Articles on MetaTrader 5 integration using MQL5

This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.

Discover how to detect user actions in MetaTrader 5, send requests to an AI API, extract responses, and implement scrolling text in your panel.

In this article, we present the Arithmetic Optimization Algorithm (AOA) based on simple arithmetic operations: addition, subtraction, multiplication and division. These basic mathematical operations serve as the foundation for finding optimal solutions to various problems.

The DI crossover often triggers in ranges where +DI and -DI oscillate without persistence. We build a two-layer hybrid: Optuna's TPE optimizes a regime gate over ADXR threshold, DI lookback, and minimum DI separation to maximize signal precision on a held-out window, then a Random Forest uses eleven ADX-derived features to accept or scale entries via afml.bet_sizing. The result filters ranging-market bursts and calibrates position size on EURUSD H1.

The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.

A reproducible, read-only Python audit for MetaTrader 5 that verifies history quality before any backtest. It exports M5 data from multiple terminals, detects gaps and synthetic bars by timestamp spacing, and reports coverage per year. The same deterministic strategy then runs on three broker feeds over a common window to quantify result drift and decompose it into spread, data/price, and trade effects.

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.

Secrets of effective optimization of trading strategies in metaheuristic approaches. Community of Scientists Optimization is a new population-based algorithm inspired by the mechanisms of the scientific community. Unlike traditional nature-inspired metaphors, CoSO models unique aspects of human scientific activity: publishing results in journals, competing for grants, and forming research teams.

In this discussion, we’ll explore additional advancements as we integrate refined event‑alerting logic for the economic calendar events displayed by the News Headline EA. This enhancement is critical—it ensures users receive timely notifications a short time before key upcoming events. Join this discussion to discover more.

We extend the stateful supply and demand framework for MetaTrader 5 with a quantitative admission model and a dedicated interaction engine. Candidate zones are scored by structural symmetry, volume participation, and ATR‑normalized displacement, then classified into objective tiers. Admitted zones follow a deterministic lifecycle that tracks first touch, validates bounces, or confirms breakouts, with full telemetry for analysis and reproducibility.

Despite what was shown in the previous article, all of this may seem simple at first. In reality, several problems remain, along with many tasks that still need to be completed. You, dear reader, may imagine that everything is easy and straightforward. Out of inexperience, you may simply accept whatever is presented to you. And that is a mistake you should try to avoid. Even worse is trying to use something without truly understanding what exactly you are using. Beginners often pass through a copy-and-paste stage. If you do not want to remain stuck at that stage forever, you should learn how to use certain tools. One of the tools most often used by programmers is documentation. The second is testing, supported by log files. Here we will see how to do this.

An SLSQP optimizer is implemented in MQL5 to resolve parameter discrepancies between a volatility library and Python's ARCH module. The article details constraint handling, gradient options, configuration, and convergence controls and shows how to integrate the solver into existing code. Practical examples and comparisons demonstrate matched log‑likelihoods and parameters on shared datasets.

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.

Learn how to create an EX5 module of exportable functions that seamlessly query and save data for the most recently filled pending order. In this comprehensive step-by-step guide, we will enhance the History Management EX5 library by developing dedicated and compartmentalized functions to retrieve essential properties of the last filled pending order. These properties include the order type, setup time, execution time, filling type, and other critical details necessary for effective pending orders trade history management and analysis.

Biogeography-Based Optimization (BBO) is an elegant global optimization method inspired by natural processes of species migration between islands within archipelagos. The algorithm is based on a simple yet powerful idea: high-quality solutions actively share their characteristics, while low-quality ones actively adopt new features, creating a natural flow of information from the best solutions to the worst. A unique adaptive mutation operator provides an excellent balance between exploration and exploitation. BBO demonstrates high efficiency on a variety of tasks.

Create a practical bridge between MetaTrader 5 and Binance: fetch 30‑minute klines with WebRequest, extract OHLC/time values from JSON, and confirm a bullish engulfing pattern using only completed candles. Then assemble the query string, compute the HMAC‑SHA256 signature, add X‑MBX‑APIKEY, and submit authenticated orders. You get a clear, end‑to‑end EA workflow from data acquisition to order execution.

We resume the topic of optimization by the scientific community. CoSO should not be viewed as a ready-made solution, but as a promising research platform. With proper development, CoSO can find its niche in tasks where adaptability and resilience to change are important, and computation time is not critical.

When running multiple strategies in parallel, you may want to periodically close all open positions and start the strategies over again. The existing code only allows this behavior to be implemented through manual intervention. Let's try to automate this part.

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.

We build a compact profiler that records calls, min/max/average times, and slow-call counts to CSV, and a simple test runner that writes deterministic pass/fail reports. The article explains where to place measurements in an EA, how to sample ticks, and how to keep pure calculations testable. Running the script first and the profiling EA second provides repeatable evidence for regression analysis.

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.

The article presents a new metaheuristic method based on the hunting strategies of Australian dingoes: group attack, chase, and scavenging. Let's see how the Dingo Optimization Algorithm (DOA) performs algorithmically.

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.

XLV is SPDR healthcare ETF and in an age where it is common to be bombarded by a wide array of traditional news items plus social media feeds, it can be pressing to select a data set for use with a model. We try to tackle this problem for this ETF by sizing up some of its critical data sets in MQL5.

Building on the partition function analysis from Part 1, this article deepens the theoretical foundation before completing the analytical pipeline. We first give a full treatment of the Hurst exponent: what it measures, what it implies about market memory, and why it matters for the MMAR. This is followed by an intuitive exploration of multifractal spectra and what f(α) reveals about volatility heterogeneity. We then move to implementation: extracting the scaling function τ(q), estimating H via R/S analysis, and fitting the multifractal spectrum across four candidate distributions. By the end, we have the complete parameter set needed to construct the MMAR process in Part 3. Part 2 of an eight-part series.

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.

Before you give up and decide to abandon learning SQL, allow me to remind you, dear readers, that here we are still using only the most basic elements. We have not yet looked at some of SQL's capabilities. Once you understand them, you will see that SQL is far more practical than it seems. Although, most likely, we will eventually change the direction of what we are building, because the creation process is dynamic. We will show a little more about creating different things in SQL, because this is truly important and useful for you. Simply thinking that you are more capable than an entire community of programmers and developers will only lead to wasted time and opportunities. Do not worry, because what comes next will be even more interesting.