Articles on strategy testing in MQL5

It doesn't matter which SQL program we use: MySQL, SQL Server, SQLite, OpenSQL, or another. They all have something in common, and the common element is the SQL language. Even if we do not intend to use Workbench, we can manipulate or work with the database directly in MetaEditor or through MQL5 to perform actions in MetaTrader 5, but to do so, you will need knowledge of SQL. So here, we will learn at least the basics.

Did you know that the Golden Cross and Death Cross strategies, based on moving average crossovers, are some of the most reliable indicators for identifying long-term market trends? A Golden Cross signals a bullish trend when a shorter moving average crosses above a longer one, while a Death Cross indicates a bearish trend when the shorter average moves below. Despite their simplicity and effectiveness, manually applying these strategies often leads to missed opportunities or delayed trades.

This article develops a market state classification module for MQL5 that interprets price behavior using completed price data. By examining volatility contraction, expansion, and structural consistency, the tool classifies market conditions as compression, transition, expansion, or trend, providing a clear contextual framework for price action analysis.

In this article we delve into the application of boostrapping techniques as a means to estimate the future performance of an automated strategy.

This article presents an MQL5 Expert Advisor that upgrades raw swing detection to a rule-based Structural Validation Engine. Swings are confirmed by a break of structure, displacement, liquidity sweeps, or time-based respect, then linked to a liquidity map and a structural state machine. The result is context-aware entries and stops anchored to validated levels, helping filter noise and systematize execution.

The article presents a complete exploration of the Nelder-Mead method, explaining how the simplex (function parameter space) is modified and rearranged at each iteration to achieve an optimal solution, and describes how the method can be improved.

In this article, we will discuss one of the possible solutions to what we have been trying to demonstrate—namely, how to allow an Excel user to perform an action in MetaTrader 5 without sending orders or opening or closing positions. The idea is that the user employs Excel to conduct fundamental analysis of a particular symbol. And by using only Excel, they can instruct an expert advisor running in MetaTrader 5 to open or close a specific position.

The Gator Oscillator by Bill Williams and the Accumulation/Distribution Oscillator are another indicator pairing that could be used harmoniously within an MQL5 Expert Advisor. We use the Gator Oscillator for its ability to affirm trends, while the A/D is used to provide confirmation of the trends via checks on volume. In exploring this indicator pairing, as always, we use the MQL5 wizard to build and test out their potential.

Here we will see how to implement something really interesting, but at the same time very difficult due to certain points that can be very confusing. The worst thing that can happen is that some traders who consider themselves professionals do not know anything about the importance of these concepts in the capital market. Well, although we focus here on programming, understanding some of the issues involved in market trading is paramount to what we are going to implement.

This article continues our series into category theory by introducing natural transformations, a key pillar within the subject. We look at the seemingly complex definition, then delve into examples and applications with this series’ ‘bread and butter’; volatility forecasting.

This article, that follows Category Theory implementation of Orders in MQL5, considers how database schemas can be incorporated for classification in MQL5. We take an introductory look at how database schema concepts could be married with category theory when identifying trade relevant text(string) information. Calendar events are the focus.

In this article, we will modify the last code shown in this series about Chart Trade. These changes are necessary to adapt the code to the current replay/simulation system model. 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.

Of all the things that we have developed so far, this system, as you will probably notice and eventually agree, is the most complex. Now we need to do something very simple: make our system simulate the operation of a trading server. This need to accurately implement the way the trading server operates seems like a no-brainer. At least in words. But we need to do this so that the everything is seamless and transparent for the user of the replay/simulation system.

This article implements a box‑constrained Truncated Newton Conjugate‑Gradient (TNC) optimizer in MQL5 and details its core components: scaling, projection to bounds, line search, and Hessian‑vector products via finite differences. It provides an objective wrapper supporting analytic or numerical derivatives and validates the solver on the Rosenbrock benchmark. A logistic regression example shows how to use TNC as a drop‑in alternative to LBFGS.

After optimizing the trading strategy, we receive sets of parameters. We can use them to create several instances of trading strategies combined in one EA. Previously, we did this manually. Here we will try to automate this process.

In order to use the data that forms the bars, we must abandon replay and start developing a simulator. We will use 1 minute bars because they offer the least amount of difficulty.