Articles with examples of trading robots developed in MQL5

In this article, we develop the Adaptive Crossover RSI Trading Suite System, which uses 14- and 50-period moving average crossovers for signals, confirmed by a 14-period RSI filter. The system includes a trading day filter, signal arrows with annotations, and a real-time dashboard for monitoring. This approach ensures precision and adaptability in automated trading.

The article provides the code and the description of the main stages of the machine learning process using a specific example. To obtain the model, you do not need Python or R knowledge. Furthermore, basic MQL5 knowledge is enough — this is exactly my level. Therefore, I hope that the article will serve as a good tutorial for a broad audience, assisting those interested in evaluating machine learning capabilities and in implementing them in their programs.

In high-probability support and resistance zones, valid entry confirmation signals are always present once the zone has been correctly identified. In this discussion, we build an intelligent MQL5 program that automatically detects entry conditions within these zones. We leverage well-known candlestick patterns alongside native confirmation indicators to validate trade decisions. Click to read further.

I invite you to get acquainted with the Multi-Agent Self-Adaptive (MASA) framework, which combines reinforcement learning and adaptive strategies, providing a harmonious balance between profitability and risk management in turbulent market conditions.

As the next step in studying neural networks, I suggest considering the methods of increasing convergence during neural network training. There are several such methods. In this article we will consider one of them entitled Dropout.

This is one of the most powerful indicators currently existing. Anyone who trades trying to have a certain degree of confidence must have this indicator on their chart. Most often the indicator is used by those who prefer “tape reading” while trading. Also, this indicator can be utilized by those who use only Price Action while trading.

In this article, we build an MQL5 Expert Advisor to detect Butterfly harmonic patterns. We identify pivot points and validate Fibonacci levels to confirm the pattern. We then visualize the pattern on the chart and automatically execute trades when confirmed.

In this article, we develop a trendline trader program that uses least squares fit to detect support and resistance trendlines, generating dynamic buy and sell signals based on price touches and open positions based on generated signals.

The article discusses, in a detailed perspective, the steps that need to be implemented towards the creation of an expert advisor based on the Zone Recovery trading algorithm. This helps aotomate the system saving time for algotraders.

In this article, we implement an adaptive Linear Regression Channel system in MQL5 that automatically calculates the regression line and standard deviation channel over a user-defined period, only activates when the slope exceeds a minimum threshold to confirm a clear trend, and dynamically recreates or extends the channel when the price breaks out by a configurable percentage of channel width.

In this article, we enhance the AI-powered trading system's modularity by separating UI components into a dedicated include file. The system now automates trade execution based on AI-generated signals, parsing JSON responses for BUY/SELL/NONE with entry/SL/TP, visualizing patterns like engulfing or divergences on charts with arrows, lines, and labels, and optional auto-signal checks on new bars.

The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders an more) for more than 1 symbol pair only from one symbol chart.

In previous articles, we have already tested various options for organizing neural networks. We also considered convolutional networks borrowed from image processing algorithms. In this article, I suggest considering Attention Mechanisms, the appearance of which gave impetus to the development of language models.

In this article, I will consider the possibility of upgrading the CCI indicator. Besides, I will present a modification of the indicator.

This article outlines the steps to create an Expert Advisor (EA) that capitalizes on price breakouts after consolidation periods. By identifying consolidation ranges and setting breakout levels, traders can automate their trading decisions based on this strategy. The Expert Advisor aims to provide clear entry and exit points while avoiding false breakouts

This article is the first installment in a two-part series designed to impart practical skills and best practices for writing custom indicators in MQL5. Using Heikin Ashi as a working example, the article explores the theory behind Heikin Ashi charts, explains how Heikin Ashi candlesticks are calculated, and demonstrates their application in technical analysis. The centerpiece is a step-by-step guide to developing a fully functional Heikin Ashi indicator from scratch, with clear explanations to help readers understand what to code and why. This foundational knowledge sets the stage for Part Two, where we will build an expert advisor that trades based on Heikin Ashi logic.

Smart Money Concept (Break Of Structure) coupled with the RSI Indicator to make informed automated trading decisions based on the market structure.

In this article, we examine the Profitunity System by Bill Williams, breaking down its core components and unique approach to trading within market chaos. We guide readers through implementing the system in MQL5, focusing on automating key indicators and entry/exit signals. Finally, we test and optimize the strategy, providing insights into its performance across various market scenarios.