In this article, I will start developing the methods of working with standard indicators, which will ultimately allow creating multi-symbol multi-period standard indicators based on library classes. Besides, I will add the "Skipped bars" event to the timeseries classes and eliminate excessive load from the main program code by moving the library preparation functions to CEngine class.
Overbought/oversold zones characterize a certain state of the market, differentiating through weaker changes in the prices of securities. This adverse change in the synamics is pronounced most at the final stage in the development of trends of any scales. Since the profit value in trading depends directly on the capability of covering as large trend amplitude as possible, the accuracy of detecting such zones is a key task in trading with any securities whatsoever.
Trading is always about making decisions in the face of uncertainty. This means that the results of the decisions are not quite obvious at the time these decisions are made. This entails the importance of theoretical approaches to the construction of mathematical models allowing us to describe such cases in meaningful manner.
In this article, I am going to improve the classes of indicator buffer objects to work in the multi-symbol mode. This will pave the way for creating multi-symbol multi-period indicators in custom programs. I will add the missing functionality to the calculated buffer objects allowing us to create multi-symbol multi-period standard indicators.
In this article, I will start the improvement of the indicator buffer objects and collection class for working in multi-period and multi-symbol modes. I am going to consider the operation of buffer objects for receiving and displaying data from any timeframe on the current symbol chart.
The article provides a description and instructions for the practical use of neural network modules on the Matlab platform. It also covers the main aspects of creation of a trading system using the neural network module. In order to be able to introduce the complex within one article, I had to modify it so as to combine several neural network module functions in one program.
The article deals with creating a collection class of indicator buffer objects. I am going to test the ability to create and work with any number of buffers for indicators (the maximum number of buffers that can be created in MQL indicators is 512).
The article considers the development of indicator buffer object classes as descendants of the abstract buffer object simplifying declaration and working with indicator buffers, while creating custom indicator programs based on DoEasy library.
In this article, we start the development of the indicator buffer classes for the DoEasy library. We will create the base class of the abstract buffer which is to be used as a foundation for the development of different class types of indicator buffers.
In the article, we will consider a sample multi-symbol multi-period indicator using the timeseries classes of the DoEasy library displaying the chart of a selected currency pair on a selected timeframe as candles in a subwindow. I am going to modify the library classes a bit and create a separate file for storing enumerations for program inputs and selecting a compilation language.
The article considers the development of a simple multi-period indicator based on the DoEasy library. Let's improve the timeseries classes to receive data from any timeframes to display it on the current chart period.
The article deals with applying DoEasy library for creating multi-symbol multi-period indicators. We are going to prepare the library classes to work within indicators and test creating timeseries to be used as data sources in indicators. We will also implement creating and sending timeseries events.
The article considers real-time update of timeseries data and sending messages about the "New bar" event to the control program chart from all timeseries of all symbols for the ability to handle these events in custom programs. The "New tick" class is used to determine the need to update timeseries for the non-current chart symbol and periods.
The article offers basic tools for the OLAP analysis of tester reports relating to single passes and optimization results. The tool can work with standard format files (tst and opt), and it also provides a graphical interface. MQL source codes are attached below.
The article deals with the development of the timeseries collection of specified timeframes for all symbols used in the program. We are going to develop the timeseries collection, the methods of setting collection's timeseries parameters and the initial filling of developed timeseries with historical data.
This article deals with the theory and practical application of the algorithm for forecasting time series, based on support-vector method. It also proposes its implementation in MQL and provides test indicators and Expert Advisors. This technology has not been implemented in MQL yet. But first, we have to get to know math for it.
In this article, we will consider combining the lists of bar objects for each used symbol period into a single symbol timeseries object. Thus, each symbol will have an object storing the lists of all used symbol timeseries periods.
This article deals with the theory and practical use of the algorithm for forecasting time series, based on the empirical decomposition mode. It proposes the MQL implementation of this method and presents test indicators and Expert Advisors.
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, we will complete the description of the pending request trading concept and create the functionality for removing pending orders, as well as modifying orders and positions under certain conditions. Thus, we are going to have the entire functionality enabling us to develop simple custom strategies, or rather EA behavior logic activated upon user-defined conditions.
3D graphics provide excellent means for analyzing huge amounts of data as they enable the visualization of hidden patterns. These tasks can be solved directly in MQL5, while DireсtX functions allow creating three-dimensional object. Thus, it is even possible to create programs of any complexity, even 3D games for MetaTrader 5. Start learning 3D graphics by drawing simple three-dimensional shapes.
We continue the development of the library functionality featuring trading using pending requests. We have already implemented sending conditional trading requests for opening positions and placing pending orders. In the current article, we will implement conditional position closure – full, partial and closing by an opposite position.
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.
We continue the development of the functionality allowing users to trade using pending requests. In this article, we are going to implement the ability to place pending orders under certain conditions.
Starting with this article, we are going to develop a functionality allowing users to trade using pending requests under certain conditions, for example, when reaching a certain time limit, exceeding a specified profit or closing a position by stop loss.
In this article, we will discuss the idea of creating a multicurrency monitor of trading signals and will develop a future application structure along with its prototype, as well as create its framework for further operation. The article presents a step-by-step creation of a flexible multicurrency application which will enable the generation of trading signals and which will assist traders in finding the desired signals.
The third part serves as a bridge between the previous two parts: it describes the mechanism of interaction with the DLL considered in the first article and the objects for report downloading, which were described in the second article. We will analyze the process of wrapper creation for a class which is imported from DLL and which forms an XML file with the trading history. We will also consider a method for interacting with this wrapper.
In the previous article, we have created the classes of pending request objects corresponding to the general concept of library objects. This time, we are going to deal with the class allowing the management of pending request objects.
In the previous articles, we checked the concept of pending trading requests. A pending request is, in fact, a common trading order executed by a certain condition. In this article, we are going to create full-fledged classes of pending request objects — a base request object and its descendants.
Artificial intelligence is often associated with something fantastically complex and incomprehensible. At the same time, artificial intelligence is increasingly mentioned in everyday life. News about achievements related to the use of neural networks often appear in different media. The purpose of this article is to show that anyone can easily create a neural network and use the AI achievements in trading.
This is the third article about the concept of pending requests. We are going to complete the tests of pending trading requests by creating the methods for closing positions, removing pending orders and modifying position and pending order parameters.
The development of trading strategies is associated with handling large amounts of data. Now, you are able to work with databases using SQL queries based on SQLite directly in MQL5. An important feature of this engine is that the entire database is placed in a single file located on a user's PC.
The first article within the Walk-Through Optimization series described the creation of a DLL to be used in our auto optimizer. This continuation is entirely devoted to the MQL5 language.
In this article, we will continue the development of trading requests, implement placing pending orders and eliminate detected shortcomings of the trading class operation.
In this article, we are going to store some data in the value of the orders and positions magic number and start the implementation of pending requests. To check the concept, let's create the first test pending request for opening market positions when receiving a server error requiring waiting and sending a repeated request.
In this article we will view seasonal characteristics of financial time series using Boxplot diagrams. Each separate boxplot (or box-and-whiskey diagram) provides a good visualization of how values are distributed along the dataset. Boxplots should not be confused with the candlestick charts, although they can be visually similar.
After we send a trading order to the server, we need to check the error codes or the absence of errors. In this article, we will consider handling errors returned by the trade server and prepare for creating pending trading requests.
In this article, we will have a look at the handler of invalid trading order parameters and improve the trading event class. Now all trading events (both single ones and the ones occurred simultaneously within one tick) will be defined in programs correctly.
The first article is devoted to the creation of a toolkit for working with optimization reports, for importing them from the terminal, as well as for filtering and sorting the obtained data. MetaTrader 5 allows downloading optimization results, however our purpose is to add our own data to the optimization report.
In the article, we continue the development of the trading class by implementing the control over incorrect trading order parameter values and voicing trading events.
