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The MetaTrader 5 platform features functionality for saving trading reports, as well as Expert Advisor testing and optimization reports. Trading and testing reports can be saved in two formats: XLSX and HTML, while the optimization report can be saved in XML. In this article we consider the HTML testing report, the XML optimization report and the HTML trading history report.

Many researchers do not pay enough attention to determining the price behavior. At the same time, complex methods are used, which very often are simply “black boxes”, such as machine learning or neural networks. The most important question arising in that case is what data to submit for training a particular model.

In this article, we will analyze the concept of correlation between variables, as well as methods for the calculation of correlation coefficients and their practical use in trading. 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.

Since its introduction, MetaTrader 5 provides multicurrency testing options. This possibility is often used by traders. However the function is not universal. The article presents several programs for drawing graphical objects on charts based on HTML and CSV trading history reports. Multicurrency trading can be analyzed in parallel, in several sub-windows, as well as in one window using the dynamic switching command.

Most of traders agree that the current market state analysis starts with the evaluation of higher chart timeframes. The analysis is performed downwards to lower timeframes until the one, at which deals are performed. This analysis method seems to be a mandatory part of professional approach for successful trading. In this article, we will discuss multi-timeframe indicators and their creation ways, as well as we will provide MQL5 code examples. In addition to the general evaluation of advantages and disadvantages, we will propose a new indicator approach using the MTF mode.

This article presents Pivot Mean Oscillator (PMO), an implementation of the cumulative moving average (CMA) as a trading indicator for the MetaTrader platforms. In particular, we first introduce Pivot Mean (PM) as a normalization index for timeseries that computes the fraction between any data point and the CMA. We then build PMO as the difference between the moving averages applied to two PM signals. Some preliminary experiments carried out on the EURUSD symbol to test the efficacy of the proposed indicator are also reported, leaving ample space for further considerations and improvements.

The article describes how to create a framework for the online analysis of multidimensional data (OLAP), as well as how to implement this in MQL and to apply such analysis in the MetaTrader environment using the example of trading account history processing.

A logical continuation of the earlier discussed topic would be the development of multifunctional mathematical models for trading tasks. In this article, I will describe the entire process related to the development of the first mathematical model describing fractals, from scratch. This model should become an important building block and be multifunctional and universal. It will build up our theoretical basis for further development of this idea.

In this article, we will continue to study fractals and will pay special attention to summarizing all the material. To do this, I will try to bring all earlier developments into a compact form which would be convenient and understandable for practical application in trading.

An indicator to report your brokers Bid/Ask spread levels. Now we can use MT5s tick data to analyze what the historic true average Bid/Ask spread actually have recently been. You shouldn't need to look at the current spread because that is available if you show both bid and ask price lines.

In this article, I decided to highlight the well-known Bernoulli scheme and to show how it can be used to describe trading-related data arrays. All this will then be used to create a self-adapting trading system. We will also look for a more generic algorithm, a special case of which is the Bernoulli formula, and will find an application for it.

In this article, we will consider how to build graphs of all optimization passes and to select the optimal custom criterion. We will also see how to create a desired solution with little MQL5 knowledge, using the articles published on the website and forum comments.

This article presents an analysis of currency data to better understand why expert advisors can have good performance in some regions of time and poor performance in other regions of time.

In this article, I decided to conduct a study related to the possibility of reducing multiple states to double-state systems. The main purpose of the article is to analyze and to come to useful conclusions that may help in the further development of scalable trading algorithms based on the probability theory. Of course, this topic involves mathematics. However, given the experience of previous articles, I see that generalized information is more useful than details.

Implementation of the Logger class for unifying and structuring messages which are printed to the Experts log. Connection to the Seq log collection and analysis system. Monitoring log messages online.

Knowing how to input data from the Web into an Expert Advisor is not so obvious. It is not so easy to do without understanding all the possibilities offered by MetaTrader 5.

The article provides the foundations of a mathematically rigorous price movement and market functioning theory. Up to the present, we have not had any mathematically rigorous price movement theory. Instead, we have had to deal with experience-based assumptions stating that the price moves in a certain way after a certain pattern. Of course, these assumptions have been supported neither by statistics, nor by theory.

The article considers the probabilistic price field evolution equation and the upcoming price spike criterion. It also reveals the essence of price values on charts and the mechanism for the occurrence of a random walk of these values.

We have previously considered various types of neural networks along with their implementations. In all cases, the neural networks were trained using the gradient decent method, for which we need to choose a learning rate. In this article, I want to show the importance of a correctly selected rate and its impact on the neural network training, using examples.

In previous articles, we have already tested various options for organizing neural networks. We also considered convolutional networks borrowed from image processing algorithms. In this article, I suggest considering Attention Mechanisms, the appearance of which gave impetus to the development of language models.

The popularity of these two methods grows, so a lot of libraries have been developed in Matlab, R, Python, C++ and others, which receive a training set as input and automatically create an appropriate network for the problem. Let us try to understand how the basic neural network type works (including single-neuron perceptron and multilayer perceptron). We will consider an exciting algorithm which is responsible for network training - gradient descent and backpropagation. Existing complex models are often based on such simple network models.

By using special data types 'matrix' and 'vector', it is possible to create code which is very close to mathematical notation. With these methods, you can avoid the need to create nested loops or to mind correct indexing of arrays in calculations. Therefore, the use of matrix and vector methods increases the reliability and speed in developing complex programs.

It has been more than a year since I published my last article. This is quite a lot time to revise ideas and to develop new approaches. In the new article, I would like to divert from the previously used supervised learning method. This time we will dip into unsupervised learning algorithms. In particular, we will consider one of the clustering algorithms—k-means.

In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders.

As a continuation of this series of articles, let's consider another type of problems within unsupervised learning methods: mining association rules. This problem type was first used in retail, namely supermarkets, to analyze market baskets. In this article, we will talk about the applicability of such algorithms in trading.

There are minor things to cover on the feed-forward neural network before we are through, the design being one of them. Let's see how we can build and design a flexible neural network to our inputs, the number of hidden layers, and the nodes for each of the network.

Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders.

We continue considering association rules. In the previous article, we have discussed theoretical aspect of this type of problem. In this article, I will show the implementation of the FP Growth method using MQL5. We will also test the implemented solution using real data.

We can find dial gauges in cars and airplanes, in industrial production and everyday life. They are used in all spheres which require quick response to behavior of a controlled value. This article describes the library of dial gauges for MetaTrader 5.

This article presents an alternative method of GUI creation based on layouts and containers, using one layout manager — the CBox class. The CBox class is an auxiliary control that acts as a container for essential controls in a GUI panel. It can make designing graphical panels easier, and in some cases, reduce coding time.