Articles on trading system automation in MQL5

We follow up on our last article, where we introduced the indicator pair of the MACD and the OBV, by looking at how this pairing could be enhanced with Machine Learning. MACD and OBV are a trend and volume complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

In this article, we enhance a neural network trading strategy in MQL5 with an adaptive learning rate to boost accuracy. We design and implement this mechanism, then test its performance. The article concludes with optimization insights for algorithmic trading.

In the second part of the article, we will continue developing a modified version of the AOS (Atomic Orbital Search) algorithm focusing on specific operators to improve its efficiency and adaptability. After analyzing the fundamentals and mechanics of the algorithm, we will discuss ideas for improving its performance and the ability to analyze complex solution spaces, proposing new approaches to extend its functionality as an optimization tool.

Discover how the Boom & Crash Interceptor EA transforms your charts into a proactive alert system-spotting explosive moves with lightning-fast velocity scans, volatility surge checks, trend confirmation, and pivot-zone filters. With crisp green “Boom” and red “Crash” arrows guiding your every decision, this tool cuts through the noise and lets you capitalize on market spikes like never before. Dive in to see how it works and why it can become your next essential edge.

The article explores the possibility of improving price forecasting based on trading volume analysis by integrating technical analysis principles with LSTM neural network architecture. Particular attention is paid to the detection and interpretation of anomalous volumes, the use of clustering and the creation of features based on volumes and their definition in the context of machine learning.

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.

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.

Explore how Vector Autoregression (VAR) models can forecast Forex OHLC (Open, High, Low, and Close) time series data. This article covers VAR implementation, model training, and real-time forecasting in MetaTrader 5, helping traders analyze interdependent currency movements and improve their trading strategies.

The Moving-Average-Convergence-Divergence (MACD) oscillator and the On-Balance-Volume (OBV) oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This pairing, as is practice in these article series, is complementary with the MACD affirming trends while OBV checks volume. As usual, we use the MQL5 wizard to build and test any potential these two may possess.

In this article, we discuss Directional Diffusion Models that exploit data-dependent anisotropic and directed noise in a forward diffusion process to capture meaningful graph representations.

We invite you to get acquainted with the NAFS (Node-Adaptive Feature Smoothing) method, which is a non-parametric approach to creating node representations that does not require parameter training. NAFS extracts features of each node given its neighbors and then adaptively combines these features to form a final representation.

The Parabolic-SAR (SAR) and the Relative Vigour Index (RVI) are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered in the past, is also complementary since SAR defines the trend while RVI checks momentum. As usual, we use the MQL5 wizard to build and test any potential this indicator pairing may have.

In the previous last article within this series, we looked at the Atom-Motif Contrastive Transformer (AMCT) framework, which uses contrastive learning to discover key patterns at all levels, from basic elements to complex structures. In this article, we continue implementing AMCT approaches using MQL5.

In this article, we implement trade execution and risk management for the Envelopes Trend Bounce Scalping Strategy in MQL5. We implement order placement and risk controls like stop-loss and position sizing. We conclude with backtesting and optimization, building on Part 18’s foundation.

In this article, we build a multi-timeframe scanner dashboard in MQL5 to display real-time trading signals. We plan an interactive grid interface, implement signal calculations with multiple indicators, and add a close button. The article concludes with backtesting and strategic trading benefits

When we use models to analyze the market situation, we mainly focus on the candlestick. However, it has long been known that candlestick patterns can help in predicting future price movements. In this article, we will get acquainted with a method that allows us to integrate both of these approaches.

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.

The Triple Exponential Moving Average Oscillator (TRIX) and the Williams Percentage Range Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered recently, is also complementary given that TRIX defines the trend while Williams Percent Range affirms support and Resistance levels. As always, we use the MQL5 wizard to prototype any potential these two may have.

In this article, we enhance the MQL5 Economic Calendar by introducing a draggable dashboard that allows us to reposition the interface for better chart visibility. We implement hover effects for buttons to improve interactivity and ensure seamless navigation with a dynamically positioned scrollbar.

Candlestick patterns offer valuable insights into potential market moves. Some single candles signal continuation of the current trend, while others foreshadow reversals, depending on their position within the price action. This article introduces an EA that automatically identifies four key candlestick formations. Explore the following sections to learn how this tool can enhance your price-action analysis.

We continue the work started in the previous article on building the RefMask3D framework using MQL5. This framework is designed to comprehensively study multimodal interaction and feature analysis in a point cloud, followed by target object identification based on a description provided in natural language.

Today we will look at why we need the iSpread feature. At the same time, we will understand how the system informs us about the remaining time of the bar when there is not a single tick available for it. 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.

The FrAMA Indicator and the Force Index Oscillator are trend and volume tools that could be paired when developing an Expert Advisor. We continue from our last article that introduced this pair by considering machine learning applicability to the pair. We are using a convolution neural network that uses the dot-product kernel in making forecasts with these indicators’ inputs. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

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.

In this article. we will discuss a method of complex multimodal interaction analysis and feature understanding.

In this article, we enhance the MQL5 Economic Calendar with a dynamic scrollbar for intuitive news navigation. We ensure seamless event display and efficient updates. We validate the responsive scrollbar and polished dashboard through testing.

The Fractal Adaptive Moving Average (FrAMA) and the Force Index Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. These two indicators complement each other a little bit because FrAMA is a trend following indicator while the Force Index is a volume based oscillator. As always, we use the MQL5 wizard to rapidly explore any potential these two may have.

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.

While analyzing the market situation, we divide it into separate segments, identifying key trends. However, traditional analysis methods often focus on one aspect and thus limit the proper perception. In this article, we will learn about a method that enables the selection of multiple objects to ensure a more comprehensive and multi-layered understanding of the situation.

An MQL5 framework that brings institutional-grade execution algorithms (TWAP, VWAP, Iceberg) to retail traders through a unified execution manager and performance analyzer for smoother, more precise order slicing and analytics.