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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DoEasy. Controls (Part 30): Animating the ScrollBar control
In this article, I will continue the development of the ScrollBar control and start implementing the mouse interaction functionality. In addition, I will expand the lists of mouse state flags and events.
ATC Champions League: Interview with Boris Odintsov (ATC 2011)
Interview with Boris Odintsov (bobsley) is the last one within the ATC Champions League project. Boris won the Automated Trading Championship 2010 - the first Championship held for the Expert Advisors in the new MQL5 language. Having appeared in the top ten already in the first week of the ATC 2010, his EA brought it to the finish and earned $77,000. This year, Boris participates in the competition with the same Expert Advisor with modified settings. Perhaps the robot would still be able to repeat its success.
Interview with Alexander Arashkevich (ATC 2011)
The Championship fervour has finally subsided and we can take a breath and start rethinking its results again. And we have another winner Alexander Arashkevich (AAA777) from Belarus, who has won a special prize from the major sponsor of Automated Trading Championship 2011 - a 3 day trip to one of the Formula One races of the 2012 season. We could not miss the opportunity to talk with him.
Neural networks made easy (Part 43): Mastering skills without the reward function
The problem of reinforcement learning lies in the need to define a reward function. It can be complex or difficult to formalize. To address this problem, activity-based and environment-based approaches are being explored to learn skills without an explicit reward function.
Neural networks made easy (Part 50): Soft Actor-Critic (model optimization)
In the previous article, we implemented the Soft Actor-Critic algorithm, but were unable to train a profitable model. Here we will optimize the previously created model to obtain the desired results.
Population optimization algorithms: Nelder–Mead, or simplex search (NM) method
The article presents a complete exploration of the Nelder-Mead method, explaining how the simplex (function parameter space) is modified and rearranged at each iteration to achieve an optimal solution, and describes how the method can be improved.
Statistical Arbitrage Through Cointegrated Stocks (Part 6): Scoring System
In this article, we propose a scoring system for mean-reversion strategies based on statistical arbitrage of cointegrated stocks. The article suggests criteria that go from liquidity and transaction costs to the number of cointegration ranks and time to mean-reversion, while taking into account the strategic criteria of data frequency (timeframe) and the lookback period for cointegration tests, which are evaluated before the score ranking properly. The files required for the reproduction of the backtest are provided, and their results are commented on as well.
Developing a multi-currency Expert Advisor (Part 14): Adaptive volume change in risk manager
The previously developed risk manager contained only basic functionality. Let's try to consider possible ways of its development, allowing us to improve trading results without interfering with the logic of trading strategies.
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.
Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
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.
Market Simulation (Part 15): Sockets (IX)
In this article, we will discuss one of the possible solutions to what we have been trying to demonstrate—namely, how to allow an Excel user to perform an action in MetaTrader 5 without sending orders or opening or closing positions. The idea is that the user employs Excel to conduct fundamental analysis of a particular symbol. And by using only Excel, they can instruct an expert advisor running in MetaTrader 5 to open or close a specific position.
MetaTrader tick info access from MQL5 services to Python application using sockets
Sometimes everything is not programmable in the MQL5 language. And even if it is possible to convert existing advanced libraries in MQL5, it would be time-consuming. This article tries to show that we can bypass Windows OS dependency by transporting tick information such as bid, ask and time with MetaTrader services to a Python application using sockets.
Developing a multi-currency Expert Advisor (Part 3): Architecture revision
We have already made some progress in developing a multi-currency EA with several strategies working in parallel. Considering the accumulated experience, let's review the architecture of our solution and try to improve it before we go too far ahead.
MQL5 Wizard Techniques you should know (Part 08): Perceptrons
Perceptrons, single hidden layer networks, can be a good segue for anyone familiar with basic automated trading and is looking to dip into neural networks. We take a step by step look at how this could be realized in a signal class assembly that is part of the MQL5 Wizard classes for expert advisors.
Building AI-Powered Trading Systems in MQL5 (Part 8): UI Polish with Animations, Timing Metrics, and Response Management Tools
In this article, we enhance the AI-powered trading system in MQL5 with user interface improvements, including loading animations for request preparation and thinking phases, as well as timing metrics displayed in responses for better feedback. We add response management tools like regenerate buttons to re-query the AI and export options to save the last response to a file, streamlining interaction.
MQL5 Wizard Techniques you should know (Part 17): Multicurrency Trading
Trading across multiple currencies is not available by default when an expert advisor is assembled via the wizard. We examine 2 possible hacks traders can make when looking to test their ideas off more than one symbol at a time.
Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (Final Part)
SAMformer offers a solution to the key drawbacks of Transformer models in long-term time series forecasting, such as training complexity and poor generalization on small datasets. Its shallow architecture and sharpness-aware optimization help avoid suboptimal local minima. In this article, we will continue to implement approaches using MQL5 and evaluate their practical value.
Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values
In the previous article, we introduced the DDPG method, which allows training models in a continuous action space. However, like other Q-learning methods, DDPG is prone to overestimating Q-function values. This problem often results in training an agent with a suboptimal strategy. In this article, we will look at some approaches to overcome the mentioned issue.
Neural Networks in Trading: Disentangling Structured Components (Encoder)
We invite you to explore the next stage in implementing the SCNN framework, which combines flexibility and interpretability, allowing structural components of a time series to be identified precisely. The article provides a detailed explanation of the mechanisms of adaptive normalization and attention, which ensure the model's resilience to changing market conditions.
Estimate future performance with confidence intervals
In this article we delve into the application of boostrapping techniques as a means to estimate the future performance of an automated strategy.
Reimagining Classic Strategies (Part III): Forecasting Higher Highs And Lower Lows
In this series article, we will empirically analyze classic trading strategies to see if we can improve them using AI. In today's discussion, we tried to predict higher highs and lower lows using the Linear Discriminant Analysis model.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (IV): Trade Management Panel class
This discussion covers the updated TradeManagementPanel in our New_Admin_Panel EA. The update enhances the panel by using built-in classes to offer a user-friendly trade management interface. It includes trading buttons for opening positions and controls for managing existing trades and pending orders. A key feature is the integrated risk management that allows setting stop loss and take profit values directly in the interface. This update improves code organization for large programs and simplifies access to order management tools, which are often complex in the terminal.
Mining Central Bank Balance Sheet Data to Get a Picture of Global Liquidity
Mining central bank balance sheet data provides a picture of global liquidity in the Forex market and key currencies. We combine data from the Fed, ECB, BOJ and PBoC into a composite index and use machine learning to uncover hidden patterns. This approach turns raw data into real trading signals by combining fundamental and technical analysis.
Using association rules in Forex data analysis
How to apply predictive rules of supermarket retail analytics to the real Forex market? How are purchases of cookies, milk and bread related to stock exchange transactions? The article discusses an innovative approach to algorithmic trading based on the use of association rules.
Neural Networks in Trading: Using Language Models for Time Series Forecasting
We continue to study time series forecasting models. In this article, we get acquainted with a complex algorithm built on the use of a pre-trained language model.
MQL5 Wizard Techniques you should know (Part 77): Using Gator Oscillator and the Accumulation/Distribution Oscillator
The Gator Oscillator by Bill Williams and the Accumulation/Distribution Oscillator are another indicator pairing that could be used harmoniously within an MQL5 Expert Advisor. We use the Gator Oscillator for its ability to affirm trends, while the A/D is used to provide confirmation of the trends via checks on volume. In exploring this indicator pairing, as always, we use the MQL5 wizard to build and test out their potential.
MQL5 Wizard Techniques you should know (Part 61): Using Patterns of ADX and CCI with Supervised Learning
The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We look at how this can be systemized by using all the 3 main training modes of Machine Learning. Wizard Assembled Expert Advisors allow us to evaluate the patterns presented by these two indicators, and we start by looking at how Supervised-Learning can be applied with these Patterns.
Exploring Machine Learning in Unidirectional Trend Trading Using Gold as a Case Study
This article discusses an approach to trading only in the chosen direction (buy or sell). For this purpose, the technique of causal inference and machine learning are used.
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.
Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes
In this article, we dive deep into the crucial aspects of choosing the most relevant and high-quality Forex data to enhance the performance of AI models.
Self Optimizing Expert Advisor With MQL5 And Python (Part IV): Stacking Models
Today, we will demonstrate how you can build AI-powered trading applications capable of learning from their own mistakes. We will demonstrate a technique known as stacking, whereby we use 2 models to make 1 prediction. The first model is typically a weaker learner, and the second model is typically a more powerful model that learns the residuals of our weaker learner. Our goal is to create an ensemble of models, to hopefully attain higher accuracy.
Trading with the MQL5 Economic Calendar (Part 4): Implementing Real-Time News Updates in the Dashboard
This article enhances our Economic Calendar dashboard by implementing real-time news updates to keep market information current and actionable. We integrate live data fetching techniques in MQL5 to update events on the dashboard continuously, improving the responsiveness of the interface. This update ensures that we can access the latest economic news directly from the dashboard, optimizing trading decisions based on the freshest data.
Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators
Did you know that we can gain more accuracy forecasting certain technical indicators than predicting the underlying price of a traded symbol? Join us to explore how to leverage this insight for better trading strategies.
Neural Networks in Trading: Dual Clustering of Multivariate Time Series (Final Part)
We continue to implement approaches proposed vy the authors of the DUET framework, which offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data.
Statistical Arbitrage Through Cointegrated Stocks (Part 2): Expert Advisor, Backtests, and Optimization
This article presents a sample Expert Advisor implementation for trading a basket of four Nasdaq stocks. The stocks were initially filtered based on Pearson correlation tests. The filtered group was then tested for cointegration with Johansen tests. Finally, the cointegrated spread was tested for stationarity with the ADF and KPSS tests. Here we will see some notes about this process and the results of the backtests after a small optimization.
Neural Networks in Trading: Transformer with Relative Encoding
Self-supervised learning can be an effective way to analyze large amounts of unlabeled data. The efficiency is provided by the adaptation of models to the specific features of financial markets, which helps improve the effectiveness of traditional methods. This article introduces an alternative attention mechanism that takes into account the relative dependencies and relationships between inputs.
From Simple Close Buttons to a Rule-Based Risk Dashboard in MQL5
Build a rule-based on-chart risk management panel in MetaTrader 5 using the MQL5 Standard Library. The guide covers a CAppDialog-based GUI, manual event routing, and an automated update loop. You will bind UI events to CTrade to execute conditional closures, show net floating P/L, and read automated targets directly from the chart.
Neural Networks Made Easy (Part 93): Adaptive Forecasting in Frequency and Time Domains (Final Part)
In this article, we continue the implementation of the approaches of the ATFNet model, which adaptively combines the results of 2 blocks (frequency and time) within time series forecasting.
Self Optimizing Expert Advisors in MQL5 (Part 10): Matrix Factorization
Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data — organized in rows and columns — we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.