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Experienced traders are well aware of the fact that most time-consuming things in trading are not opening and tracking positions but selecting symbols and looking for entry points.
Of course, these are not huge issues if you work only with 1-2 symbols. But if your trading toolkit consists of hundreds of stocks and dozens of Forex symbols, it may take several hours only to find suitable entry points.

In this article, we will develop an EA simplifying the search for stocks. The EA is to be helpful in three ways:

• it is to pre-filter stocks providing us with a list of the ones that meet our conditions;
• is should simplify navigation through the generated stock list;
• it is to display additional data necessary to make a decision.

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Contents

• Introduction
• Concept of separate optimization
• Selecting a trend-following trading strategy
• Testing the trading strategy
• Selecting a flat-based strategy
• Testing the flat-based strategy
• Combined strategy
• Summary
• Conclusion

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The subject of Kohonen neural networks was approached to in some articles on the mql5.com website, such as Using Self-Organizing Feature Maps (Kohonen Maps) in MetaTrader 5 and Self-Organizing Feature Maps (Kohonen Maps) - Revisiting the Subject. They introduced readers to the general principles of building neural networks of this type and visually analyzing the economic numbers of markets using such maps.


However, in practical terms, using Kohonen networks just for algorithmic trading has been confined with only one approach, namely the same visual analysis of topology maps built for the EA optimization results. In this case, one's value judgment, or rather one's vision and ability to draw reasonable conclusions from a picture turns out to be, perhaps, the crucial factor, sidelining the network properties regarding representing data in terms of nuts-and-bolts matters.


In other words, the features of neural network algorithms were not used to the full, i.e., they were used without automatically extracting knowledge or supporting decision making with specific recommendations. In this paper, we consider the problem of defining the optimal sets of robots' parameters in a more formalized manner. Moreover, we are going to apply Kohonen network to forecasting economic ranges. However, before proceeding to these applied problems, we should revise the existing source codes, get something fixed, and make some improvements.


It is highly recommended to read the above articles first, if you are not familiar with the terms such as 'network', 'layer', 'neuron' ('node'), 'link', 'weight', 'learning rate', 'learning range', and other notions related to Kohonen networks. Then we will have to saturate ourselves in this matter, so re-teaching the basic notions would lengthen this publication significantly.

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If a trader is trying to attract investors, it is very likely that they will want to check his or her trading results. So a trader should be able to present trading history to demonstrate the results. The MetaTrader 5 allows saving the trading history to a file (Toolbox — Trading tab — context menu — Report). A report can be saved as XLSX (to be analyzed in Microsoft Excel) and as HTML files which can be viewed in any browsers. The second option is obviously more popular, since some users may not have Excel, while everyone has a browser. Therefore, an HTML report with trading results would be more suitable for a potential investor.

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In the previous article, we considered the Random Decision Forest algorithm and wrote a simple self-learning EA based on Reinforcement learning.

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Correlation is a statistical relationship between two or more random variables (or quantities which can be considered random with some acceptable degree of accuracy). Changes in one ore more variables lead to systematic changes of other related variables. The mathematical measure of the correlation of two random variables is the correlation coefficient. If a change in one random variable does not lead to a regular change in the other random variable but leads to a change in another statistical characteristic of this random variable, such a relation is not considered correlation, although it is statistical.

Table of contents

• Introduction
• The concept of correlation
• Types of correlations
• Implementation of indicators
• Correlation-based trading system
• Testing the trading system
• Findings
• Conclusions

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Comprehensive data processing requires extensive tools and is often beyond the sandbox of one single application. Specialized programming languages are used for processing and analyzing data, statistics and machine learning. One of the leading programming languages for data processing is Python. A very effective solution is to use the power of the language and included libraries for the development of trading systems.

When creating a server, we need to associate a socket with one or more hosts (IP addresses) and an unused port. If the list of hosts is not set or is specified as "0.0.0.0", the socket will listen to all hosts. If you specify "127.0.0.1" or 'localhost', connection will be possible only within the "internal loop", i.e. only within one computer.

Since only the client is available in MQL5, we will create a server in Python.

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The MetaTrader development environment enables the integration of applications with external data, in particular with the data obtained from the Internet using the WebRequest function. HTML is the most universal and the most frequently used data format on the web. If a public service does not provide an open API for requests or its protocol is difficult to implement in MQL, the desired HTML pages can be parsed. In particular, traders often use various economic calendars. Although the task is not so relevant now, since the platform features the built-in Calendar, some traders may need specific news from specific sites. Also, we sometimes need to analyze deals from a trading HTML report received from third parties.

The MQL5 ecosystem provides various solutions to the problem, which however are usually specific and have their limitations. On the other hand, there is kind of "native" and universal method to search and parse data from HTML. This method is connected with the use of CSS selectors. In this article we will consider the MQL5 implementation of this method, as well as examples of their practical use.

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This article is a development of the article "Interaction between MetaTrader 4 and MATLAB" by A. Emelyanov, and it provides information on solving a similar task for modern 64-bit versions of all platforms utilized by users. Over the past period, the method for creating shared DLL libraries has been substantially upgraded in the MATLAB package. Therefore, the method discussed in the original article requires modification. This happened because MATLAB Compiler SDK or MATLAB Coder must now be used instead of MATLAB Compiler. In addition, the practice of working with dynamic memory in MATLAB has changed, which implies certain adjustments of the source code that addresses a library written in the MATLAB language.

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Most frequent users of this website know pretty well that MQL5 is the best option for developing custom EAs. Unfortunately, not all brokers allow creating accounts available in MetaTrader 5. Even if you currently work with a broker that allows that, you may switch to a broker offering only MetaTrader 4 in the future. What are you going to do with all the MQL5 EAs you have developed in that case? Are you going to spend a huge amount of time to rework them to fit into MQL4? Perhaps, it would be more reasonable to develop an EA able to work both in MetaTrader 5 and MetaTrader 4?

In this article, we will try to develop such an EA and check if a trading system based on an order grid is usable.

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