We all know the saying "Better to see once than hear a hundred times". You can read various books about Paris or Venice, but based on the mental images you wouldn't have the same feelings as on the evening walk in these fabulous cities. The advantage of visualization can easily be projected on any aspect of our lives, including work in the market, for example, the analysis of price on charts using indicators, and of course, the visualization of strategy testing. This article contains descriptions of all the visualization features of the MetaTrader 5 Strategy Tester.
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This article centers around strategies that actively use pending orders, a metalanguage that can be created to formally describe such strategies and the use of a multi-purpose Expert Advisor whose operation is based on those descriptions
In this article, we will continue analyzing the algorithms of the CStrategy trading engine. The third part of the series contains the detailed analysis of examples of how to develop specific trading strategies using this approach. Special attention is paid to auxiliary algorithms — Expert Advisor logging system and data access using a conventional indexer (Close[1], Open[0] etc.)
Let's now shape the PHP-based Twitter idea which was introduced in the first part of this article. We are assembling the different parts of the SDSS. Regarding the client side of the system architecture, we are relying on the new MQL5 WebRequest() function for sending trading signals via HTTP.
In this article, we will create a new base class of all library objects adding the event functionality to all its descendants and develop the class for tracking symbol collection events based on the new base class. We will also change account and account event classes for developing the new base object functionality.
We continue testing the patterns and trying the methods described in the articles about trading currency pair baskets. Let's consider in practice, whether it is possible to use the patterns of the combined WPR graph crossing the moving average. If the answer is yes, we should consider the appropriate usage methods.
The article dwells on Elder-Ray trading system based on Bulls Power, Bears Power and Moving Average indicators (EMA — exponential averaging). This system was described by Alexander Elder in his book "Trading for a Living".
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.
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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.
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.
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.
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 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.
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.
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.
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.
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.
In this article, we will start the development of the library base trading class and add the initial verification of permissions to conduct trading operations to its first version. Besides, we will slightly expand the features and content of the base trading class.
In this article, we will start the development of the new library section - trading classes. Besides, we will consider the development of a unified base trading object for MetaTrader 5 and MetaTrader 4 platforms. When sending a request to the server, such a trading object implies that verified and correct trading request parameters are passed to it.
The article deals with storing data in the program's source code and creating audio and graphical files out of them. When developing an application, we often need audio and images. The MQL language features several methods of using such data.
In this article, we will consider the class of displaying text messages. Currently, we have a sufficient number of different text messages. It is time to re-arrange the methods of their storage, display and translation of Russian or English messages to other languages. Besides, it would be good to introduce convenient ways of adding new languages to the library and quickly switching between them.
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.
The article presents an extended study of seasonal characteristics: autocorrelation heat maps and scatter plots. The purpose of the article is to show that "market memory" is of seasonal nature, which is expressed through maximized correlation of increments of arbitrary order.
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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.
In the previous two articles, we discussed the application of Merrill patterns to various data types. An application was developed to test the presented ideas. In this article, we will continue working with the Strategy Builder, to improve its efficiency and to implement new features and capabilities.
In the previous article, we considered application of Merrill patterns to various data, such as to a price value on a currency symbol chart and values of standard MetaTrader 5 indicators: ATR, WPR, CCI, RSI, among others. Now, let us try to create a strategy construction set based on Merrill patterns.
The article arranges the work of an account object on a new base object of all library objects, improves the CBaseObj base object and tests setting tracked parameters, as well as receiving events for any library objects.
In this article, we will consider creation of a symbol collection based on the abstract symbol object developed in the previous article. The abstract symbol descendants are to clarify a symbol data and define the availability of the basic symbol object properties in a program. Such symbol objects are to be distinguished by their affiliation with groups.
The article provides a critical examination of regular divergence and efficiency of various indicators. In addition, it contains filtering options for an increased analysis accuracy and features description of non-standard solutions. As a result, we will create a new tool for solving the technical task.
In this article, we are going to finish the development of the base object of all library objects, so that any library object based on it is able to interact with a user. For example, users will be able to set the maximum acceptable size of a spread for opening a position and a price level, upon reaching which an event from a symbol object is sent to the program with the spread or price level-based signal.
In previous articles within this series, we tried various methods for creating a more or less profitable grid Expert Advisor. Now we will try to increase the EA profitability through diversification. Our ultimate goal is to reach 100% profit per year with the maximum balance drawdown no more than 20%.
The article provides the description of a simple HTML code parsing library using third-party components. In particular, it covers the possibilities of accessing data which cannot be retrieved using GET and POST requests. We will select a website with not too large pages and will try to obtain interesting data from this site.
In the previous article, we defined position closure events for MQL4 in the library and got rid of the unused order properties. Here we will consider the creation of the Account object, develop the collection of account objects and prepare the functionality for tracking account events.
In this article, we will have a look at Merrill patterns' model and try to evaluate their current relevance. To do this, we will develop a tool to test the patterns and apply the model to various data types such as Close, High and Low prices, as well as oscillators.