In this article, we will continue to modify the Expert Advisor we have been working on throughout the preceding articles of the MQL5 Cookbook series. This time, the Expert Advisor will be enhanced with indicators whose values will be used to check position opening conditions. To spice it up, we will create a drop-down list in the external parameters to be able to select one out of three trading indicators.
In continuation of our work on the Expert Advisor from the previous article of the series called "MQL5 Cookbook: Analyzing Position Properties in the MetaTrader 5 Strategy Tester", we will enhance it with a whole lot of useful functions, as well as improve and optimize the existing ones. The Expert Advisor will this time have external parameters that can be optimized in the MetaTrader 5 Strategy Tester and will in some ways resemble a simple trading system.
This time we will create a simple Expert Advisor that will get position properties on the current symbol and display them on the custom info panel during manual trading. The info panel will be created using graphical objects and displayed information will be refreshed at every tick. This is going to be much more convenient than all the time having to manually run the script described in the previous article of the series called "MQL5 Cookbook: Getting Position Properties".
The article focuses on standard MQL5 functions for working with time, as well as programming techniques and practically useful functions for working with time that are required when creating Expert Advisors and indicators. Particular attention is paid to the general theory of time measurement. This article should be of interest primarily to novice MQL5 programmers.
Nowadays, every trader must have heard of neural networks and knows how cool it is to use them. The majority believes that those who can deal with neural networks are some kind of superhuman. In this article, I will try to explain to you the neural network architecture, describe its applications and show examples of practical use.
In addition to creation of neuronets, the NeuroSolutions software suite allows exporting them as DLLs. This article describes the process of creating a neuronet, generating a DLL and connecting it to an Expert Advisor for trading in MetaTrader 5.
This article explains how to use the major functionalities of the MQL5 Standard Library Trade Classes in writing Expert Advisors which implements position closing and modifying, pending order placing and deletion and verifying of Margin before placing a trade. We have also demonstrated how Trade classes can be used to obtain order and deal details.
The basic rule of trader - let profit to grow, cut off losses! This article considers one of the basic techniques, allowing to follow this rule - moving the protective stop level (Stop loss level) after increasing position profit, i.e. - Trailing Stop level. You'll find the step by step procedure to create a class for trailing stop on SAR and NRTR indicators. Everyone will be able to insert this trailing stop into their experts or use it independently to control positions in their accounts.
The problem of calculation of the total position volume of the specified symbol and magic number is considered in this article. The proposed method requests only the minimum necessary part of the history of deals, finds the closest time when the total position was equal to zero, and performs the calculations with the recent deals. Working with global variables of the client terminal is also considered.
The concept of diversification of assets on financial markets is quiet old, and has always attracted beginner traders. In this article, the author proposes a maximally simple approach to a construction of a multi-currency Expert Advisor, for an initial introduction to this direction of trading strategies.
The article covers the problem of development of active control panels in MQL5. Interface elements are managed by the event handling mechanism. Besides, the option of a flexible setup of control elements properties is available. The active control panel allows working with positions, as well setting, modifying and deleting market and pending orders.
By creating a sample program of visual design, we demonstrate how to design and construct classes in MQL5. The article is written for beginner programmers, who are working on MT5 applications. We propose a simple and easy grasping technology for creating classes, without the need to deeply immerse into the theory of object-oriented programming.
MQL5 gave a mass of innovations, including work with events of various types (timer events, trade events, custom events, etc.). Ability to handle events allows you to create completely new type of programs for automatic and semi-automatic trading. In this article we will consider trade events and write some code for the OnTrade() function, that will process the Trade event.
Layered memory approaches that mimic human cognitive processes enable the processing of complex financial data and adaptation to new signals, thereby improving the effectiveness of investment decisions in dynamic markets.
We continue our work on creating the FinMem framework, which uses layered memory approaches that mimic human cognitive processes. This allows the model not only to effectively process complex financial data but also to adapt to new signals, significantly improving the accuracy and effectiveness of investment decisions in dynamically changing markets.
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.
Today, we take an important step toward helping every developer understand how to read class structures and quickly build Expert Advisors using the MQL5 Standard Library. The library is rich and expandable, yet it can feel like being handed a complex toolkit without a manual. Here we share and discuss an alternative integration routine—a concise, repeatable workflow that shows how to connect classes reliably in real projects.
We invite you to explore FinAgent, a multimodal financial trading agent framework designed to analyze various types of data reflecting market dynamics and historical trading patterns.
This article explains how to build an Expert Advisor (EA) that interacts with chart objects, particularly trend lines, to identify and trade breakout and reversal opportunities. You will learn how the EA confirms valid signals, manages trade frequency, and maintains consistency with user-selected strategies.
In the previous article, we introduced the multi-agent self-adaptive framework MASA, which combines reinforcement learning approaches and self-adaptive strategies, providing a harmonious balance between profitability and risk in turbulent market conditions. We have built the functionality of individual agents within this framework. In this article, we will continue the work we started, bringing it to its logical conclusion.
We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns.
This article teaches you how to build an MQL5 Expert Advisor that automatically detects support and resistance zones and executes trades based on them. You’ll learn how to program your EA to identify these key market levels, monitor price reactions, and make trading decisions without manual intervention.
Learn how to build a Smart Trade Manager Expert Advisor in MQL5 that automates trade management with break-even, trailing stop, and partial close features. A practical, step-by-step guide for traders who want to save time and improve consistency through automation.
In this article, we build an MQL5 EA that detects regular RSI divergences using swing points with strength, bar limits, and tolerance checks. It executes trades on bullish or bearish signals with fixed lots, SL/TP in pips, and optional trailing stops. Visuals include colored lines on charts and labeled swings for better strategy insights.
We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks.
This article is about orthogonal polynomials. Their use can become the basis for a more accurate and effective analysis of market information allowing traders to make more informed decisions.
In this article, we build an MQL5 EA that detects hidden RSI divergences via swing points with strength, bar ranges, tolerance, and slope angle filters for price and RSI lines. It executes buy/sell trades on validated signals with fixed lots, SL/TP in pips, and optional trailing stops for risk control.
Learn how to build a clean and professional on-chart control panel in MQL5 for a Risk-Based Trade Placement Expert Advisor. This step-by-step guide explains how to design a functional GUI that allows traders to input trade parameters, calculate lot size, and prepare for automated order placement.
I invite you to explore the MacroHFT framework, which applies context-aware reinforcement learning and memory to improve high-frequency cryptocurrency trading decisions using macroeconomic data and adaptive agents.
Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data — organized in rows and columns — we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.
In this article, we develop an MQL5 Expert Advisor for statistical mean reversion trading, calculating moments like mean, variance, skewness, kurtosis, and Jarque-Bera statistics over a specified period to identify non-normal distributions and generate buy/sell signals based on confidence intervals with adaptive thresholds
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.
Learn how to build an interactive MQL5 Expert Advisor with an on-chart control panel. Know how to compute risk-based lot sizes and place trades directly from the chart.
In Part 5 of our MQL5 AI trading system series, we enhance the ChatGPT-integrated Expert Advisor by introducing a collapsible sidebar, improving navigation with small and large history popups for seamless chat selection, while maintaining multiline input handling, persistent encrypted chat storage, and AI-driven trade signal generation from chart data.
We invite you to get acquainted with the Hierarchical Double-Tower Transformer (Hidformer) framework, which was developed for time series forecasting and data analysis. The framework authors proposed several improvements to the Transformer architecture, which resulted in increased forecast accuracy and reduced computational resource consumption.
In Part 6 of our MQL5 AI trading system series, we advance the ChatGPT-integrated Expert Advisor by introducing chat deletion functionality through interactive delete buttons in the sidebar, small/large history popups, and a new search popup, allowing traders to manage and organize persistent conversations efficiently while maintaining encrypted storage and AI-driven signals from chart data.
Gamma and Delta were originally developed as risk-management tools for hedging options exposure, but over time they evolved into powerful instruments for advanced scalping, order-flow modeling, and microstructure trading. Today, they serve as real-time indicators of price sensitivity and liquidity behavior, enabling traders to anticipate short-term volatility with remarkable precision.
In this article, we develop a Candle Range Theory (CRT) trading system in MQL5 that identifies accumulation ranges on a specified timeframe, detects breaches with manipulation depth filtering, and confirms reversals for entry trades in the distribution phase. The system supports dynamic or static stop-loss and take-profit calculations based on risk-reward ratios, optional trailing stops, and limits on positions per direction for controlled risk management.
Learn how to build a complete Kagi Chart engine in MQL5—constructing price reversals, generating dynamic line segments, and updating Kagi structures in real time. This first part teaches you how to render Kagi charts directly on MetaTrader 5, giving traders a clear view of trend shifts and market strength while preparing for automated Kagi-based trading logic in Part 2.
Today, we use the MQL5 Standard Library to build custom signal classes and let the MQL5 Wizard assemble a professional Expert Advisor for us. This approach simplifies development so that even beginner programmers can create robust EAs without in-depth coding knowledge, focusing instead on tuning inputs and optimizing performance. Join this discussion as we explore the process step by step.
