Articles on trading system automation in MQL5

This article concludes the series devoted to trading currency pair baskets. Here we test the remaining pattern and discuss applying the entire method in real trading. Market entries and exits, searching for patterns and analyzing them, complex use of combined indicators are considered.

In this article, we will discuss how to develop a trading robot with minimum programming. Of course, MetaTrader 5 provides a high level of control over trading positions. However, using only the manual ability to place orders can be quite difficult and risky for less experienced users.

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.

The article describes the rules of the trading system by Bill Williams, the procedure of application for a developed MQL5 module to search and mark patterns of this system on the chart, automated trading with found patterns, and also presents the results of testing on various trading instruments.

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 learn how to create Expert Advisors (EAs) working both in MetaTrader 4 and MetaTrader 5. To do this, we are going to develop an EA constructing order grids. Griders are EAs that place several limit orders above the current price and the same number of limit orders below it simultaneously.

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.

Following up our previous article on the currency baskets trading principles, here we are going to analyze the patterns traders can detect. We will also consider the advantages and the drawbacks of each pattern and provide some recommendations on their use. The indicators based on Williams' oscillator will be used as analysis tools.

The original basic article has not lost its relevance and thus if you are interested in this topic, be sure to read the first article. However much time has passed since then, so the current Visual Studio 2017 features an updated interface. The MetaTrader 5 platform has also acquired new features. The article provides a description of dll project development stages, as well as DLL setup and interaction with MetaTrader 5 tools.

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 considers working with account events for tracking important changes in account properties affecting the automated trading. We have already implemented some functionality for tracking account events in the previous article when developing the account object collection.

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.

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.

This article presents Pivot Mean Oscillator (PMO), an implementation of the cumulative moving average (CMA) as a trading indicator for the MetaTrader platforms. In particular, we first introduce Pivot Mean (PM) as a normalization index for timeseries that computes the fraction between any data point and the CMA. We then build PMO as the difference between the moving averages applied to two PM signals. Some preliminary experiments carried out on the EURUSD symbol to test the efficacy of the proposed indicator are also reported, leaving ample space for further considerations and improvements.

This article's objective is to study profitability of algorithms with different entries into trades and exits using trailing stop. Entry types to be used are random entry and reverse entry. Stop orders to be used are trailing stop and trailing take. The article demonstrates money-making algorithms with a profitability of about 30% per annum.

Any Expert Advisor developer, regardless of programming skills, is daily confronted with the same trading tasks and algorithmic problems, which should be solved to organize a reliable trading process. The article describes the possibilities of the CStrategy trading engine that can undertake the solution of these tasks and provide a user with convenient mechanism for describing a custom trading idea.

In this article, we will create the class of a symbol object that is to be the basic object for creating the symbol collection. The class will allow us to obtain data on the necessary symbols for their further analysis and comparison.

In this article, we demonstrate features of botbrains.app - a no-code platform for trading robots development. To create a trading robot you don't need to write any code - just drag and drop the necessary blocks onto the scheme, set their parameters, and establish connections between them.

In the article, we will apply Reinforcement learning to develop self-learning Expert Advisors. In the previous article, we considered the Random Decision Forest algorithm and wrote a simple self-learning EA based on Reinforcement learning. The main advantages of such an approach (trading algorithm development simplicity and high "training" speed) were outlined. Reinforcement learning (RL) is easily incorporated into any trading EA and speeds up its optimization.

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 gives a description of ways of use of the multiple regression analysis for development of trading systems. It demonstrates the use of the regression analysis for strategy search automation. A regression equation generated and integrated in an EA without requiring high proficiency in programming is given as an example.

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.

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.

It is possible to start making money on MQL5.com right now without having to be a seller of Market applications or a profitable signals provider. Select the products you like and post links to them on various web resources. Attract potential customers and the profit is yours!

The article dwells on gaps — significant differences between a close price of a previous timeframe and an open price of the next one, as well as on forecasting a daily bar direction. Applying the GetOpenFileName function by the system DLL is considered as well.

We continue our discussion of the patterns traders can come across while trading currency baskets. In this part, we will consider the patterns formed when using combined trend indicators. Indicators based on a currency index are to be used as the analytical tool.

The concept of diversification of assets on financial markets is quiet old, and has always attracted beginner traders. In this article, the author proposes a maximally simple approach to a construction of a multi-currency Expert Advisor, for an initial introduction to this direction of trading strategies.

In this article, we will consider the main aspects of integration of neural networks and the trading terminal, with the purpose of creating a fully featured trading robot.

In this article we are going to analyze the NRTR indicator and create a trading system based on this indicator. We are going to develop a module of trading signals that can be used in creating strategies based on a combination of NRTR with additional trend confirmation indicators.

The article considers one of the variants for Expert Advisor practical realization to trade DiNapoli levels using MQL5 standard tools. Its performance is tested and conclusions are made.

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.)

This is the must read article for anyone wanting to improve their programming career. This article series is aimed at making you the best programmer you can possibly be, no matter how experienced you are. The discussed ideas work for MQL5 programming newbies as well as professionals.

Efficiency of any trading robot depends on the correct selection of its parameters (optimization). However, parameters that are considered optimal for one time interval may not retain their effectiveness in another period of trading history. Besides, EAs showing profit during tests turn out to be loss-making in real time. The issue of continuous optimization comes to the fore here. When facing plenty of routine work, humans always look for ways to automate it. In this article, I propose a non-standard approach to solving this issue.