MQL5 Programming Articles

In the upcoming series of articles, I will demonstrate the development of self-adapting algorithms considering most market factors, as well as show how to systematize these situations, describe them in logic and take them into account in your trading activity. I will start with a very simple algorithm that will gradually acquire theory and evolve into a very complex project.

From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.

The article describes the basic principles and methods that allow you to analyze any strategy using spreadsheets (Excel, Calc, Google). The obtained results are compared with MetaTrader 5 tester.

In conclusion of the topic of working with timeseries organise storage, search and sort of data stored in indicator buffers which will allow to further perform the analysis based on values of the indicators to be created on the library basis in programs. The general concept of all collection classes of the library allows to easily find necessary data in the corresponding collection. Respectively, the same will be possible in the class created today.

This is the next article within the series, in which I show how I created a convenient library for manual application of chart graphics by utilizing keyboard shortcuts. The tools used include straight lines and their combinations. In this part, we will view how the drawing tools are applied using the functions described in the first part. The library can be connected to any Expert Advisor or indicator which will greatly simplify the charting tasks. This solution DOES NOT use external dlls, while all the commands are implemented using built-in MQL tools.

All traders visit the market with the goal of earning their first million dollars. How to do that without excessive risk and start-up budget? MQL5 services provide such opportunity for developers and traders from around the world.

In previous articles, we used stochastic gradient descent to train a neural network using the same learning rate for all neurons within the network. In this article, I propose to look towards adaptive learning methods which enable changing of the learning rate for each neuron. We will also consider the pros and cons of this approach.

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...

In this article, I will show the criteria to be used when selecting a system or a signal for investing your funds, as well as describe the optimal approach to the development of trading systems and highlight the importance of this matter in Forex trading.

In the article, develop an object which will contain all data of one buffer for one indicator. Such objects will be necessary for storing serial data of indicator buffers. With their help, it will be possible to sort and compare buffer data of any indicators, as well as other similar data with each other.

The article considers creation of the custom indicator object for the use in EAs. Let’s slightly improve library classes and add methods to get data from indicator objects in EAs.

We have earlier discussed some types of neural network implementations. In the considered networks, the same operations are repeated for each neuron. A logical further step is to utilize multithreaded computing capabilities provided by modern technology in an effort to speed up the neural network learning process. One of the possible implementations is described in this article.

The article continues developing indicator object classes and their collections. For each indicator object create its description and correct collection class for error-free storage and getting indicator objects from the collection list.

We continue studying the world of neural networks. In this article, we will consider another type of neural networks, recurrent networks. This type is proposed for use with time series, which are represented in the MetaTrader 5 trading platform by price charts.

The article considers creation of classes of descendant objects of base abstract indicator. Such objects will provide access to features of creating indicator EAs, collecting and getting data value statistics of various indicators and prices. Also, create indicator object collection from which getting access to properties and data of each indicator created in the program will be possible.

In this article, I will try to explain in detail what grid and martingale are, as well as what they have in common. Besides, I will try to analyze how viable these strategies really are. The article features mathematical and practical sections.

The article considers creation of an abstract indicator which further will be used as the base class to create objects of library’s standard and custom indicators.

In the article, consider creation of multi-symbol multi-period standard indicator Accumulation/Distribution. Slightly improve library classes with respect to indicators so that, the programs developed for outdated platform MetaTrader 4 based on this library could work normally when switching over to MetaTrader 5.

The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.

As a continuation of the neural network topic, I propose considering convolutional neural networks. This type of neural network are usually applied to analyzing visual imagery. In this article, we will consider the application of these networks in the financial markets.

The article aims to describe the main features of Forex trading as simply and quickly as possible, as well as share some basic ideas with beginners. It also attempts to answer the most tantalizing questions in the trading community along with showcasing the development of a simple indicator.

This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.

In the article, complete development of objects of multi-period multi-symbol standard indicators. Using Ichimoku Kinko Hyo standard indicator example, analyze creation of compound custom indicators which have auxiliary drawn buffers for displaying data on the chart.

The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.

In the article, let’s improve library methods for correct display of multi-symbol multi-period standard indicators, which lines are displayed on the current symbol chart with a shift set in the settings. As well, let’s put things in order in methods of work with standard indicators and remove the redundant code to the library area in the final indicator program.

In this second article, we will continue to study neural networks and will consider an example of using our created CNet class in Expert Advisors. We will work with two neural network models, which show similar results both in terms of training time and prediction accuracy.

Traders often talk about trends and flats but very few of them really understand what a trend/flat really is and even fewer are able to clearly explain these concepts. Discussing these basic terms is often beset by a solid set of prejudices and misconceptions. However, if we want to make profit, we need to understand the mathematical and logical meaning of these concepts. In this article, I will take a closer look at the essence of trend and flat, as well as try to define whether the market structure is based on trend, flat or something else. I will also consider the most optimal strategies for making profit on trend and flat markets.

We usually analyze the market using candlesticks or bars that slice the price series into regular intervals. Doesn't such discretization method distort the real structure of market movements? Discretization of an audio signal at regular intervals is an acceptable solution because an audio signal is a function that changes over time. The signal itself is an amplitude which depends on time. This signal property is fundamental.

In the current article, I will improve the library classes to implement the ability to develop multi-symbol multi-period standard indicators requiring several indicator buffers to display their data.