There are multiple different approaches to market research and analysis. The main ones are technical and fundamental. In technical analysis, traders collect, process and analyze numerical data and parameters related to the market, including prices, volumes, etc. In fundamental analysis, traders analyze events and news affecting the markets directly or indirectly. The article deals with price velocity measurement methods and studies trading strategies based on that methods.
The article presents a simple and fast method of creating graphical windows using Visual Studio with subsequent integration into the Expert Advisor's MQL code. The article is meant for non-specialist audiences and does not require any knowledge of C# and .Net technology.
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.
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.
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.
Using limit orders instead of conventional take profits has long been a topic of discussions on the forum. What is the advantage of this approach and how can it be implemented in your trading? In this article, I want to offer you my vision of this topic.
This article provides programmatic definition of one of the movement continuation models. The main idea is defining two waves — the main and the correction one. For extreme points, I apply fractals as well as "potential" fractals - extreme points that have not yet formed as fractals.
The main advantage of trading robots lies in the ability to work 24 hours a day on a remote VPS server. But sometimes it is necessary to intervene in their work, while there may be no direct access to the server. Is it possible to manage EAs remotely? The article proposes one of the options for controlling EAs via external commands.
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.
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".
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 is the second part of the article showing the development of a multi-symbol signal Expert Advisor for manual trading. We have already created the graphical interface. It is now time to connect it with the program's functionality.
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.
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 reasons for moving an indicator code to an Expert Advisor may vary. How to assess the pros and cons of this approach? The article describes implementing an indicator code into an EA. Several experiments are conducted to assess the speed of the EA's operation.
The article discusses the methods for building and training ensembles of neural networks with bagging structure. It also determines the peculiarities of hyperparameter optimization for individual neural network classifiers that make up the ensemble. The quality of the optimized neural network obtained in the previous article of the series is compared with the quality of the created ensemble of neural networks. Possibilities of further improving the quality of the ensemble's classification are considered.
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.
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.
MQL programming language allows implementing the concept of modular development of trading strategies. The article shows an example of developing a multi-module Expert Advisor consisting of separately compiled file modules.
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.
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.
The article describes the way to create a custom strategy tester and a custom analyzer of the optimization passes. After reading it, you will understand how the math calculations mode and the mechanism of so-called frames work, how to prepare and load custom data for calculations and use effective algorithms for their compression. This article will also be interesting to those interested in ways of storing custom information within an expert.
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.
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.
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.
Different situations happen in trader’s life. Often, the history of successful trades allows us to restore a strategy, while looking at a loss history we try to develop and improve it. In both cases, we compare trades with known indicators. This article suggests methods of batch comparison of trades with a number of indicators.
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 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.
The main difference of the trading system proposed in the article is the use of mathematical tools for analyzing stock quotes. The system applies digital filtering and spectral estimation of discrete time series. The theoretical aspects of the strategy are described and a test Expert Advisor is created.
This article deals primarily with the classes CExpertAdvisor and CExpertAdvisors, which serve as the container for all the other components described in this article-series regarding cross-platform expert advisors.
A new version of the graphics library for creating scientific charts (the CGraphic class) has been presented recently. This update of the developed library for creating graphical interfaces will introduce a version with a new control for creating charts. Now it is even easier to visualize data of different types.
This article discusses how custom stop levels can be set up in a cross-platform expert advisor. It also discusses a closely-related method by which the evolution of a stop level over time can be defined.
In this update of the library, the Table control (the CTable class) will be supplemented with new options. The lineup of controls in the table cells is expanded, this time adding text edit boxes and combo boxes. As an addition, this update also introduces the ability to resize the window of an MQL application during its runtime.
This article discusses an implementation of stop levels in an expert advisor in order to make it compatible with the two platforms MetaTrader 4 and MetaTrader 5.
The eighth part of the article features the description of the CSymbol class, which is a special object that provides access to any trading instrument. When used inside an Expert Advisor, the class provides a wide set of symbol properties, while allowing to simplify Expert Advisor programming and to expand its functionality.
In the new version of the library, all controls will be drawn on separate graphical objects of the OBJ_BITMAP_LABEL type. We will also continue to describe the optimization of code: changes in the core classes of the library will be discussed.
As the library grows, its code must be optimized again in order to reduce its size. The version of the library described in this article has become even more object-oriented. This made the code easier to learn. A detailed description of the latest changes will allow the readers to develop the library independently based on their own needs.
This article discusses the implementation of various methods of time filtering a cross-platform expert advisor. The time filter classes are responsible for checking whether or not a given time falls under a certain time configuration setting.
This article discusses the implementation of money management method for a cross-platform expert advisor. The money management classes are responsible for the calculation of the lot size to be used for the next trade to be entered by the expert advisor.
This article will implement the ability to select text using various key combinations and deletion of the selected text, similar to the way it is done in any other text editor. In addition, we will continue to optimize the code and prepare the classes to move on to the final process of the second stage of the library's evolution, where all controls will be rendered as separate images (canvases).
