The article discusses the ECO optimization algorithm, which is based on ecological concepts: populations are grouped into habitats based on territorial proximity, exchange genetic material within habitats, and migrate between them. Despite its wide range of operators and elegant biological metaphor, the algorithm produced a certain result discussed below.
Debugging is an integral part of the programming cycle. This article discusses common techniques for debugging any application running in the MetaTrader 5 environment.
This second part adds the geometry layer to a Cairo‑inspired graphics library for MetaTrader 5. It defines a path of double‑precision points grouped into contours, records open/closed intent, and stores vertices in a flat array with start indices. We implement MoveTo, LineTo, Close, provide basic shape helpers, and include a demo that visualizes the built geometry for inspection and reuse.
Now we will implement this solution, since MQL5 is based on the same principles as event-driven programmingю Developers often use this model when creating DLLs. I know that at first, the event-driven model will seem confusing and illogical. But in this article, I will explain the principles of event-driven programming in a way that is easier to understand, so that if you are just getting started, you will have a clear grasp of how it works. Understanding what I am about to explain in this article will help you throughout your work as a programmer.
In this article, we will look at how to easily implement an indicator that shows whether a position is generating a profit or a loss. The procedure is simple and effective. Even without in-depth expertise, this indicator will allow you to easily recognize when to close a position. This way, you will avoid unexpected results, since the calculation reflects the actual outcome you would get if you closed the position.
The article presents a reproducible implementation of a hybrid quantum-neural network model for algorithmic trading on Forex without using real quantum hardware. A fixed three-qubit quantum circuit in IBM Qiskit converts sliding-window statistics (mean returns, volatility, and range) into a probability distribution, from which seven quantum metrics are calculated. These features are integrated into a bidirectional LSTM architecture with regularization and mechanisms to address class imbalance, including focal loss and a sampler.
The article presents the full integration of the 3D-bar module into a quantum-enhanced trading system for forecasting the movement of currency pairs. The system combines stationary four-dimensional features, an 8-qubit quantum encoder, and CatBoost gradient boosting with 52+ features. The system is implemented in Python using MetaTrader 5, Qiskit, CatBoost, and optional integration with the Llama 3.2 LLM for interpreting forecasts.
In this article, you will learn how to create a visual signal on your trading platform so you can determine directly on the chart whether a position is long or short, without having to open the Terminal. In addition, the article also explains how to implement a feature that improves the display when moving Take Profit and Stop Loss lines by hiding the horizontal line that follows the mouse cursor while these lines are being moved, to avoid confusion. The article provides practical insight into setting up market simulation systems.
The ECO (Ecological Cycle Optimizer) algorithm offers an interesting metaphor for applying the concept of the ecological cycle to the field of metaheuristic optimization. The idea of dividing a population into trophic levels — producers, herbivores, carnivores, omnivores, and decomposers — creates a hierarchical search structure, in which each group contributes to the overall optimization process.
This article implements a lock-free shared-memory bridge in MetaTrader 5 that writes ticks in Apache Arrow’s columnar layout via the C Data Interface. It details the column layout, double buffering with a seqlock, and a batching strategy. You get full source for a writer class, a streaming Expert Advisor, and a self-test script that validates correctness before any Python reader is involved.
In this article, I will show you, dear reader, how to select the objects we create on the chart and modify the position indicator so that it can perform many more functions than originally intended. We will look at how to implement the ability to move price levels and create price lines directly on the chart. Many people may find this difficult. However, you will see that we'll do this with minimal effort. You just need to give it a little thought.
We need a way to handle the graphical objects we create. The approach presented in the previous article works very well for certain scenarios. In this case, we will need something more complex, given the specific nature of the problem at hand. Therefore, we will not attempt to replace the ZOrder management mechanisms already present in MetaTrader 5, nor, of course, will we check which object is in the foreground or covered by another object. We are going to do something completely different. Here, I will show you what changes need to be made to the code in order to use part of what MetaTrader 5 already does for us.
In this turning-point article, we will begin to explore in greater depth the interaction between the applications we are developing to ensure full support for the replay/simulation system. Here we will analyze a problem that, on the one hand, is quite unpleasant, but on the other hand, is very interesting to explain and solve. The problem is this: how can we restore the take-profit and stop-loss lines after they have been deleted, and do so without using the terminal by performing the operation directly on the chart? At first glance, it seems simple. However, there are several obstacles that must be overcome.
In the previous article, we considered how to implement a position indicator that allows you to close an open position directly from the chart by interacting with an object available on the chart. After completing and testing the first mechanism, we began making changes to ensure that take-profit and stop-loss levels could be removed for an open position. However, since the necessary changes required detailed explanations, in that same article I showed only the changes that needed to be made to the expert advisor; I still needed to show the changes that needed to be made to the position indicator.
The Dandelion Optimizer (DO) turns the simple flight of a seed carried by the wind into a mathematical search strategy. The three phases — vortex rising, drift toward the center of the population, and landing along a Lévy-flight trajectory — form an elegant metaphor that yields interesting results in practice.
In this article, we'll start making some improvements to the position indicator so that we can interact with it and modify price lines or close a position directly through the position indicator. Before we move on to the implementation, there are a few things worth clarifying, especially for those who aren't familiar with this. The indicator cannot be used in any way to change anything on the trading server. This is because MetaTrader 5 has a security system in place that allows only Expert Advisors to modify orders and positions. No application other than an Expert Advisor can manipulate orders or positions.
This article opens a step-by-step 2D graphics engine for MetaTrader. It standardizes ARGB colors and implements a reusable surface: a uint pixel buffer uploaded as a dynamic resource and shown via one OBJ_BITMAP_LABEL. You will draw rectangles and a vertical gradient, check real transparency, and learn an efficient update path with a single Flush call.
The Dendritic Cell Algorithm (DCA) is a metaheuristic inspired by the mechanisms of the innate immune system. Dendritic cells patrol the search space, accumulate signals about the quality of positions, and reach a collective decision: whether to exploit what they have found or to continue exploration. Let's take a look at how a biological model for detecting pathogens is transformed into an optimization algorithm.
The article presents an adaptation of the Deterministic Dendritic Cell Algorithm (dDCA) for continuous optimization problems. The algorithm, inspired by the immune system's Danger Theory, uses a signal accumulation mechanism to automatically balance exploration and exploitation within the search space.
The article describes the practical implementation of a hybrid algorithmic trading system that combines quantum computing (IBM Qiskit) and gradient boosting (CatBoost) to predict movements in the EURUSD pair on the hourly time frame. The system extracts four unique quantum features from a probability distribution across 256 states using eight qubits and, in combination with classical indicators and delta encoding of time categories, achieves 62% accuracy on 15,000 candlesticks.
This article details a practical framework for converting MetaTrader 5 trendlines from static drawings into managed runtime entities. It covers object discovery, event-driven synchronization of user edits, and confirmation logic based on ATR multipliers and closed candles. A central manager coordinates multiple lines and updates their visual state. Readers can implement consistent, extensible rules for detecting proximity, validating bounces, and confirming breakouts.
The article describes the development of an MVP prototype for an autonomous trading bot for MetaTrader 5 that uses large language models (LLMs) via the OpenRouter API to analyze the market and make trading decisions. A Python script retrieves historical OHLCV data, sends it to an LLM for technical analysis based on support/resistance levels and Price Action patterns, and then automatically places orders with specified stop loss and take profit levels.
The article describes the process of fine-tuning a language model for trading based on real historical data from MetaTrader 5. The base model, which has only theoretical knowledge of technical analysis, is trained on a thousand examples of the real behavior of currency pairs (EURUSD, GBPUSD, USDCHF, USDCAD) over 180 days. After being trained using Ollama, the model begins to understand the specific characteristics of each instrument.
The article discusses the Differential Search Algorithm (DSA), which simulates the migration of a superorganism in search of optimal living conditions. The algorithm uses a Gamma distribution to generate a pseudo-stable random walk and offers four strategies for selecting the direction of movement, along with three coordinate mutation mechanisms. How will this method perform?
The article describes an approach to trade labeling using oscillators for machine learning models. This eliminates look-ahead bias. It has been shown that this type of labeling does not lead to model overfitting, and the strategies continue to perform well over the long term.
We are adding to our web application the ability to retrieve and display information about the terminal instances’ trading accounts, including balance, profit, connection status, and other important details. We will also implement a flexible configuration system that lets you manage application settings via an external JSON file, and improve the user interface of the main page.
Experimental evaluation on standard benchmark functions reveals the advantages and limitations of directly adapting combinatorial algorithms. The article provides a detailed description of the ECEA algorithm's mechanisms and test results.
A new original population-based algorithm, ECEA, is presented. Inspired by the process of water freezing, it adapts ideas from the Crystal Energy Optimizer (CEO) algorithm, which uses graph-based search, for general optimization problems. The algorithm uses a dynamic elite group, three search strategies, and a periodic diversification mechanism.
A hybrid exit engine for MQL5 replaces static TPs with CRT-derived structural levels. The CRT_ProfitConserve class secures a partial at the first level and then trails the remaining position by structural anchors rather than fixed pips. The article walks through the class API, essential methods, and example usage in EAs, providing a clear path to embed CRT-based exits into existing strategies.
This article presents a reproducible MetaTrader 5 workflow: collect history, engineer nine context features, label simulated EMA crossover trades, train with FLAML, and export to ONNX with fixed opset and plain probabilities. The Expert Advisor loads the model natively, mirrors the Python feature contract, and uses a tunable confidence threshold as a trade filter. Readers can swap signals and features to reuse the same pipeline.
This article presents two versions of the Crystal Structure Algorithm: the original and the modified version. The Crystal Structure Algorithm (CryStAl), published in 2021 and inspired by the physics of crystal structures, was positioned as a parameter-free metaheuristic for global optimization. However, testing revealed a critical problem with the algorithm. A modified version, CryStAlm, is also presented; it addresses the original's key shortcomings.
A metaheuristic algorithm that simulates the growth of coronary arteries in the human heart for optimization problems. It uses the principles of angiogenesis (the growth of new blood vessels), bifurcation (branching), and pruning of weak branches to find optimal solutions in a multidimensional space. Testing its effectiveness across a wide range of tasks yielded unexpected results.
What if lunar cycles and seasonal patterns influence the foreign exchange markets? This article shows how to translate astrological concepts into the language of mathematics and machine learning. I built a Python system with 88 features based on astronomical cycles, trained CatBoost on 15 years of EURUSD data, and obtained some intriguing results. The code is open-source, the methods are verifiable, and the conclusions are unexpected — ancient wisdom meets gradient boosting.
Let's move on to using multiple terminal instances on the server by setting up a simple control panel for starting and stopping them. Now it is time to expand the functionality and move on to the next stages — implementing more complex features, such as managing multiple terminal instances, state persistence, integration with the MetaTrader 5 API, and a web interface with comprehensive information about the terminals.
How can we conveniently monitor multiple terminals running Expert Advisors, especially when they are on different computers? Let's try to create a web interface for managing the launch of MetaTrader 5 trading terminals and viewing detailed information about the operation of each instance.
The Crow Search Algorithm (CSA) is an elegant metaheuristic inspired by crows’ ability to hide food and find other crows' caches, solving optimization problems by balancing following successful solutions with random exploration of the search space. Let's find out how well the algorithm performs.
A new optimization method, the Bison Algorithm (BIA), uses two strategies, inspired by the behavior of bison, for solving continuous problems with a single objective function. The key features of BIA are two fundamental principles borrowed from the behavior of bison: the ability to move dynamically and a defensive strategy.
The article describes an interesting approach to algorithmic trading based on symbolic mathematical equations instead of traditional machine learning "black boxes". The author demonstrates how to transform opaque neural networks into readable mathematical equations using the SymPy library and polynomial regression, allowing for a full understanding of the logic behind trading decisions. The approach combines the computational power of ML with the transparency of classical methods, giving traders the ability to analyze, adjust, and adapt models in real time.
The custom modification of the Dingo algorithm presented in the article has raised the bar for finding the best optimization algorithm. Are even better results possible?
The article presents a step-by-step development of a multi-threaded trading robot with machine learning in Python and MetaTrader 5. The system architecture is considered – from data collection and creation of technical indicators to training XGBoost models with portfolio risk management. The implementation of data augmentation, feature clustering via Gaussian Mixture Models, and flow coordination for parallel trading of multiple currency pairs is described in detail.
