Articles on trading system automation in MQL5

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The article examines the possibility of creating an advanced ZigZag indicator. The idea of identifying nodes is based on the use of the Envelopes indicator. We assume that we can find a certain combination of input parameters for a series of Envelopes, whereby all ZigZag nodes lie within the confines of the Envelopes bands. Consequently, we can try to predict the coordinates of the new node.

Price trends form price channels that can be observed on financial symbol charts. The breakout of the current channel is one of the strong trend reversal signals. In this article, I suggest a way to automate the process of finding such signals and see if the channel breakout pattern can be used for creating a trading strategy.

This article covers the main principles set fourth in evolutionary algorithms, their variety and features. We will conduct an experiment with a simple Expert Advisor used as an example to show how our trading system benefits from optimization. We will consider software programs that implement genetic, evolutionary and other types of optimization, and provide examples of application when optimizing a predictor set and parameters of the trading system.

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The article describes an example of Renko charting and its implementation in MQL5 as an indicator. Modifications of this indicator distinguish it from a classic chart. It can be constructed both in the indicator window and on the main chart. Moreover, there is the ZigZag indicator. You can find a few examples of the chart implementation.

The article deals with the concept of night trading, as well as trading strategies and their implementation in MQL5. We perform tests and make appropriate conclusions.

The article dwells on the development of an application for selecting the best optimization passes using several possible options. The application is able to sort out the optimization results by a variety of factors. Optimization passes are always written to a database, therefore you can always select new robot parameters without re-optimization. Besides, you are able to see all optimization passes on a single chart, calculate parametric VaR ratios and build the graph of the normal distribution of passes and trading results of a certain ratio set. Besides, the graphs of some calculated ratios are built dynamically beginning with the optimization start (or from a selected date to another selected date).

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The article provides a brief overview of ten trend following strategies, as well as their testing results and comparative analysis. Based on the obtained results, we draw a general conclusion about the appropriateness, advantages and disadvantages of trend following trading.

Is it possible to develop an Expert Advisor able to optimize position open and close conditions at regular intervals according to the code commands? What happens if we implement a neural network (multilayer perceptron) in the form of a module to analyze history and provide strategy? We can make the EA optimize a neural network monthly (weekly, daily or hourly) and continue its work afterwards. Thus, we can develop a self-optimizing EA.

Subscribers often search for an appropriate signal by analyzing the total growth on the signal provider's account, which is not a bad idea. However, it is also important to analyze potential risks of a particular trading strategy. In this article we will show a simple and efficient way to evaluate a Trading Signal based on its performance values.

In order to make profits based on high expectations, we must understand three basic principles of good trading: 1) know your risk when entering the market; 2) cut your losses early and allow your profit to run; 3) know the expectation of your system – test and adjust it regularly. This article provides a program code trailing open positions and actualizing the second golden principle, as it allows profit to run for the highest possible level.

This article focuses on the object oriented approach to doing what we did in the article "Step-By-Step Guide to writing an Expert Advisor in MQL5 for Beginners" - creating a simple Expert Advisor. Most people think this is difficult, but I want to assure you that by the time you finish reading this article, you will be able to write your own Expert Advisor which is object oriented based.

This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package.

The article is based on 'The Mathematics of Money Management' by Ralph Vince. It provides the description of empirical and parametric methods used for finding the optimal size of a trading lot. Also the article features implementation of trading modules for the MQL5 Wizard based on these methods.

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.

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 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".

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.

How many cores do you have on your home computer? How many computers can you use to optimize a trading strategy? We show here how to use the MQL5 Cloud Network to accelerate calculations by receiving the computing power across the globe with the click of a mouse. The phrase "Time is money" becomes even more topical with each passing year, and we cannot afford to wait for important computations for tens of hours or even days.

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.

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?

This article draws your attention to the brief review of the method and program code of the mechanical trading system based on the technique proposed by Stanislav Chuvashov. The market analysis considered in the article has something in common with Thomas DeMark's approach to drawing trend lines for the last closest time interval, fractals being the reference points in the construction of trend lines.

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.

A big program starts with a small file, which then grows in size as you keep adding more functions and objects. Most robot developers utilize include files to handle this problem. However, there is a better solution: start developing any trading application in a project. There are so many reasons to do so.

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.

In this article, we continue to consider writing the code to trading systems described in a book by Linda B. Raschke and Laurence A. Connors “Street Smarts: High Probability Short-Term Trading Strategies”. This time we study Momentum Pinball system: there is described creation of two indicators, trade robot and signal block on it.

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.