Articles on trading system automation in MQL5

In the first part, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. We created the COrder abstract object which is a base object for storing data on history orders and deals, as well as on market orders and positions. Now we will develop all the necessary objects for storing account history data in collections.

The article provides the description of the technology aimed at increasing the effectiveness of any automated trading system. It provides a brief explanation of the idea, as well as its underlying basics, possibilities and disadvantages.

This article continues the series of publications on a universal Expert Advisor model. This part describes in detail the original event model based on centralized data processing, and considers the structure of the CStrategy base class of the engine.

This article starts a new series about the creation of the DoEasy library for easy and fast program development. In the current article, we will implement the library functionality for accessing and working with symbol timeseries data. We are going to create the Bar object storing the main and extended timeseries bar data, and place bar objects to the timeseries list for convenient search and sorting of the objects.

In this article, I will create the collection class of Depths of Market of all symbols and start developing the functionality for working with the MQL5.com Signals service by creating the signal object class.

I am not a professional programmer. And thus, the principle of "going from the simple to the complex" is of primary importance to me when I am working on trading system development. What exactly is simple for me? First of all, it is the visualization of the process of creating the system, and the logic of its work. Also, it is a minimum of handwritten code. In this article, I will attempt to create and test the trading system, based on a Matlab package, and then write an Expert Advisor for MetaTrader 5. The historical data from MetaTrader 5 will be used for the testing process.

This article shows you how to build an Object-Oriented Expert Advisor from scratch, from conceiving a theoretical trading idea to programming a MQL5 EA that makes that idea real in the empirical world. Learning by doing is IMHO a solid approach to succeed, so I am showing a practical example in order for you to see how you can order your ideas to finally code your Forex robots. My goal is also to invite you to adhere the OO principles.

Would you like to see an hourly chart with bars opening from the second and the fifth minute of the hour? What does a redrawn chart look like when the opening time of bars is changing every minute? What advantages does trading on such charts have? You will find answers to these questions in this article.

In this article we will continue dealing with the OLAP technology applied to trading. We will expand the functionality presented in the first two articles. This time we will consider the operational analysis of quotes. We will put forward and test the hypotheses on trading strategies based on aggregated historical data. The article presents Expert Advisors for studying bar patterns and adaptive trading.

Analyse and examples of techniques how trading analysis can be performed on MetaTrader 5 platform, but executed by MetaTrader 4. Article will show you how to create simple signal provider in your MetaTrader 5, and connect to it with multiple clients, even running MetaTrader 4. Also you will find out how you can follow participants of Automated Trading Championship in your real MetaTrader 4 account.

Thomas DeMark Sequential is good at showing balance changes in the price movement. This is especially evident if we combine its signals with a level indicator, for example, Murray levels. The article is intended mostly for beginners and those who still cannot find their "Grail". I will also display some features of building levels that I have not seen on other forums. So, the article will probably be useful for advanced traders as well... Suggestions and reasonable criticism are welcome...

This article is about what you can not see in a backtest report, what you should expect using automated trading software, how to manage your money if you are using expert advisors, and how to cover a significant loss to remain in the trading activity when you are using automated procedures.

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. In the previous article, we discussed the first steps that anyone needs to understand before proceeding to creating an Expert Advisor that trades automatically. We considered the concepts and the structure.

We begin testing the patterns and trying the methods described in the articles about trading currency pair baskets. Let's see how oversold/overbought level breakthrough patterns are applied in practice.

In the last part of the series of articles about the CStrategy trading engine, we will consider simultaneous operation of multiple trading algorithms, will learn to load strategies from XML files, and will present a simple panel for selecting Expert Advisors from a single executable module, and managing their trading modes.

Perhaps one of the most advanced models among currently existing language neural networks is GPT-3, the maximal variant of which contains 175 billion parameters. Of course, we are not going to create such a monster on our home PCs. However, we can view which architectural solutions can be used in our work and how we can benefit from them.

This article describes the usage of the TesterWithDrawal() function for estimating risks in trade systems which imply the withdrawing of a certain part of assets during their operation. In addition, it describes the effect of this function on the algorithm of calculation of the drawdown of equity in the strategy tester. This function is useful when optimizing parameter of your Expert Advisors.

The market price is formed out of a stable balance between demand and supply which, in turn, depend on a variety of economic, political and psychological factors. Differences in nature as well as causes of influence of these factors make it difficult to directly consider all the components. This article sets forth an attempt to predict the market price on the basis of an elaborated regression model.

In this article, we will create a new base class of all library objects adding the event functionality to all its descendants and develop the class for tracking symbol collection events based on the new base class. We will also change account and account event classes for developing the new base object functionality.