Articles on data analysis and statistics 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.

While analyzing a huge number of trading strategies, orders for development of applications for MetaTrader 5 and MetaTrader 4 terminals and various MetaTrader websites, I came to the conclusion that all this diversity is based mostly on the same elementary functions, actions and values appearing regularly in different programs. This resulted in DoEasy cross-platform library for easy and quick development of МetaТrader 5 and МetaТrader 4 applications.

In this article we will perform an experiment: we will color optimization results. The color is determined by three parameters: the levels of red, green and blue (RGB). There are other color coding methods, which also use three parameters. Thus, three testing parameters can be converted to one color, which visually represents the values. Read this article to find out if such a representation can be useful.

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. The article provides a description of how to connect MetaTrader 5 and Python using sockets, as well as how to receive quotes via the terminal API.

The article describes a preprocessor, a scanner, and a parser to be used in parsing the MQL-based source codes. MQL implementation is attached.

In the first part of the article, I have described a modified ZigZag indicator and a class for receiving data of that type of indicators. Here, I will show how to develop indicators based on these tools and write an EA for tests that features making deals according to signals formed by ZigZag indicator. As an addition, the article will introduce a new version of the EasyAndFast library for developing graphical user interfaces.

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.

Based on universal tools designed for working with Kohonen networks, we construct the system of analyzing and selecting the optimal EA parameters and consider forecasting time series. In Part I, we corrected and improved the publicly available neural network classes, having added necessary algorithms. Now, it is time to apply them to practice.

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.

In this article, we continue expanding the features of the utility for collecting and navigating through symbols. This time, we will create new tabs displaying only the symbols that satisfy some of the necessary parameters and find out how to easily add custom tabs with the necessary sorting rules.

The present article develops the idea of using Kohonen Maps in MetaTrader 5, covered in some previous publications. The improved and enhanced classes provide tools to solve application tasks.

The article considers applying the separate optimization method during various market conditions. Separate optimization means defining trading system's optimal parameters by optimizing for an uptrend and downtrend separately. To reduce the effect of false signals and improve profitability, the systems are made flexible, meaning they have some specific set of settings or input data, which is justified because the market behavior is constantly changing.

In this article, we are going to expand the capabilities of the previously created utility by adding tabs for selecting the symbols we need. We will also learn how to save graphical objects we have created on the specific symbol chart, so that we do not have to constantly create them again. Besides, we will find out how to work only with symbols that have been preliminarily selected using a specific website.

The article describes the creation of a custom exchange symbol using the MQL5 language. In particular, it considers the use of exchange quotes from the popular Finam website. Another option considered in this article is the possibility to work with an arbitrary format of text files used in the creation of the custom symbol. This allows working with any financial symbols and data sources. After creating a custom symbol, we can use all the capabilities of the MetaTrader 5 Strategy Tester to test trading algorithms for exchange instruments.

In this article, we will apply the probability theory and mathematical statistics methods to creating and testing trading strategies. We will also look for optimal trading risk using the differences between the price and the random walk. It is proved that if prices behave like a zero-drift random walk (with no directional trend), then profitable trading is impossible.

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. In this article, we will develop an EA simplifying the search for entry points on trading instruments provided by your broker.

This article provides programmatic definition of one of the movement continuation models. The main idea is defining two waves — the main and the correction one. For extreme points, I apply fractals as well as "potential" fractals - extreme points that have not yet formed as fractals.

The article dwells on the development of an application for selecting the best optimization passes using several possible options. The application is able to sort out the optimization results by a variety of factors. Optimization passes are always written to a database, therefore you can always select new robot parameters without re-optimization. Besides, you are able to see all optimization passes on a single chart, calculate parametric VaR ratios and build the graph of the normal distribution of passes and trading results of a certain ratio set. Besides, the graphs of some calculated ratios are built dynamically beginning with the optimization start (or from a selected date to another selected date).

The article provides an overview of the terminal's capabilities for creating and working with custom symbols, offers options for simulating a trading history using custom symbols, trend and various chart patterns.

Comparing several time series during a technical analysis is a quite common task that requires appropriate tools. In this article, I suggest developing a tool for graphical analysis and detecting correlations between two or more time series.

The widget provides websites with a detailed release schedule of 500 indicators and indices, of the world's largest economies. Thus, traders quickly receive up-to-date information on all important events with explanations and graphs in addition to the main website content.

The article describes custom methods for assessing the trading history. Two classes have been written for downloading and analyzing history. The first of them collects the trading history and represents it as a summary table. The second one deals with statistics: it calculates a number of variables and builds charts for a more efficient evaluation of trading results.

Trading account monitoring provides a detailed report on all completed deals. All trading statistics are collected automatically and provided to you as easy-to-understand diagrams and graphs.

In the article, we continue to develop the MQL application for working with optimization results. This time, we will show how to form the table of the best results after optimizing the parameters by specifying another criterion via the graphical interface.

Before launching a robot on a trading account, we usually test and optimize it on quotes history. However, a reasonable question arises: how can past results help us in the future? The article describes applying the Monte Carlo method to construct custom criteria for trading strategy optimization. In addition, the EA stability criteria are considered.

The trade Signals service develops in leaps and bounds. Trusting our funds to a signal provider, we would like to minimize the risk of losing our deposit. So how to puzzle out in this forest of trade signals? How to find the one that would produce profits? This paper proposes to create a tool for visually analyzing the history of trades on trade signals in a symbol chart.

Most subscribers choose a trade signal by the beauty of the balance curve and by the number of subscribers. This is why many today's providers care of beautiful statistics rather than of real signal quality, often playing with lot sizes and artificially reducing the balance curve to an ideal appearance. This paper deals with the reliability criteria and the methods a provider may use to enhance its signal quality. An exemplary analysis of a specific signal history is presented, as well as methods that would help a provider to make it more profitable and less risky.

This is a continuation of the idea of processing and analysis of optimization results. This time, our purpose is to select the 100 best optimization results and display them in a GUI table. The user will be able to select a row in the optimization results table and receive a multi-symbol balance and drawdown graph on separate charts.

The article implements an MQL application with a graphical interface for extended visualization of the optimization process. The graphical interface applies the last version of EasyAndFast library. Many users may ask why they need graphical interfaces in MQL applications. This article demonstrates one of multiple cases where they can be useful for traders.

The article is based on 'The Mathematics of Money Management' by Ralph Vince. It provides the description of empirical and parametric methods used for finding the optimal size of a trading lot. Also the article features implementation of trading modules for the MQL5 Wizard based on these methods.

The Strategy Tester in the MetaTrader 5 trading platform provides only two optimization options: complete search of parameters and genetic algorithm. This article proposes a new method for optimizing trading strategies — Simulated annealing. The method's algorithm, its implementation and integration into any Expert Advisor are considered. The developed algorithm is tested on the Moving Average EA.

Trading in financial markets is associated with a whole range of risks that should be taken into account in the algorithms of trading systems. Reducing such risks is the most important task to make a profit when trading.

The article develops the ideas proposed in the previous part and considers them further. It describes the problems of yield distributions, plotting and studying statistical regularities.

The article deals with the concept of night trading, as well as trading strategies and their implementation in MQL5. We perform tests and make appropriate conclusions.

The article suggests a technology helping everyone to create custom trading strategies by assembling an individual indicator set, as well as to develop custom market entry signals.

Different situations happen in trader’s life. Often, the history of successful trades allows us to restore a strategy, while looking at a loss history we try to develop and improve it. In both cases, we compare trades with known indicators. This article suggests methods of batch comparison of trades with a number of indicators.

This article deals with seven types of moving averages (MA) and a trading strategy to work with them. We also test and compare various MAs at a single trading strategy and evaluate the efficiency of each moving average compared to others.

Mini Market Emulator is an indicator designed for partial emulation of work in the terminal. Presumably, it can be used to test "manual" strategies of market analysis and trading.

The article considers the classic method for divergence construction and provides an additional divergence interpretation method. A trading strategy was developed based on this new interpretation method. This strategy is also described in the article.