Articles on trading system automation in MQL5
Read articles on the trading systems with a wide variety of ideas at the core. Learn how to use statistical methods and patterns on candlestick charts, how to filter signals and where to use semaphore indicators.
The MQL5 Wizard will help you create robots without programming to quickly check your trading ideas. Use the Wizard to learn about genetic algorithms.
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Neural networks made easy (Part 20): Autoencoders
We continue to study unsupervised learning algorithms. Some readers might have questions regarding the relevance of recent publications to the topic of neural networks. In this new article, we get back to studying neural networks.
Developing a multi-currency Expert Advisor (Part 13): Automating the second stage — selection into groups
We have already implemented the first stage of the automated optimization. We perform optimization for different symbols and timeframes according to several criteria and store information about the results of each pass in the database. Now we are going to select the best groups of parameter sets from those found at the first stage.
Building a Custom Market Regime Detection System in MQL5 (Part 2): Expert Advisor
This article details building an adaptive Expert Advisor (MarketRegimeEA) using the regime detector from Part 1. It automatically switches trading strategies and risk parameters for trending, ranging, or volatile markets. Practical optimization, transition handling, and a multi-timeframe indicator are included.
Developing a multi-currency Expert Advisor (Part 17): Further preparation for real trading
Currently, our EA uses the database to obtain initialization strings for single instances of trading strategies. However, the database is quite large and contains a lot of information that is not needed for the actual EA operation. Let's try to ensure the EA's functionality without a mandatory connection to the database.
Fast trading strategy tester in Python using Numba
The article implements a fast strategy tester for machine learning models using Numba. It is 50 times faster than the pure Python strategy tester. The author recommends using this library to speed up mathematical calculations, especially the ones involving loops.
Price Action Analysis Toolkit Development (Part 12): External Flow (III) TrendMap
The flow of the market is determined by the forces between bulls and bears. There are specific levels that the market respects due to the forces acting on them. Fibonacci and VWAP levels are especially powerful in influencing market behavior. Join me in this article as we explore a strategy based on VWAP and Fibonacci levels for signal generation.
Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)
We will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.
MQL5 Trading Tools (Part 1): Building an Interactive Visual Pending Orders Trade Assistant Tool
In this article, we introduce the development of an interactive Trade Assistant Tool in MQL5, designed to simplify placing pending orders in Forex trading. We outline the conceptual design, focusing on a user-friendly GUI for setting entry, stop-loss, and take-profit levels visually on the chart. Additionally, we detail the MQL5 implementation and backtesting process to ensure the tool’s reliability, setting the stage for advanced features in the preceding parts.
Neural networks made easy (Part 66): Exploration problems in offline learning
Models are trained offline using data from a prepared training dataset. While providing certain advantages, its negative side is that information about the environment is greatly compressed to the size of the training dataset. Which, in turn, limits the possibilities of exploration. In this article, we will consider a method that enables the filling of a training dataset with the most diverse data possible.
Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (Final Part)
In the previous article, we introduced the multi-agent adaptive framework MASAAT, which uses an ensemble of agents to perform cross-analysis of multimodal time series at different data scales. Today we will continue implementing the approaches of this framework in MQL5 and bring this work to a logical conclusion.
Creation of an Automated Trading System
You must admit that it sounds alluringly - you become a fortunate possessor of a program that can develop for you a profitable automated trading system (ATC) within a few minutes. All you need is to enter desirable inputs and press Enter. And - here you are, take your ATC tested and having positive expected payoff. Where thousands of people spend thousands of hours on developing that very unique ATC, which will "wine and dine", these statements soundб to put it mildly, very hollow. On the one hand, this really looks a little larger than life... However, to my mind, this problem can be solved.
Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE)
In an attempt to obtain the most accurate forecasts, researchers often complicate forecasting models. Which in turn leads to increased model training and maintenance costs. Is such an increase always justified? This article introduces an algorithm that uses the simplicity and speed of linear models and demonstrates results on par with the best models with a more complex architecture.
Developing a Calendar-Based News Event Breakout Expert Advisor in MQL5
Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.
Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D)
We invite you to get acquainted with a new approach to detecting objects using hypernetworks. A hypernetwork generates weights for the main model, which allows taking into account the specifics of the current market situation. This approach allows us to improve forecasting accuracy by adapting the model to different trading conditions.
Neural Networks in Trading: Optimizing the Transformer for Time Series Forecasting (LSEAttention)
The LSEAttention framework offers improvements to the Transformer architecture. It was designed specifically for long-term multivariate time series forecasting. The approaches proposed by the authors of the method can be applied to solve problems of entropy collapse and learning instability, which are often encountered with vanilla Transformer.
Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
MQL5 Wizard Techniques you should know (Part 80): Using Patterns of Ichimoku and the ADX-Wilder with TD3 Reinforcement Learning
This article follows up ‘Part-74’, where we examined the pairing of Ichimoku and the ADX under a Supervised Learning framework, by moving our focus to Reinforcement Learning. Ichimoku and ADX form a complementary combination of support/resistance mapping and trend strength spotting. In this installment, we indulge in how the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm can be used with this indicator set. As with earlier parts of the series, the implementation is carried out in a custom signal class designed for integration with the MQL5 Wizard, which facilitates seamless Expert Advisor assembly.
Data Science and ML (Part 41): Forex and Stock Markets Pattern Detection using YOLOv8
Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.
MQL5 Wizard Techniques you should know (Part 42): ADX Oscillator
The ADX is another relatively popular technical indicator used by some traders to gauge the strength of a prevalent trend. Acting as a combination of two other indicators, it presents as an oscillator whose patterns we explore in this article with the help of MQL5 wizard assembly and its support classes.
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.
Experiments with neural networks (Part 4): Templates
In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading. Simple explanation.
Currency pair strength indicator in pure MQL5
We are going to develop a professional indicator for currency strength analysis in MQL5. This step-by-step guide will show you how to develop a powerful trading tool with a visual dashboard for MetaTrader 5. You will learn how to calculate the strength of currency pairs across multiple timeframes (H1, H4, D1), implement dynamic data updates, and create a user-friendly interface.
Quantitative analysis in MQL5: Implementing a promising algorithm
We will analyze the question of what quantitative analysis is and how it is used by major players. We will create one of the quantitative analysis algorithms in the MQL5 language.
Reimagining Classic Strategies (Part 19): Deep Dive Into Moving Average Crossovers
This article revisits the classic moving average crossover strategy and examines why it often fails in noisy, fast-moving markets. It presents five alternative filtering methods designed to strengthen signal quality and remove weak or unprofitable trades. The discussion highlights how statistical models can learn and correct the errors that human intuition and traditional rules miss. Readers leave with a clearer understanding of how to modernize an outdated strategy and of the pitfalls of relying solely on metrics like RMSE in financial modeling.
Automating Trading Strategies in MQL5 (Part 33): Creating a Price Action Shark Harmonic Pattern System
In this article, we develop a Shark pattern system in MQL5 that identifies bullish and bearish Shark harmonic patterns using pivot points and Fibonacci ratios, executing trades with customizable entry, stop-loss, and take-profit levels based on user-selected options. We enhance trader insight with visual feedback through chart objects like triangles, trendlines, and labels to clearly display the X-A-B-C-D pattern structure
MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library
Learn how to develop and implement a comprehensive pending orders EX5 library in your MQL5 code or projects. This article will show you how to create an extensive pending orders management EX5 library and guide you through importing and implementing it by building a trading panel or graphical user interface (GUI). The expert advisor orders panel will allow users to open, monitor, and delete pending orders associated with a specified magic number directly from the graphical interface on the chart window.
Price Action Analysis Toolkit Development (Part 23): Currency Strength Meter
Do you know what really drives a currency pair’s direction? It’s the strength of each individual currency. In this article, we’ll measure a currency’s strength by looping through every pair it appears in. That insight lets us predict how those pairs may move based on their relative strengths. Read on to learn more.
Price Action Analysis Toolkit Development (Part 30): Commodity Channel Index (CCI), Zero Line EA
Automating price action analysis is the way forward. In this article, we utilize the Dual CCI indicator, the Zero Line Crossover strategy, EMA, and price action to develop a tool that generates trade signals and sets stop-loss (SL) and take-profit (TP) levels using ATR. Please read this article to learn how we approach the development of the CCI Zero Line EA.
Neural Networks Made Easy (Part 94): Optimizing the Input Sequence
When working with time series, we always use the source data in their historical sequence. But is this the best option? There is an opinion that changing the sequence of the input data will improve the efficiency of the trained models. In this article I invite you to get acquainted with one of the methods for optimizing the input sequence.
How to Use Crashlogs to Debug Your Own DLLs
25 to 30% of all crashlogs received from users appear due to errors occurring when functions imported from custom dlls are executed.
Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python
So far we have considered the automation of launching sequential procedures for optimizing EAs exclusively in the standard strategy tester. But what if we would like to perform some handling of the obtained data using other means between such launches? We will attempt to add the ability to create new optimization stages performed by programs written in Python.
Brute force approach to patterns search (Part V): Fresh angle
In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.
Trading Insights Through Volume: Trend Confirmation
The Enhanced Trend Confirmation Technique combines price action, volume analysis, and machine learning to identify genuine market movements. It requires both price breakouts and volume surges (50% above average) for trade validation, while using an LSTM neural network for additional confirmation. The system employs ATR-based position sizing and dynamic risk management, making it adaptable to various market conditions while filtering out false signals.
MQL5 Wizard Techniques you should know (Part 73): Using Patterns of Ichimoku and the ADX-Wilder
The Ichimoku-Kinko-Hyo Indicator and the ADX-Wilder oscillator are a pairing that could be used in complimentarily within an MQL5 Expert Advisor. The Ichimoku is multi-faceted, however for this article, we are relying on it primarily for its ability to define support and resistance levels. Meanwhile, we also use the ADX to define our trend. As usual, we use the MQL5 wizard to build and test any potential these two may possess.
Dynamic Swing Architecture: Market Structure Recognition from Swings to Automated Execution
This article introduces a fully automated MQL5 system designed to identify and trade market swings with precision. Unlike traditional fixed-bar swing indicators, this system adapts dynamically to evolving price structure—detecting swing highs and swing lows in real time to capture directional opportunities as they form.
Price Action Analysis Toolkit Development (Part 2): Analytical Comment Script
Aligned with our vision of simplifying price action, we are pleased to introduce another tool that can significantly enhance your market analysis and help you make well-informed decisions. This tool displays key technical indicators such as previous day's prices, significant support and resistance levels, and trading volume, while automatically generating visual cues on the chart.
Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part)
We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns.
Price Action Analysis Toolkit Development (Part 5): Volatility Navigator EA
Determining market direction can be straightforward, but knowing when to enter can be challenging. As part of the series titled "Price Action Analysis Toolkit Development", I am excited to introduce another tool that provides entry points, take profit levels, and stop loss placements. To achieve this, we have utilized the MQL5 programming language. Let’s delve into each step in this article.
Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies
Let's continue developing a multi-currency EA with several strategies working in parallel. Let's try to move all the work associated with opening market positions from the strategy level to the level of the EA managing the strategies. The strategies themselves will trade only virtually, without opening market positions.
MQL5 Trading Tools (Part 10): Building a Strategy Tracker System with Visual Levels and Success Metrics
In this article, we develop an MQL5 strategy tracker system that detects moving average crossover signals filtered by a long-term MA, simulates or executes trades with configurable TP levels and SL in points, and monitors outcomes like TP/SL hits for performance analysis.