mastering API and WebRequest Function in MQL5 (II)

This article teaches you how to retrieve and extract price data from external platforms using APIs and the WebRequest function in MQL5. You’ll learn how URLs are structured, how API responses are formatted, how to convert server data into readable strings, and how to identify and extract specific values from JSON responses.