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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Developing a Replay System (Part 55): Control Module

In this article, we will implement a control indicator so that it can be integrated into the message system we are developing. Although it is not very difficult, there are some details that need to be understood about the initialization of this module. The material presented here is for educational purposes only. In no way should it be considered as an application for any purpose other than learning and mastering the concepts shown.

MQL5 Trading Tools (Part 27): Rendering Parametric Butterfly Curve on Canvas

In this article, we explore the butterfly curve, a parametric mathematical equation, and render it visually on a MQL5 canvas. We build an interactive display with a draggable, resizable canvas window, supersampled curve rendering, gradient backgrounds, and a color-segmented legend. By the end, we have a fully functional visual tool that plots the butterfly curve directly on the MetaTrader 5 chart.

Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia

We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.

Building a Trade Analytics System (Part 2): How to Capture Closed Trades and Send JSON in MQL5

We build a lightweight bridge that captures closed trades in MetaTrader 5 and sends them to an external backend over HTTP as JSON. It uses OnTradeTransaction for event detection, reads details from deal history, assembles a JSON payload, and posts it via WebRequest. A local Flask API is used to test the flow, delivering a working path to move trade data outside the terminal.

Data Science and ML (Part 48): Are Transformers a Big Deal for Trading?

From ChatGPT to Gemini and many model AI tools for text, image, and video generation. Transformers have rocked the AI-world. But, are they applicable in the financial (trading) space? Let's find out.

Graph Theory: Traversal Breadth-First Search (BFS) Applied in Trading

Breadth First Search (BFS) uses level-order traversal to model market structure as a directed graph of price swings evolving through time. By analyzing historical bars or sessions layer by layer, BFS prioritizes recent price behavior while still respecting deeper market memory.

Implementing Practical Modules from Other Languages in MQL5 (Part 05): The Logging module from Python, Log Like a Pro

Integrating Python's logging module with MQL5 empowers traders with a systematic logging approach, simplifying the process of monitoring, debugging, and documenting trading activities. This article explains the adaptation process, offering traders a powerful tool for maintaining clarity and organization in trading software development.

Evaluating the Quality of Forex Spread Trading Based on Seasonal Factors in MetaTrader 5

The article examines the quality of a seasonal trading approach on a daily timeframe, both for individual symbols and for spreads. Particular attention is paid to identifying recurring monthly cycles and the possibilities of their application in trading within the current year.

Developing a Replay System (Part 39): Paving the Path (III)

Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.

Evaluating the Quality of Forex Spread Trading Based on Seasonal Factors in MetaTrader 5

Developing a Replay System (Part 41): Starting the second phase (II)

If everything seemed right to you up to this point, it means you're not really thinking about the long term, when you start developing applications. Over time you will no longer need to program new applications, you will just have to make them work together. So let's see how to finish assembling the mouse indicator.

Developing a Replay System (Part 34): Order System (III)

In this article, we will complete the first phase of construction. Although this part is fairly quick to complete, I will cover details that were not discussed previously. I will explain some points that many do not understand. Do you know why you have to press the Shift or Ctrl key?

Implementing a Breakeven Mechanism in MQL5 (Part 2): ATR- and RRR-Based Breakeven

This article completes the implementation of ATR- and RRRR-based breakeven mechanisms in MQL5 and develops, from scratch, a class that makes it easy to switch breakeven modes without having to enter the parameters again. To evaluate the effectiveness of each breakeven type, several backtests are run, analyzing their advantages and disadvantages in the context of algorithmic trading.

MQL5 Trading Tools (Part 18): Rounded Speech Bubbles/Balloons with Orientation Control

This article shows how to build rounded speech bubbles in MQL5 by combining a rounded rectangle with a pointer triangle and controlling orientation (up, down, left, right). It details geometry precomputation, supersampled filling, rounded apex arcs, and segmented borders with an extension ratio for seamless joins. Readers get configurable code for size, radii, colors, opacity, and thickness, ready for alerts or tooltips in trading interfaces.

Price-Driven CGI Model: Advanced Data Post-Processing and Implementation

In this article, we will explore the development of a fully customizable Price Data export script using MQL5, marking new advancements in the simulation of the Price Man CGI Model. We have implemented advanced refinement techniques to ensure that the data is user-friendly and optimized for animation purposes. Additionally, we will uncover the capabilities of Blender 3D in effectively working with and visualizing price data, demonstrating its potential for creating dynamic and engaging animations.

MQL5 Trading Toolkit (Part 7): Expanding the History Management EX5 Library with the Last Canceled Pending Order Functions

Learn how to complete the creation of the final module in the History Manager EX5 library, focusing on the functions responsible for handling the most recently canceled pending order. This will provide you with the tools to efficiently retrieve and store key details related to canceled pending orders with MQL5.

Engineering Trading Discipline into Code (Part 4): Enforcing Trading Hours and News Disabling in MQL5

An MQL5 control system that blocks orders outside scheduled trading hours and during scheduled news releases, converting time rules into executable restrictions. It combines a permissions management mechanism, a transaction-level expert advisor, and a visual dashboard for real-time status and upcoming restrictions. Configuration is accomplished using editable files, with caching and a CSV audit log for traceability.

Implementing a Breakeven Mechanism in MQL5 (Part 2): ATR- and RRR-Based Breakeven

MQL5 Trading Toolkit (Part 7): Expanding the History Management EX5 Library with the Last Canceled Pending Order Functions

MQL5 Trading Tools (Part 33): Building a Rich Content Markup Documentation System for MQL5 Programs

We extend the Part 9 setup wizard to build a canvas-based, in-chart documentation system for MetaTrader 5. The panel is tabbed and scrollable, supports inline styling, images, and interactive controls, and renders with supersampled anti-aliasing. The result is a reusable engine that any MQL5 program can embed to deliver self-contained documentation directly on the chart.

Engineering Trading Discipline into Code (Part 4): Enforcing Trading Hours and News Disabling in MQL5

MQL5 Wizard Techniques you should know (Part 29): Continuation on Learning Rates with MLPs

We wrap up our look at learning rate sensitivity to the performance of Expert Advisors by primarily examining the Adaptive Learning Rates. These learning rates aim to be customized for each parameter in a layer during the training process and so we assess potential benefits vs the expected performance toll.

Developing a Replay System (Part 56): Adapting the Modules

Although the modules already interact with each other properly, an error occurs when trying to use the mouse pointer in the replay service. We need to fix this before moving on to the next step. Additionally, we will fix an issue in the mouse indicator code. So this version will be finally stable and properly polished.

MQL5 Trading Tools (Part 30): Class-Based Tool Palette Sidebar

We refactor the Tools Palette from a flat, function-based panel into a modular, class-driven sidebar in MQL5. The design introduces supersampled canvas rendering for anti-aliased shapes, theme control, a category registry, snap alignment, and selective corner rounding. The result is a reusable, scalable sidebar foundation that you can extend with tool selection, dragging, and fly-out menus in future steps.

Backtracking Search Algorithm (BSA)

What if an optimization algorithm could remember its past journeys and use that memory to find better solutions? BSA does just that – balancing exploration with revisiting the tried and true. In this article, we reveal the secrets of the algorithm. A simple idea, minimum parameters and a stable result.

MQL5 Trading Tools (Part 28): Filling Sweep Polygons for Butterfly Curve in MQL5

We expand the capabilities of the MetaTrader 5 butterfly curve canvas by adding multi-layered wing fills, vein lines, scale dots, and a full body (abdomen, thorax, head, eyes, antennae). This article implements polygon fills with vertical and radial gradients, as well as filled circles and ellipses, all using supersampling antialiasing. You will also receive reusable MQL5 helper functions and a rendering order that transforms a simple curve into a customizable, detailed chart illustration.

RiskGate: Centralized Risk Management for Multiple EAs

Many MetaTrader 5 setups run several EAs on one account, so risk gets fragmented and correlated exposure slips through. The article introduces RiskGate, a centralized Service that evaluates EA intents account‑wide: EAs send a JSON signal, the Service returns approved, lot and reason. You will see the client/server wiring, example rules (daily loss, exposure and correlation caps), unit‑tested handler design, and an EA example. The result is consistent portfolio‑level risk with simpler EAs.

MQL5 Trading Tools (Part 29): Step-by-Step Butterfly Animation on Canvas

In this article, we expand our butterfly animation program with a four-stage animation pipeline: sequential curve drawing, smooth wing fill fading, detailed body rendering, and continuous flight. We implement a timer-driven state machine, four oscillators for wing flapping, vertical bobbing, horizontal sway, and tilt, as well as a neon glow around the wing outlines and a cyclical color change based on hue. You will learn how to structure these effects on the MetaTrader 5 canvas for clean and controlled playback.

MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

Price Action Analysis Toolkit Development (Part 74): Building an MQL5 Expert Advisor from Indicator Buffers

This article implements an MQL5 Expert Advisor that connects to a weekend gap indicator via iCustom and CopyBuffer, reading six buffers for buy/sell signals and SL/TP. It validates broker stop-distance rules, handles closed-bar confirmation and duplicate-signal control, and executes orders with a configurable magic number. The EA also includes midpoint stop-loss management and a backtesting procedure so you can verify behavior and adapt parameters to your setup.

MQL5 Wizard Techniques you should know (Part 92): Using B-Tree Indexing and a Bayesian NN in a Custom Signal Class

In this article we present yet another custom MQL5 Signal Class that we are labelling ‘CSignalBTreeBayesian’. We are marrying the algorithm of a balanced tree with a neural network that is built on Bayesian principles to formulate yet another custom signal testable independently or with other signals thanks to the MQL5 Wizard.

Developing a Replay System (Part 58): Returning to Work on the Service

After a break in development and improvement of the service used for replay/simulator, we are resuming work on it. Now that we've abandoned the use of resources like terminal globals, we'll have to completely restructure some parts of it. Don't worry, this process will be explained in detail so that everyone can follow the development of our service.

MQL5 Wizard Techniques you should know (Part 90): Fenwick Tree Money Management with 1D CNN in MQL5

This article implements a Fenwick Tree (Binary Indexed Tree) for volume-aware money management inside an MQL5 Wizard Expert Advisor. We structure cumulative volume in O(log n) and apply four scaling modes—linear, conservative, aggressive, and mean-reversion—optionally gated by a lightweight 1D CNN. Practical tests compare the algorithm alone versus the CNN‑filtered approach to illustrate adaptive lot sizing and risk control under varying volume topologies.

MQL5 Bootstrap (I): Reusable Functions for Working with Positions and Orders

This article presents a compact MQL5 utility layer for routine trade operations. It includes position existence checkers, position counters, bulk close helpers, and functions to retrieve the most recent or oldest position by symbol, magic, or type. A simple SMA crossover Expert Advisor demonstrates integration. The result is cleaner EAs, fewer inconsistencies across projects, and faster maintenance.

Exchange Market Algorithm (EMA)

The article presents a detailed analysis of the Exchange Market Algorithm (EMA) inspired by the behavior of stock market traders. The algorithm simulates stock trading, where market participants with varying levels of success employ different strategies to maximize profits.

Seasonality Indicator by Hours, Days of the Week, and Days of the Month

The article explains how to develop a tool for analyzing recurring price patterns in financial markets — by day of the month (1-31), day of the week (Monday-Sunday), or hour of the day (0-23). The indicator analyzes historical data, calculates the average return for each period, and displays the results as a histogram with a forecast. It includes customizable parameters: seasonality type, number of bars analyzed, display as percentages or absolute values, chart colors.

Price Action Analysis Toolkit Development (Part 71): Weekend Gap Structure Mapping in MQL5

The article delivers an object-based MQL5 implementation that detects weekend gaps from time discontinuities and renders them directly on the chart. It manages graphical objects, tracks state transitions (fresh, partial, reaction, filled), and preserves completed gaps as historical zones. The result is a reproducible framework for monitoring how price revisits and fills weekend gap structures.

Neural Networks in Trading: LSTM Optimization for Multivariate Time Series Forecasting (DA-CG-LSTM)

This article introduces the DA-CG-LSTM algorithm, which offers new approaches to time series analysis and forecasting. It explains how innovative attention mechanisms and model flexibility can improve forecast accuracy.

Beyond GARCH (Part I): Mandelbrot's MMAR versus Engle's GARCH

This article starts the MMAR pipeline on EURUSD M5 data. We load market data via the MetaTrader5 Python API and run partition-function analysis with non-overlapping intervals to test for multifractal scaling. The result is an evidence-based decision on fractality, a prerequisite for building MMAR and for choosing whether to proceed beyond GARCH.

Neural Networks in Trading: LSTM Optimization for Multivariate Time Series Forecasting (Final Part)

We continue to implement the DA-CG-LSTM framework, which offers innovative methods for time series analysis and forecasting. The use of CG-LSTM and dual attention allows for more accurate detection of both long-term and short-term dependencies in data, which is particularly useful for working with financial markets.

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