MQL4 and MQL5 Programming Articles

In this article, I will show you how to calculate the total profit or loss of any trade, including commission and swap. I will provide the most accurate mathematical model and use it to write the code and compare it with the standard. Besides, I will also try to get on the inside of the main MQL5 function to calculate profit and get to the bottom of all the necessary values from the specification.

MetaTrader 4 and MetaTrader 5 uses different conventions in processing trade requests. This article discusses the possibility of using a class object that can be used to represent the trades processed by the server, in order for a cross-platform expert advisor to further work on them, regardless of the version of the trading platform and mode being used.

The article describes the library allowing you to increase the efficiency of working with HTTP requests in MQL5. Execution of WebRequest in non-blocking mode is implemented in additional threads that use auxiliary charts and Expert Advisors, exchanging custom events and reading shared resources. The source codes are applied as well.

All channel indicators are displayed as three lines, including central, top and bottom lines. The drawing principle of the central line is similar to a moving average, while the moving average indicator is mostly used for drawing channels. The top and bottom lines are located at equal distances from the central line. This distance can be determined in points, as percent of price (the Envelopes indicator), using a standard deviation value (Bollinger Bands), or an ATR value (Keltner channel).

In the previous part, we considered the implementation of the MySQL connector. In this article, we will consider its application by implementing the service for collecting signal properties and the program for viewing their changes over time. The implemented example has practical sense if users need to observe changes in properties that are not displayed on the signal's web page.

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.

Testing manual strategies on history. Check how your trading algorithm works turning a deaf ear to programming niceties!

In the previous articles, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. We already have collections of historical orders and deals, market orders and positions, as well as the class for convenient selection and sorting of orders. In this part, we will continue the development of the base object and teach the Engine Library to track trading events on the account.

The technical analysis widely implements the indicators showing the basic quotes "more clearly" and allowing traders to perform analysis and forecast market prices movement. It's quite obvious that there is no sense in using the indicators, let alone applying them in creation of trading systems, unless we can solve the issues concerning initial quotes transformation and the obtained result credibility. In this article we show that there are serious reasons for such a conclusion.

The article deals with conflict-free trading of several experts on one МТ 4 Client Terminal. It will be useful for those who have basic command of working with the terminal and programming in MQL 4.

The article presents a new version of the Pattern Analyzer application. This version provides bug fixes and new features, as well as the revised user interface. Comments and suggestions from previous article were taken into account when developing the new version. The resulting application is described in this article.

This article seeks to upgrade the indicator created earlier on and briefly deals with a method for estimating forecast confidence intervals using bootstrapping and quantiles. As a result, we will get the forecast indicator and scripts to be used for estimation of the forecast accuracy.

Genetic (evolutionary) algorithms are used for optimization purposes. An example of such purpose can be neuronet learning, i.e., selection of such weight values that allow reaching the minimum error. At this, the genetic algorithm is based on the random search method.

The article is devoted to the practical application of the fuzzy logic concept for financial markets analysis. We propose the example of the indicator generating signals based on two fuzzy rules based on Envelopes indicator. The developed indicator uses several indicator buffers: 7 buffers for calculations, 5 buffers for the charts display and 2 color buffers.

In this article, I will continue the development of the basic graphical element class of all library graphical objects powered by the CCanvas Standard Library class. I will create the methods for drawing graphical primitives and for displaying a text on a graphical element object.

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.

It is impossible to get a truly stable algorithm if we use optimization based on historical data to select parameters. A stable algorithm should be aware of what parameters are needed when working on any trading instrument at any time. It should not forecast or guess, it should know for sure.

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.

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.

The article gives formulas and the calculation order for data shown in the Tester report.

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.

The article deals with the methods of indicators computational algorithms optimization. Everyone will find a method that suits his/her needs best. Three methods are described here.One of them is quite simple, the next one requires solid knowledge of Math and the last one requires some wit. Indicators or MetaTrader5 terminal design features are used to realize most of the described methods. The methods are quite universal and can be used not only for acceleration of the linear regression calculation, but also for many other indicators.

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.

Environment MQL4 is based on the conception of safe "sandbox": reading and saving files using the language are allowed in some predefined folders only. This protects the user of MetaTrader 4 from the potential danger of damaging important data on the HDD. However, it is sometimes necessary to leave that safe area. This article is devoted to the problem of how to do it easily and correctly.

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.

This is the last article within the series devoted to the Reversing trading strategy. Here we will try to solve the problem, which caused the testing results instability in previous articles. We will also develop and test our own algorithm for manual trading in any market using the reversing strategy.

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.

In my previous article, I mentioned the importance of identifying the trend which is the direction of prices. In this article I will share one of the most important concepts and indicators which is the Momentum indicator. I will share how to design a trading system based on this Momentum indicator.

This article has been made to show you how to use neural networks, via FANN2MQL, using an easy example: teaching a simple pattern to the neuralnetwork, and testing it to see if it can recognize patterns it has never seen.

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 deals with the simultaneous launch of Expert Advisor testing on four different trading instruments. The final comparison of four testing reports is provided in a table similar to how goods are represented in online stores. An additional bonus is that distribution charts will be automatically created for each symbol.

The article demonstrates the capabilities and potential of a well-known mathematical method coupled with visual thinking and an "out of the box" market outlook. On the one hand, it serves to attract the attention of a wide audience as it can get the creative minds to reconsider the trading paradigm as such. And on the other, it can give rise to alternative developments and program code implementations regarding a wide range of tools for analysis and forecasting.

Studies related to search for the fractal behavior of financial data suggest that behind the seemingly chaotic behavior of economic time series there are hidden stable mechanisms of participants' collective behavior. These mechanisms can lead to the emergence of price dynamics on the exchange, which can define and describe specific properties of price series. When applied to trading, one could benefit from the indicators which can efficiently and reliably estimate the fractal parameters in the scale and time frame, which are relevant in practice.

The problem of calculation of the total position volume of the specified symbol and magic number is considered in this article. The proposed method requests only the minimum necessary part of the history of deals, finds the closest time when the total position was equal to zero, and performs the calculations with the recent deals. Working with global variables of the client terminal is also considered.