Articles on strategy testing in MQL5
How to develop, write and test a trading strategy, how to find the optimal system parameters and how to analyze the results? The MetaTrader platform offers developers of trading robots rich functionality for fast and accurate testing of trading ideas. Read these articles to learn how to test multi-currency robots and how to use MQL5 Cloud Network for optimization purposes.
Developers of automated trading systems are recommended to start with the testing fundamentals and tick generation algorithms in the strategy tester.
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Creating a Probabilistic Market-Neutral Trading Robot Based on a Return Distribution
A market-neutral trading strategy based on the empirical return distribution offers an alternative to traditional technical analysis methods, replacing price direction forecasting with the statistical placement of orders at levels the price is likely to reach. This article provides a detailed analysis of the mathematical framework for calculating percentiles, algorithms for weighting position sizes based on the probability of an order being triggered, and mechanisms for adapting to changing market conditions through grid expiration. A complete implementation in MQL5 is provided.
Tabu Search (TS)
The article discusses the Tabu Search algorithm, one of the first and most well-known metaheuristic methods. We will go through the algorithm operation in detail, starting with choosing an initial solution and exploring neighboring options, with an emphasis on using a tabu list. The article covers the key aspects of the algorithm and its features.
African Buffalo Optimization (ABO)
The article presents the African Buffalo Optimization (ABO) algorithm, a metaheuristic approach developed in 2015 based on the unique behavior of these animals. The article describes in detail the stages of the algorithm implementation and its efficiency in finding solutions to complex problems, which makes it a valuable tool in the field of optimization.
Developing a Replay System (Part 46): Chart Trade Project (V)
Tired of wasting time searching for that very file that you application needs in order to work? How about including everything in the executable? This way you won't have to search for the things. I know that many people use this form of distribution and storage, but there is a much more suitable way. At least as far as the distribution of executable files and their storage is concerned. The method that will be presented here can be very useful, since you can use MetaTrader 5 itself as an excellent assistant, as well as MQL5. Furthermore, it is not that difficult to understand.
Population optimization algorithms: Whale Optimization Algorithm (WOA)
Whale Optimization Algorithm (WOA) is a metaheuristic algorithm inspired by the behavior and hunting strategies of humpback whales. The main idea of WOA is to mimic the so-called "bubble-net" feeding method, in which whales create bubbles around prey and then attack it in a spiral motion.
Battle Royale Optimizer (BRO)
The article explores the Battle Royale Optimizer algorithm — a metaheuristic in which solutions compete with their nearest neighbors, accumulate “damage,” are replaced when a threshold is exceeded, and periodically shrink the search space around the current best solution. It presents both pseudocode and an MQL5 implementation of the CAOBRO class, including neighbor search, movement toward the best solution, and an adaptive delta interval. Test results on the Hilly, Forest, and Megacity functions highlight the strengths and limitations of the approach. The reader is provided with a ready-to-use foundation for experimentation and tuning key parameters such as popSize and maxDamage.
Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)
After improving the C_Mouse class, we can focus on creating a class designed to create a completely new framework fr our analysis. We will not use inheritance or polymorphism to create this new class. Instead, we will change, or better said, add new objects to the price line. That's what we will do in this article. In the next one, we will look at how to change the analysis. All this will be done without changing the code of the C_Mouse class. Well, actually, it would be easier to achieve this using inheritance or polymorphism. However, there are other methods to achieve the same result.
Developing a Replay System (Part 69): Getting the Time Right (II)
Today we will look at why we need the iSpread feature. At the same time, we will understand how the system informs us about the remaining time of the bar when there is not a single tick available for it. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Developing a Replay System (Part 67): Refining the Control Indicator
In this article, we'll look at what can be achieved with a little code refinement. This refinement is aimed at simplifying our code, making more use of MQL5 library calls and, above all, making it much more stable, secure and easy to use in other projects that we may develop in the future.
Interactive Supply and Demand Zone Manager in MQL5 (Part II): Event-Driven Architecture and Persistent Lifecycle Logging
This article advances the stateful supply and demand zone framework for MetaTrader 5 by replacing polling with an event-driven model based on OnChartEvent(). We split synchronization into dedicated handlers for creation, modification, and deletion, and separate market logic in OnTick() from user interactions in OnChartEvent(). A persistent, append-only CSV logger records all lifecycle events, improving responsiveness, state consistency, and recoverable history for downstream analysis.
Chemical reaction optimization (CRO) algorithm (Part I): Process chemistry in optimization
In the first part of this article, we will dive into the world of chemical reactions and discover a new approach to optimization! Chemical reaction optimization (CRO) uses principles derived from the laws of thermodynamics to achieve efficient results. We will reveal the secrets of decomposition, synthesis and other chemical processes that became the basis of this innovative method.
MQL5 Bootstrap (III): Simplified Functions for Working with News
This article presents a unified news model and a set of reusable MQL5 classes for working with the MetaTrader 5 Economic Calendar. You will retrieve, filter, and cache events by time, currency, country, and importance using a single interface across three providers: built-in calendar, CSV, and SQLite. The framework supports export/import, next/previous event lookup, and reliable strategy‑tester backtesting without changing trading logic.
Most notable Artificial Cooperative Search algorithm modifications (ACSm)
Here we will consider the evolution of the ACS algorithm: three modifications aimed at improving the convergence characteristics and the algorithm efficiency. Transformation of one of the leading optimization algorithms. From matrix modifications to revolutionary approaches regarding population formation.
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.
Market Simulation (Part 19): First Steps with SQL (II)
As we explained in the first article about SQL, there is no point in spending time programming procedures to do what is already built into SQL. However, without knowing the basics, you won’t be able to do anything with SQL or take full advantage of everything this tool offers. Therefore, in this article, we will look at how to perform basic tasks in databases.
Market Simulation (Part 02): Cross Orders (II)
Unlike what was done in the previous article, here we will test the selection option using an Expert Advisor. Although this is not a final solution yet, it will be enough for now. With the help of this article, you will be able to understand how to implement one of the possible solutions.
Hybridization of population algorithms. Sequential and parallel structures
Here we will dive into the world of hybridization of optimization algorithms by looking at three key types: strategy mixing, sequential and parallel hybridization. We will conduct a series of experiments combining and testing relevant optimization algorithms.
Chaos optimization algorithm (COA): Continued
We continue studying the chaotic optimization algorithm. The second part of the article deals with the practical aspects of the algorithm implementation, its testing and conclusions.
Market Simulation (Part 07): Sockets (I)
Sockets. Do you know what they are for or how to use them in MetaTrader 5? If the answer is no, let's start by studying them. In today's article, we'll cover the basics. Since there are several ways to do the same thing, and we are always interested in the result, I want to show that there is indeed a simple way to transfer data from MetaTrader 5 to other programs, such as Excel. However, the main idea is not to transfer data from MetaTrader 5 to Excel, but the opposite, that is, to transfer data from Excel or any other program to MetaTrader 5.
Developing a Replay System (Part 33): Order System (II)
Today we will continue to develop the order system. As you will see, we will be massively reusing what has already been shown in other articles. Nevertheless, you will receive a small reward in this article. First, we will develop a system that can be used with a real trading server, both from a demo account or from a real one. We will make extensive use of the MetaTrader 5 platform, which will provide us with all the necessary support from the beginning.
Market Simulation (Part 17): Sockets (XI)
The implementation of the part of the code that will run in MetaTrader 5 does not present any difficulty. However, there are several points that need to be taken into account. This is necessary so that you can make the system work. Remember one important thing: not just one program will be running. In reality, we will have to run three programs simultaneously. It is important to implement and structure each of them in such a way that they can interact and communicate with one another, and that each of them understands what the others are trying or intending to do.
Developing a Replay System (Part 68): Getting the Time Right (I)
Today we will continue working on getting the mouse pointer to tell us how much time is left on a bar during periods of low liquidity. Although at first glance it seems simple, in reality this task is much more difficult. This involves some obstacles that we will have to overcome. Therefore, it is important that you have a good understanding of the material in this first part of this subseries in order to understand the following parts.
Trust Your Backtest Data First: Building a Reproducible Historical Data Audit in Python for MetaTrader 5
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.
Decoding Market Intent: Reading Structure, Liquidity, and Price Behavior
We implement a five-stage MQL5 pipeline that quantifies market structure, liquidity interaction, and price behavior on four timeframes, then resolves them into a 0–100 Market Intent Score. Decision states (WAIT/WATCH/ACTION) are driven by explicit weights plus hard gates. The analytical core feeds a concise dashboard and, when AutoTrade is on, an execution layer with entry zones, invalidation and liquidity‑based targets.
Neuroboids Optimization Algorithm 2 (NOA2)
The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.
Formulating Dynamic Multi-Pair EA (Part 10): Asymmetric Stop-Loss Logic Based on Pair-Specific Volatility Signatures
The EA learns each symbol's volatility profile before trading by processing 1000 bars and summarizing candle ranges, bodies and wicks, noise ratio, trend runs, pullback size, and true‑range dispersion. A classifier assigns regime and structure labels per pair. The stop‑loss optimizer maps those labels to a symbol‑specific ATR multiplier, and the risk module sizes lots to maintain constant percentage risk.
Bivariate Copulae in MQL5 (Part 1): Implementing Gaussian and Student's t-Copulae for Dependency Modeling
This is the first part of an article series presenting the implementation of bivariate copulae in MQL5. This article presents code implementing Gaussian and Student's t-copulae. It also delves into the fundamentals of statistical copulae and related topics. The code is based on the Arbitragelab Python package by Hudson and Thames.
Role of random number generator quality in the efficiency of optimization algorithms
In this article, we will look at the Mersenne Twister random number generator and compare it with the standard one in MQL5. We will also find out the influence of the random number generator quality on the results of optimization algorithms.
Bacterial Chemotaxis Optimization (BCO)
The article presents the original version of the Bacterial Chemotaxis Optimization (BCO) algorithm and its modified version. We will take a closer look at all the differences, with a special focus on the new version of BCOm, which simplifies the bacterial movement mechanism, reduces the dependence on positional history, and uses simpler math than the computationally heavy original version. We will also conduct the tests and summarize the results.
MQL5 Wizard Techniques you should know (14): Multi Objective Timeseries Forecasting with STF
Spatial Temporal Fusion which is using both ‘space’ and time metrics in modelling data is primarily useful in remote-sensing, and a host of other visual based activities in gaining a better understanding of our surroundings. Thanks to a published paper, we take a novel approach in using it by examining its potential to traders.
Population optimization algorithms: Charged System Search (CSS) algorithm
In this article, we will consider another optimization algorithm inspired by inanimate nature - Charged System Search (CSS) algorithm. The purpose of this article is to present a new optimization algorithm based on the principles of physics and mechanics.
Neuroboids Optimization Algorithm (NOA)
A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.
Interactive Supply and Demand Zone Manager in MQL5 (Part III): Zone Analysis, Stateful Interaction, and Pending Event Management
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.
Database Is Easy (Part 1): A Lightweight ORM Framework for MQL5 Using SQLite
This article presents a structured way to manage SQLite data in MQL5 through an ORM layer for MetaTrader 5. It introduces core classes for entity modeling and database access, a fluent CRUD API, reflection hooks for OnGet/OnSet, and macros to define models quickly. Practical code shows creating tables, binding fields, inserting, updating, querying, and deleting records. Developers gain reusable, type-safe components that minimize repetitive SQL.
The MQL5 Standard Library Explorer (Part 13): Implementing the Math Solvers Library in Trading
We present a complete workflow for adaptive filtering in MQL5 using the CNlEq Levenberg–Marquardt–like solver. The EA fits a VAMAC model—two EWMAs with an ATR‑based scaling—by supplying residuals and a Jacobian through CNlEq's reverse‑communication loop, with optional numerical or analytical derivatives. Code, setup instructions, and GBPUSD H1 tests show how to replace static thresholds with on‑bar re‑estimation.
Development and Forward Testing of an Autonomous LLM Agent for Trading with SEAL
A hybrid architecture based on Llama 3.2 and SEAL is being tested on eight currency pairs (M15), with forward-period data isolation and information leakage control. The methodology combines adversarial self-play, curriculum learning, and class balancing to ensure stable training. The experiments confirm the gap between forecast accuracy and actual returns, providing readers with practical guidelines for testing strategies and accurately assessing their generalizability.
Developing a Replay System (Part 65): Playing the service (VI)
In this article, we will look at how to implement and solve the mouse pointer issue when using it in conjunction with a replay/simulation application. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Population optimization algorithms: Binary Genetic Algorithm (BGA). Part I
In this article, we will explore various methods used in binary genetic and other population algorithms. We will look at the main components of the algorithm, such as selection, crossover and mutation, and their impact on the optimization. In addition, we will study data presentation methods and their impact on optimization results.
Graph Theory: Network Flow of Commodities (Ford-Fulkerson Algorithm), Used as a Liquidity-Capacity Engine
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.
Market Simulation (Part 03): A Matter of Performance
Often we have to take a step back and then move forward. In this article, we will show all the changes necessary to ensure that the Mouse and Chart Trade indicators do not break. As a bonus, we'll also cover other changes that have occurred in other header files that will be widely used in the future.