Multiple indicators on one chart (Part 02): First experiments
In the previous article "Multiple indicators on one chart" I presented the concept and the basics of how to use multiple indicators on one chart. In this article, I will provide the source code and will explain it in detail.
High frequency arbitrage trading system in Python using MetaTrader 5
In this article, we will create an arbitration system that remains legal in the eyes of brokers, creates thousands of synthetic prices on the Forex market, analyzes them, and successfully trades for profit.
Pair Trading: Algorithmic Trading with Auto Optimization Based on Z-Score Differences
In this article, we will explore what pair trading is and how correlation trading works. We will also create an EA for automating pair trading and add the ability to automatically optimize this trading algorithm based on historical data. In addition, as part of the project, we will learn how to calculate the differences between two pairs using the z-score.
Timeseries in DoEasy library (part 44): Collection class of indicator buffer objects
The article deals with creating a collection class of indicator buffer objects. I am going to test the ability to create and work with any number of buffers for indicators (the maximum number of buffers that can be created in MQL indicators is 512).
From Novice to Expert: Navigating Market Irregularities
Market rules are continuously evolving, and many once-reliable principles gradually lose their effectiveness. What worked in the past no longer works consistently over time. Today’s discussion focuses on probability ranges and how they can be used to navigate market irregularities. We will leverage MQL5 to develop an algorithm capable of trading effectively even in the choppiest market conditions. Join this discussion to find out more.
Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer)
In this article, we will discuss the hybrid trading system StockFormer, which combines predictive coding and reinforcement learning (RL) algorithms. The framework uses 3 Transformer branches with an integrated Diversified Multi-Head Attention (DMH-Attn) mechanism that improves on the vanilla attention module with a multi-headed Feed-Forward block, allowing it to capture diverse time series patterns across different subspaces.
Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models
In the forex markets It is very challenging to predict the future trend without having an idea of the past. Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market
Adaptive Smart Money Architecture (ASMA): Merging SMC Logic With Market Sentiment for Dynamic Strategy Switching
This topic explores how to build an Adaptive Smart Money Architecture (ASMA)—an intelligent Expert Advisor that merges Smart Money Concepts (Order Blocks, Break of Structure, Fair Value Gaps) with real-time market sentiment to automatically choose the best trading strategy depending on current market conditions.
Tales of Trading Robots: Is Less More?
Two years ago in "The Last Crusade" we reviewed quite an interesting yet currently not widely used method for displaying market information - point and figure charts. Now I suggest you try to write a trading robot based on the patterns detected on the point and figure chart.
Developing a trading Expert Advisor from scratch (Part 29): The talking platform
In this article, we will learn how to make the MetaTrader 5 platform talk. What if we make the EA more fun? Financial market trading is often too boring and monotonous, but we can make this job less tiring. Please note that this project can be dangerous for those who experience problems such as addiction. However, in a general case, it just makes things less boring.
The MQL5 Standard Library Explorer (Part 1): Introduction with CTrade, CiMA, and CiATR
The MQL5 Standard Library plays a vital role in developing trading algorithms for MetaTrader 5. In this discussion series, our goal is to master its application to simplify the creation of efficient trading tools for MetaTrader 5. These tools include custom Expert Advisors, indicators, and other utilities. We begin today by developing a trend-following Expert Advisor using the CTrade, CiMA, and CiATR classes. This is an especially important topic for everyone—whether you are a beginner or an experienced developer. Join this discussion to discover more.
Creating an EA that works automatically (Part 14): Automation (VI)
In this article, we will put into practice all the knowledge from this series. We will finally build a 100% automated and functional system. But before that, we still have to learn one last detail.
Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models
In the forex markets It is very challenging to predict the future trend without having an idea of the past. Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market
Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time
This article outlines a simple strategy for normalizing the market data using the daily range and training a neural network to enhance market predictions. The developed models may be used in conjunction with an existing technical analysis frameworks or on a standalone basis to assist in predicting the overall market direction. The framework outlined in this article may be further refined by any technical analyst to develop models suitable for both manual and automated trading strategies.
Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?
Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.
Adaptive Smart Money Architecture (ASMA): Merging SMC Logic With Market Sentiment for Dynamic Strategy Switching
This topic explores how to build an Adaptive Smart Money Architecture (ASMA)—an intelligent Expert Advisor that merges Smart Money Concepts (Order Blocks, Break of Structure, Fair Value Gaps) with real-time market sentiment to automatically choose the best trading strategy depending on current market conditions.
MQL5 Wizard Techniques you should know (Part 48): Bill Williams Alligator
The Alligator Indicator, which was the brain child of Bill Williams, is a versatile trend identification indicator that yields clear signals and is often combined with other indicators. The MQL5 wizard classes and assembly allow us to test a variety of signals on a pattern basis, and so we consider this indicator as well.
The MQL5 Standard Library Explorer (Part 1): Introduction with CTrade, CiMA, and CiATR
The MQL5 Standard Library plays a vital role in developing trading algorithms for MetaTrader 5. In this discussion series, our goal is to master its application to simplify the creation of efficient trading tools for MetaTrader 5. These tools include custom Expert Advisors, indicators, and other utilities. We begin today by developing a trend-following Expert Advisor using the CTrade, CiMA, and CiATR classes. This is an especially important topic for everyone—whether you are a beginner or an experienced developer. Join this discussion to discover more.
Non-standard Automated Trading
Successful and comfortable trading using MT4 platform without detailed market analysis - is it possible? Can such trading be implemented in practice? I suppose, yes. Especially in terms of the automated trading!
Price Action Analysis Toolkit Development (Part 42): Interactive Chart Testing with Button Logic and Statistical Levels
In a world where speed and precision matter, analysis tools need to be as smart as the markets we trade. This article presents an EA built on button logic—an interactive system that instantly transforms raw price data into meaningful statistical levels. With a single click, it calculates and displays mean, deviation, percentiles, and more, turning advanced analytics into clear on-chart signals. It highlights the zones where price is most likely to bounce, retrace, or break, making analysis both faster and more practical.
Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap
What is the easiest way to implement new functionality? In this article, we will take one step back and then two steps forward.
Price Action Analysis Toolkit Development (Part 42): Interactive Chart Testing with Button Logic and Statistical Levels
In a world where speed and precision matter, analysis tools need to be as smart as the markets we trade. This article presents an EA built on button logic—an interactive system that instantly transforms raw price data into meaningful statistical levels. With a single click, it calculates and displays mean, deviation, percentiles, and more, turning advanced analytics into clear on-chart signals. It highlights the zones where price is most likely to bounce, retrace, or break, making analysis both faster and more practical.
MQL5 Cookbook: Handling Custom Chart Events
This article considers aspects of design and development of custom chart events system in the MQL5 environment. An example of an approach to the events classification can also be found here, as well as a program code for a class of events and a class of custom events handler.
Graphics in DoEasy library (Part 97): Independent handling of form object movement
In this article, I will consider the implementation of the independent dragging of any form objects using a mouse. Besides, I will complement the library by error messages and new deal properties previously implemented into the terminal and MQL5.
Reimagining Classic Strategies (Part 12): EURUSD Breakout Strategy
Join us today as we challenge ourselves to build a profitable break-out trading strategy in MQL5. We selected the EURUSD pair and attempted to trade price breakouts on the hourly timeframe. Our system had difficulty distinguishing between false breakouts and the beginning of true trends. We layered our system with filters intended to minimize our losses whilst increasing our gains. In the end, we successfully made our system profitable and less prone to false breakouts.
Neural networks made easy (Part 24): Improving the tool for Transfer Learning
In the previous article, we created a tool for creating and editing the architecture of neural networks. Today we will continue working on this tool. We will try to make it more user friendly. This may see, top be a step away form our topic. But don't you think that a well organized workspace plays an important role in achieving the result.
Statistical Arbitrage with predictions
We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
Sending Trading Signals in a Universal Expert Advisor
The article describes different ways of sending trading signals from a signal program unit of a universal Expert Advisor into the positions and orders controlling unit. It dwells on serial and parallel interfaces.
Trader's Kit: Drag Trade Library
The article describes Drag Trade Library that provides functionality for visual trading. The library can easily be integrated into virtually any Expert Advisor. Your Expert Advisor can be transformed from an automat into an automated trading and information system almost effortless on your side by just adding a few lines of code.
Trend strength and direction indicator on 3D bars
We will consider a new approach to market trend analysis based on three-dimensional visualization and tensor analysis of the market microstructure.
Neural networks made easy (Part 19): Association rules using MQL5
We continue considering association rules. In the previous article, we have discussed theoretical aspect of this type of problem. In this article, I will show the implementation of the FP Growth method using MQL5. We will also test the implemented solution using real data.
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session
Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.
Improve Your Trading Charts With Interactive GUI's in MQL5 (Part I): Movable GUI (I)
Unleash the power of dynamic data representation in your trading strategies or utilities with our comprehensive guide on creating movable GUI in MQL5. Dive into the core concept of chart events and learn how to design and implement simple and multiple movable GUI on the same chart. This article also explores the process of adding elements to your GUI, enhancing their functionality and aesthetic appeal.
How to create a custom True Strength Index indicator using MQL5
Here is a new article about how to create a custom indicator. This time we will work with the True Strength Index (TSI) and will create an Expert Advisor based on it.
Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs
In this article, we develop a JSON parsing framework in MQL5 to handle data exchange for AI API integration, focusing on a JSON class for processing JSON structures. We implement methods to serialize and deserialize JSON data, supporting various data types like strings, numbers, and objects, essential for communicating with AI services like ChatGPT, enabling future AI-driven trading systems by ensuring accurate data handling and manipulation.
William Gann methods (Part I): Creating Gann Angles indicator
What is the essence of Gann Theory? How are Gann angles constructed? We will create Gann Angles indicator for MetaTrader 5.
Developing a Trading Strategy: Using a Volume-Bound Approach
In the world of technical analysis, price often takes center stage. Traders meticulously map out support, resistance, and patterns, yet frequently ignore the critical force that drives these movements: volume. This article delves into a novel approach to volume analysis: the Volume Boundary indicator. This transformation, utilizing sophisticated smoothing functions like the butterfly and triple sine curves, allows for clearer interpretation and the development of systematic trading strategies.
Using Skype to Send Messages from an Expert Advisor
The article deals with the ways of how to send internal messages and SMSes from an Expert Advisor to mobile phones using Skype.
Creating an EA that works automatically (Part 05): Manual triggers (II)
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. At the end of the previous article, I suggested that it would be appropriate to allow manual use of the EA, at least for a while.
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.