Articles on the MQL5 programming and use of trading robots

What should your program do, if graphic control panels have been removed or modified by someone else? In this article we will show you how not to have "ownerless" objects on the chart, and how not to lose control over them in cases of renaming or deleting programmatically created objects after the application is deleted.

The purpose of this article is to create a custom tool, which would enable users to receive and use the entire array of information about patterns discussed earlier. We will create a library of pattern related functions which you will be able to use in your own indicators, trading panels, Expert Advisors, etc.

For successful trading, we almost always need indicators that can separate the main price movement from noise fluctuations. In this article, we consider one of the most promising digital filters, the Kalman filter. The article provides the description of how to draw and use the filter.

The article discusses how to write your own class of trading signals with the implementation of signals on the crossing of the price and the moving average, and how to include it to the generator of trading strategies of the MQL5 Wizard, as well as describes the structure and format of the description of the generated class for the MQL5 Wizard.

In this article, I will share with you one of the most popular and commonly used indicators in the world of trading which is RSI. You will learn how to design a trading system using this indicator.

The article features formalized rules of two trading strategies 'Turtle Soup' and 'Turtle Soup Plus One' from Street Smarts: High Probability Short-Term Trading Strategies by Linda Bradford Raschke and Laurence A. Connors. The strategies described in the book are quite popular. But it is important to understand that the authors have developed them based on the 15...20 year old market behavior.

This article will teach you how to receive trade signals that are necessary for a trade system to work. The examples of forming 20 trade signals are given here as separate custom functions that can be used while developing Expert Advisors. For your convenience, all the functions used in the article are combined in a single mqh include file that can be easily connected to a future Expert Advisor.

Random Forest (RF) with the use of bagging is one of the most powerful machine learning methods, which is slightly inferior to gradient boosting. This article attempts to develop a self-learning trading system that makes decisions based on the experience gained from interaction with the market.

This article explains how to use the major functionalities of the MQL5 Standard Library Trade Classes in writing Expert Advisors which implements position closing and modifying, pending order placing and deletion and verifying of Margin before placing a trade. We have also demonstrated how Trade classes can be used to obtain order and deal details.

There are numerous trading strategies out there. Some of them look for a trend, while others define ranges of price fluctuations to trade within them. Is it possible to combine these two approaches to increase profitability?

The article provides an example of an MQL application with its graphical interface featuring multi-symbol balance and deposit drawdown graphs based on the last test results.

We now know that probability density function (PDF) of a market cycle does not remind a Gaussian but rather a PDF of a sine wave and most of the indicators assume that the market cycle PDF is Gaussian we need a way to "correct" that. The solution is to use Fisher Transform. The Fisher transform changes PDF of any waveform to approximately Gaussian. This article describes the mathematics behind the Fisher Transform and the Inverse Fisher Transform and their application to trading. A proprietary trading signal module based on the Inverse Fisher Transform is presented and evaluated.

The article deals with the popular trading method - triangular arbitrage. Here we analyze the topic in as much detail as possible, consider the positive and negative aspects of the strategy and develop the ready-made Expert Advisor code.

Traders often look for trend reversal points since the price has the greatest potential for movement at the very beginning of a newly formed trend. Consequently, various reversal patterns are considered in the technical analysis. The Double top/bottom is one of the most well-known and frequently used ones. The article proposes the method of the pattern programmatic detection. It also tests the pattern's profitability on history data.

Profitability of trading systems is defined not only by logic and precision of analyzing the financial instrument dynamics, but also by the quality of the performance algorithm of this logic. False trigger is typical for low quality performance of the main logic of a trading robot. Ways of solving the specified problem are considered in this article.

The article considers an example of applying the fuzzy logic to build a simple trading system, using the Fuzzy library. Variants for improving the system by combining fuzzy logic, genetic algorithms and neural networks are proposed.

This article explains the step by step process of identifying and resolving code errors as well as the steps in testing and optimizing of the Expert Advisor input parameters. You will learn how to use Strategy Tester of MetaTrader 5 client terminal to find the best symbol and set of input parameters for your Expert Advisor.

The article considers the possibility to apply Bayesian optimization to hyperparameters of deep neural networks, obtained by various training variants. The classification quality of a DNN with the optimal hyperparameters in different training variants is compared. Depth of effectiveness of the DNN optimal hyperparameters has been checked in forward tests. The possible directions for improving the classification quality have been determined.

The article deals with various ways of how to control open positions and pending orders. It is devoted to simplifying of writing Expert Advisors.

The article describes how to add the ability to work with Microsoft SQL Server database server to MQL5-based Expert Advisors. Import of functions from a DLL is used. The DLL is created using the Microsoft .NET platform and the C# language. The methods used in the article are also suitable for experts written in MQL4, with minor adjustments.

When developing indicators, Expert Advisors and scripts, developers often need to create various pieces of code, which are not directly related to the trading strategy. In this article, we consider a way to create Expert Advisors using earlier created blocks, such as trailing, filtering and scheduling code, among others. We will see the benefits of this programming approach.

In this article I will discuss the development of Expert Advisor, based on the book "New Trading Dimensions: How to Profit from Chaos in Stocks, Bonds, and Commodities" by Bill Williams. The strategy itself is well known and its use is still controversial among traders. The article considers trading signals of the system, the specifics of its implementation, and the results of testing on historical data.

This article is a follow-up to the previous one called "Reversal patterns: Testing the Double top/bottom pattern". Now we will have a look at another well-known reversal pattern called Head and Shoulders, compare the trading efficiency of the two patterns and make an attempt to combine them into a single trading system.

Despite the fact that many traders still prefer manual trading, it is hardly possible to completely avoid the automation of routine operations. The article shows an example of developing a multi-symbol signal Expert Advisor for manual trading.

Is it possible to trade on a real MetaTrader 5 account today? How to organize such trading? The article contains the theory of these questions and the working codes used for copying trades from the MetaTrader 5 terminal to MetaTrader 4. The article will be useful both for the developers of Expert Advisors and for practicing traders.

This is the first article in a series related to reversal patterns in the framework of algorithmic trading. We will begin with the most interesting pattern family, which originate from the Double Top and Double Bottom patterns.

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.

In this article we look at the possibility of creating a flexible news feed that offers more options in terms of the type of news and also its source. The article will show how a web API can be integrated with the MetaTrader 5 terminal.

In this article, we will study the reverse martingale technique and will try to understand whether it is worth using, as well as whether it can help improve your trading strategy. We will create an Expert Advisor to operate on historic data and to check what indicators are best suitable for the reversing technique. We will also check whether it can be used without any indicator as an independent trading system. In addition, we will check if reversing can turn a loss-making trading system into a profitable one.

In early 2011 we released the first version of the MQL5 Wizard. This new application provides a simple and convenient tool to automatically generate trading robots. Any MetaTrader 5 user can create a custom Expert Advisor without even knowing how to program in MQL5.

In this article, we will learn about Bollinger Bands which is one of the most popular indicators in the trading world. We will consider technical analysis and see how to design an algorithmic trading system based on the Bollinger Bands indicator.

Creation of expert advisors in the MetaTrader trading system has a number of features.

The number of trading robots used on the currency markets has significantly increased recently. They employ various concepts and strategies, however, none of them has yet succeeded to create a win-win sample of artificial intelligence. Therefore, many traders remain committed to manual trading. But even for such specialists, robotic assistants or, so called, trading panels, are created. This article is yet another example of creating a trading panel from scratch.

In this article, I will continue the development of the topic by improving the flexibility of the previously created algorithm. The algorithm became more stable with an increase in the number of candles in the analysis window or with an increase in the threshold percentage of the overweight of falling or growing candles. I had to make a compromise and set a larger sample size for analysis or a larger percentage of the prevailing candle excess.

Automation of trading strategies involving graphical patterns requires the ability to search for extreme points on the charts for further processing and interpretation. Existing tools do not always provide such an ability. The algorithms described in the article allow finding all extreme points on charts. The tools discussed here are equally efficient both during trends and flat movements. The obtained results are not strongly affected by a selected timeframe and are only defined by a specified scale.

This article focuses on specifics of choice, preconditioning and evaluation of the input variables (predictors) for use in machine learning models. New approaches and opportunities of deep predictor analysis and their influence on possible overfitting of models will be considered. The overall result of using models largely depends on the result of this stage. We will analyze two packages offering new and original approaches to the selection of predictors.

Support Vector Machines have long been used in fields such as bioinformatics and applied mathematics to assess complex data sets and extract useful patterns that can be used to classify data. This article looks at what a support vector machine is, how they work and why they can be so useful in extracting complex patterns. We then investigate how they can be applied to the market and potentially used to advise on trades. Using the Support Vector Machine Learning Tool, the article provides worked examples that allow readers to experiment with their own trading.

The article describes a method of automated creation of neural network EAs using MQL5 Wizard and Hlaiman EA Generator. It shows you how you can easily start working with neural networks, without having to learn the entire body of theoretical information and writing your own code.