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MQL5 Wizard Techniques you should know (Part 63): Using Patterns of DeMarker and Envelope Channels
MQL5 Wizard Techniques you should know (Part 63): Using Patterns of DeMarker and Envelope Channels
The DeMarker Oscillator and the Envelope indicator are momentum and support/resistance tools that can be paired when developing an Expert Advisor. We therefore examine on a pattern by pattern basis what could be of use and what potentially avoid. We are using, as always, a wizard assembled Expert Advisor together with the Patterns-Usage functions that are built into the Expert Signal Class.
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.
Black Hole Algorithm (BHA)
Black Hole Algorithm (BHA)
The Black Hole Algorithm (BHA) uses the principles of black hole gravity to optimize solutions. In this article, we will look at how BHA attracts the best solutions while avoiding local extremes, and why this algorithm has become a powerful tool for solving complex problems. Learn how simple ideas can lead to impressive results in the world of optimization.
Developing a Replay System (Part 78): New Chart Trade (V)
Developing a Replay System (Part 78): New Chart Trade (V)
In this article, we will look at how to implement part of the receiver code. Here we will implement an Expert Advisor to test and learn how the protocol interaction works. 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.
Building a Trading System (Part 3): Determining Minimum Risk Levels for Realistic Profit Targets
Building a Trading System (Part 3): Determining Minimum Risk Levels for Realistic Profit Targets
Every trader's ultimate goal is profitability, which is why many set specific profit targets to achieve within a defined trading period. In this article, we will use Monte Carlo simulations to determine the optimal risk percentage per trade needed to meet trading objectives. The results will help traders assess whether their profit targets are realistic or overly ambitious. Finally, we will discuss which parameters can be adjusted to establish a practical risk percentage per trade that aligns with trading goals.
How to Add New UI Languages to the MetaTrader 5 Platform
How to Add New UI Languages to the MetaTrader 5 Platform
User interface of the MetaTrader 5 platform is translated into several languages. Don't worry if your native language is not among the supported ones. You can easily complete the translation using the special MetaTrader 5 MultiLanguage Pack utility, offered by MetaQuotes Software Corp. for free to all comers. In this article we will show some examples of how to add a new user interface languages to the MetaTrader 5 platform.
Multi-module trading robot in Python and MQL5 (Part I): Creating basic architecture and first modules
Multi-module trading robot in Python and MQL5 (Part I): Creating basic architecture and first modules
We are going to develop a modular trading system that combines Python for data analysis with MQL5 for trade execution. Four independent modules monitor different market aspects in parallel: volumes, arbitrage, economics and risks, and use RandomForest with 400 trees for analysis. Particular emphasis is placed on risk management, since even the most advanced trading algorithms are useless without proper risk management.
MetaTrader Meets Google Sheets with Pythonanywhere: A Guide to Secure Data Flow
MetaTrader Meets Google Sheets with Pythonanywhere: A Guide to Secure Data Flow
This article demonstrates a secure way to export MetaTrader data to Google Sheets. Google Sheet is the most valuable solution as it is cloud based and the data saved in there can be accessed anytime and from anywhere. So traders can access trading and related data exported to google sheet and do further analysis for future trading anytime and wherever they are at the moment.
Developing a Replay System (Part 77): New Chart Trade (IV)
Developing a Replay System (Part 77): New Chart Trade (IV)
In this article, we will cover some of the measures and precautions to consider when creating a communication protocol. These are pretty simple and straightforward things, so we won't go into too much detail in this article. But to understand what will happen, you need to understand the content of the article.
Simplifying Databases in MQL5 (Part 1): Introduction to Databases and SQL
Simplifying Databases in MQL5 (Part 1): Introduction to Databases and SQL
We explore how to manipulate databases in MQL5 using the language's native functions. We cover everything from table creation, insertion, updating, and deletion to data import and export, all with sample code. The content serves as a solid foundation for understanding the internal mechanics of data access, paving the way for the discussion of ORM, where we'll build one in MQL5.
Chart Synchronization for Easier Technical Analysis
Chart Synchronization for Easier Technical Analysis
Chart Synchronization for Easier Technical Analysis is a tool that ensures all chart timeframes display consistent graphical objects like trendlines, rectangles, or indicators across different timeframes for a single symbol. Actions such as panning, zooming, or symbol changes are mirrored across all synced charts, allowing traders to seamlessly view and compare the same price action context in multiple timeframes.
Trend criteria in trading
Trend criteria in trading
Trends are an important part of many trading strategies. In this article, we will look at some of the tools used to identify trends and their characteristics. Understanding and correctly interpreting trends can significantly improve trading efficiency and minimize risks.
Getting Started with MQL5 Algo Forge
Getting Started with MQL5 Algo Forge
We are introducing MQL5 Algo Forge — a dedicated portal for algorithmic trading developers. It combines the power of Git with an intuitive interface for managing and organizing projects within the MQL5 ecosystem. Here, you can follow interesting authors, form teams, and collaborate on algorithmic trading projects.
From Novice to Expert: Mastering Detailed Trading Reports with Reporting EA
From Novice to Expert: Mastering Detailed Trading Reports with Reporting EA
In this article, we delve into enhancing the details of trading reports and delivering the final document via email in PDF format. This marks a progression from our previous work, as we continue exploring how to harness the power of MQL5 and Python to generate and schedule trading reports in the most convenient and professional formats. Join us in this discussion to learn more about optimizing trading report generation within the MQL5 ecosystem.
Artificial Tribe Algorithm (ATA)
Artificial Tribe Algorithm (ATA)
The article provides a detailed discussion of the key components and innovations of the ATA optimization algorithm, which is an evolutionary method with a unique dual behavior system that adapts depending on the situation. ATA combines individual and social learning while using crossover for explorations and migration to find solutions when stuck in local optima.
Self Optimizing Expert Advisors in MQL5 (Part 13): A Gentle Introduction To Control Theory Using Matrix Factorization
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.
Introduction to MQL5 (Part 20): Introduction to Harmonic Patterns
Introduction to MQL5 (Part 20): Introduction to Harmonic Patterns
In this article, we explore the fundamentals of harmonic patterns, their structures, and how they are applied in trading. You’ll learn about Fibonacci retracements, extensions, and how to implement harmonic pattern detection in MQL5, setting the foundation for building advanced trading tools and Expert Advisors.
Building a Trading System (Part 1): A Quantitative Approach
Building a Trading System (Part 1): A Quantitative Approach
Many traders evaluate strategies based on short-term performance, often abandoning profitable systems too early. Long-term profitability, however, depends on positive expectancy through optimized win rate and risk-reward ratio, along with disciplined position sizing. These principles can be validated using Monte Carlo simulation in Python with back-tested metrics to assess whether a strategy is robust or likely to fail over time.
From Basic to Intermediate: Template and Typename (IV)
From Basic to Intermediate: Template and Typename (IV)
In this article, we will take a very close look at how to solve the problem posed at the end of the previous article. There was an attempt to create a template of such type so that to be able to create a template for data union.
Statistical Arbitrage Through Cointegrated Stocks (Part 3): Database Setup
Statistical Arbitrage Through Cointegrated Stocks (Part 3): Database Setup
This article presents a sample MQL5 Service implementation for updating a newly created database used as source for data analysis and for trading a basket of cointegrated stocks. The rationale behind the database design is explained in detail and the data dictionary is documented for reference. MQL5 and Python scripts are provided for the database creation, schema initialization, and market data insertion.
Reimagining Classic Strategies (Part 15): Daily Breakout Trading Strategy
Reimagining Classic Strategies (Part 15): Daily Breakout Trading Strategy
Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic methods. Our findings show that while the original strategy produced high accuracy, it suffered from instability and poor risk control. By refining the approach, we demonstrate how discretionary insights can be adapted into more robust, algorithmic trading strategies.
MetaTrader 5 Machine Learning Blueprint (Part 2): Labeling Financial Data for Machine Learning
MetaTrader 5 Machine Learning Blueprint (Part 2): Labeling Financial Data for Machine Learning
In this second installment of the MetaTrader 5 Machine Learning Blueprint series, you’ll discover why simple labels can lead your models astray—and how to apply advanced techniques like the Triple-Barrier and Trend-Scanning methods to define robust, risk-aware targets. Packed with practical Python examples that optimize these computationally intensive techniques, this hands-on guide shows you how to transform noisy market data into reliable labels that mirror real-world trading conditions.
Price Action Analysis Toolkit Development (Part 37): Sentiment Tilt Meter
Price Action Analysis Toolkit Development (Part 37): Sentiment Tilt Meter
Market sentiment is one of the most overlooked yet powerful forces influencing price movement. While most traders rely on lagging indicators or guesswork, the Sentiment Tilt Meter (STM) EA transforms raw market data into clear, visual guidance, showing whether the market is leaning bullish, bearish, or staying neutral in real-time. This makes it easier to confirm trades, avoid false entries, and time market participation more effectively.
MQL5 Wizard Techniques you should know (Part 79): Using Gator Oscillator and Accumulation/Distribution Oscillator with Supervised Learning
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.
From Basic to Intermediate: Template and Typename (III)
From Basic to Intermediate: Template and Typename (III)
In this article, we will discuss the first part of the topic, which is not so easy for beginners to understand. In order not to get even more confused and to explain this topic correctly, we will divide the explanation into stages. We will devote this article to the first stage. However, although at the end of the article it may seem that we have reached the deadlock, in fact we will take a step towards another situation, which will be better understood in the next article.
MetaTrader 5 Machine Learning Blueprint (Part 1): Data Leakage and Timestamp Fixes
MetaTrader 5 Machine Learning Blueprint (Part 1): Data Leakage and Timestamp Fixes
Before we can even begin to make use of ML in our trading on MetaTrader 5, it’s crucial to address one of the most overlooked pitfalls—data leakage. This article unpacks how data leakage, particularly the MetaTrader 5 timestamp trap, can distort our model's performance and lead to unreliable trading signals. By diving into the mechanics of this issue and presenting strategies to prevent it, we pave the way for building robust machine learning models that deliver trustworthy predictions in live trading environments.
CRUD Operations in Firebase using MQL
CRUD Operations in Firebase using MQL
This article offers a step-by-step guide to mastering CRUD (Create, Read, Update, Delete) operations in Firebase, focusing on its Realtime Database and Firestore. Discover how to use Firebase SDK methods to efficiently manage data in web and mobile apps, from adding new records to querying, modifying, and deleting entries. Explore practical code examples and best practices for structuring and handling data in real-time, empowering developers to build dynamic, scalable applications with Firebase’s flexible NoSQL architecture.
How to publish a product on the Market
How to publish a product on the Market
Start offering your trading applications to millions of MetaTrader users from around the world though the Market. The service provides a ready-made infrastructure: access to a large audience, licensing solutions, trial versions, publication of updates and acceptance of payments. You only need to complete a quick seller registration procedure and publish your product. Start generating additional profits from your programs using the ready-made technical base provided by the service.
From Basic to Intermediate: Template and Typename (II)
From Basic to Intermediate: Template and Typename (II)
This article explains how to deal with one of the most difficult programming situations you can encounter: using different types in the same function or procedure template. Although we have spent most of our time focusing only on functions, everything covered here is useful and can be applied to procedures.
Creating 3D bars based on time, price and volume
Creating 3D bars based on time, price and volume
The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.
From Basic to Intermediate: Definitions (I)
From Basic to Intermediate: Definitions (I)
In this article we will do things that many will find strange and completely out of context, but which, if used correctly, will make your learning much more fun and interesting: we will be able to build quite interesting things based on what is shown here. This will allow you to better understand the syntax of the MQL5 language. The materials provided here are for educational purposes only. It should not be considered in any way as a final application. Its purpose is not to explore the concepts presented.
Developing a Replay System (Part 76): New Chart Trade (III)
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.