Articles on strategy testing in MQL5

In this article, we will continue diving into the implementation of the ACMO (Atmospheric Cloud Model Optimization) algorithm. In particular, we will discuss two key aspects: the movement of clouds into low-pressure regions and the rain simulation, including the initialization of droplets and their distribution among clouds. We will also look at other methods that play an important role in managing the state of clouds and ensuring their interaction with the environment.

In the previous article, we completed the necessary introduction to SQL. And, in my opinion, we properly clarified what we wanted to show and explain about SQL. This was done so that anyone who comes to look at the market replay/simulation system being built can at least get an idea of what may be happening there. The point is that there is no sense in programming things that SQL handles perfectly.

This article presents a unique experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Working in this direction will provide further insight into which specific algorithms can successfully continue their search using coordinates set by the user as a starting point, and what factors influence their success.

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.

Many people tend to underestimate SQL, or even not use it at all, because they do not fully understand how it actually works. When running queries against an SQL database, we are not always looking for a universal answer; in some cases, we need a very specific and practical answer. If a database is created with a proper structure and data model, almost any type of information can be integrated into it.

In the previous article, I showed how to proceed in order to add a query mechanism. This was needed so that, inside MQL5 code, you could fully use SQL and retrieve results using an SQL SELECT query. But there is still one last function we need to implement. This is the DatabaseReadBind function. Since understanding it properly requires a slightly more detailed explanation, it was decided to cover it not in the previous article, but in today's article. So, since the topic will be fairly extensive, let us proceed directly to the next section.

In today's article we will begin studying the use of SQL in MQL5 code. We will also look at how to create a database. Or, more precisely, how to create a SQLite database file using the features built into MQL5. We will also see how to create a table, and then how to establish a relationship between tables by using primary and foreign keys. All of this, once again, will be done with MQL5. We will see how easy it is to create code that can later be migrated to other SQL implementations by using a class that helps hide the implementation being created. And, most importantly, we will see that at various points we may face the risk that something will go wrong when using SQL. This happens because, in MQL5 code, SQL code will always be placed inside a string.

In the previous article, we configured the indicator to display the financial result. However, not everyone likes using this display mode. The reasons differ from one trader to another, although in some cases they seem quite reasonable and justified to me. Adapting the code to provide this capability is by no means one of the most difficult tasks. It's actually pretty simple. In this article, we'll look at how to do this.

Here we will start bringing together different components or applications that were previously completely isolated from each other. Chart Trade, the mouse indicator, and the Expert Advisor had already been linked to one another, but there was still no way to directly display on the chart the positions open on the trading server, which are often managed using a cross-order system. From this point on, this becomes possible, opening the way for various ideas and future implementations. Although we are only beginning to put these components into operation, we already have a direction for further development.

Debugging is an integral part of the programming cycle. This article discusses common techniques for debugging any application running in the MetaTrader 5 environment.

The article examines two practical aspects of the Adwizard-based optimization pipeline: diagnostics and recovery after failures when generating the final Expert Advisor database, as well as preliminary selection of strategy parameter ranges before project creation. It is shown how analyzing the stages/jobs/tasks tables in SQLite and restarting stages based on their statuses help restore the process, while trial optimization narrows the search space, eliminates redundant parameters, and reduces the risk of getting stuck at local maxima.