Training the CatBoost classifier in Python and exporting the model to mql5, as well as parsing the model parameters and a custom strategy tester. The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.
The article provides a description and instructions for the practical use of neural network modules on the Matlab platform. It also covers the main aspects of creation of a trading system using the neural network module. In order to be able to introduce the complex within one article, I had to modify it so as to combine several neural network module functions in one program.
This article describes the connection of the graphical part of the auto optimizer program with its logical part. It considers the optimization launch process, from a button click to task redirection to the optimization manager.
We have previously considered the creation of automatic walk-forward optimization. This time, we will proceed to the internal structure of the auto optimizer tool. The article will be useful for all those who wish to further work with the created project and to modify it, as well as for those who wish to understand the program logic. The current article contains UML diagrams which present the internal structure of the project and the relationships between objects. It also describes the process of optimization start, but it does not contain the description of the optimizer implementation process.
This article provides further description of the walk-forward optimization in the MetaTrader 5 terminal. In previous articles, we considered methods for generating and filtering the optimization report and started analyzing the internal structure of the application responsible for the optimization process. The Auto Optimizer is implemented as a C# application and it has its own graphical interface. The fifth article is devoted to the creation of this graphical interface.
The MetaTrader 5 platform allows developing and testing trading robots that simultaneously trade multiple financial instruments. The built-in Strategy Tester automatically downloads required tick history from the broker's server taking into account contract specifications, so the developer does not need to do anything manually. This makes it possible to easily and reliably reproduce trading environment conditions, including even millisecond intervals between the arrival of ticks on different symbols. In this article we will demonstrate the development and testing of a spread strategy on two Moscow Exchange futures.
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.
Before launching a robot on a trading account, we usually test and optimize it on quotes history. However, a reasonable question arises: how can past results help us in the future? The article describes applying the Monte Carlo method to construct custom criteria for trading strategy optimization. In addition, the EA stability criteria are considered.
Scalping automatic systems are rightfully regarded the pinnacle of algorithmic trading, but at the same time their code is the most difficult to write. In this article we will show how to build strategies based on analysis of incoming ticks using the built-in debugging tools and visual testing. Developing rules for entry and exit often require years of manual trading. But with the help of MetaTrader 5, you can quickly test any such strategy on real history.
Creating custom symbols pushes the boundaries in the development of trading systems and financial market analysis. Now traders are able to plot charts and test trading strategies on an unlimited number of financial instruments.
The article deals with the approaches enabling accurate simulation of walk forward optimization using the built-in tester and auxiliary libraries implemented in MQL.
Before any product is published in the Market, it must undergo compulsory preliminary checks in order to ensure a uniform quality standard. This article considers the most frequent errors made by developers in their technical indicators and trading robots. An also shows how to self-test a product before sending it to the Market.
In this article, we consider yet another custom trading strategy optimization criterion based on the balance graph analysis. The linear regression is calculated using the function from the ALGLIB library.
The main purpose of the article is to describe the mechanism of working with our application and its capabilities. Thus the article can be treated as an instruction on how to use the application. It covers all possible pitfalls and specifics of the application usage.
In this article, I would like to give an example of what a trader's program can be like as well as what results can be achieved in 9 months, having started to learn MQL5 from scratch. This example will also show how multi-functional and informative such a program can be for a trader while taking minimum space on the price chart. And we will be able to see just how colorful, bright and intuitively clear to the user trade information panels can get. As well as many other features...
Buying a trading robot on MQL5 Market has a distinct benefit over all other similar options - an automated system offered can be thoroughly tested directly in the MetaTrader 5 terminal. Before buying, an Expert Advisor can and should be carefully run in all unfavorable modes in the built-in Strategy Tester to get a complete grasp of the system.
This article is intended primarily for the programmers who have already learned the language but have not fully mastered the program development yet. It reveals some debugging techniques and presents a combined experience of the author and many other programmers.
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.
You have decided to study MQL5 trading strategies' programming language, but you know nothing about it? We have tried to examine MQL5 and MetaTrader 5 terminal from the newcomers' point of view and have written this short introductory article. In this article, you can find a brief idea of the possibilities of the language, as well as some tips on working with MetaEditor 5 and the terminal.
What are the differences between the three modes of testing in MetaTrader 5, and what should be particularly looked for? How does the testing of an EA, trading simultaneously on multiple instruments, take place? When and how are the indicator values calculated during testing, and how are the events handled? How to synchronize the bars from different instruments during testing in an "open prices only" mode? This article aims to provide answers to these and many other questions.
We all know the saying "Better to see once than hear a hundred times". You can read various books about Paris or Venice, but based on the mental images you wouldn't have the same feelings as on the evening walk in these fabulous cities. The advantage of visualization can easily be projected on any aspect of our lives, including work in the market, for example, the analysis of price on charts using indicators, and of course, the visualization of strategy testing. This article contains descriptions of all the visualization features of the MetaTrader 5 Strategy Tester.
In this article, we will create a pattern that uses a single set of parameters for optimization of a trading system, while allowing for unlimited number of parameters. The list of symbols will be created in a standard text file (*.txt). Input parameters for each symbol will also be stored in files. This way we will be able to circumvent the restriction of the terminal on the number of input parameters of an Expert Advisor.
The first article within the Walk-Through Optimization series described the creation of a DLL to be used in our auto optimizer. This continuation is entirely devoted to the MQL5 language.
The third part serves as a bridge between the previous two parts: it describes the mechanism of interaction with the DLL considered in the first article and the objects for report downloading, which were described in the second article. We will analyze the process of wrapper creation for a class which is imported from DLL and which forms an XML file with the trading history. We will also consider a method for interacting with this wrapper.
The development of trading strategies is associated with handling large amounts of data. Now, you are able to work with databases using SQL queries based on SQLite directly in MQL5. An important feature of this engine is that the entire database is placed in a single file located on a user's PC.
In this article we will view seasonal characteristics of financial time series using Boxplot diagrams. Each separate boxplot (or box-and-whiskey diagram) provides a good visualization of how values are distributed along the dataset. Boxplots should not be confused with the candlestick charts, although they can be visually similar.
The first article is devoted to the creation of a toolkit for working with optimization reports, for importing them from the terminal, as well as for filtering and sorting the obtained data. MetaTrader 5 allows downloading optimization results, however our purpose is to add our own data to the optimization report.
The article considers an approach to stress testing of a trading strategy using custom symbols. A custom symbol class is created for this purpose. This class is used to receive tick data from third-party sources, as well as to change symbol properties. Based on the results of the work done, we will consider several options for changing trading conditions, under which a trading strategy is being tested.
This article is a continuation of the previous publication related to the creation of a graphical interface for optimization management. The article considers the logic of the add-on. A wrapper for the MetaTrader 5 terminal will be created: it will enable the running of the add-on as a managed process via C#. In addition, operation with configuration files and setup files is considered in this article. The application logic is divided into two parts: the first one describes the methods called after pressing a particular key, while the second part covers optimization launch and management.
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.
The article describes an attempt to combine theory with practice in the algorithmic trading field. Most of discussions concerning the creation of Trading Systems is connected with the use of historic bars and various indicators applied thereon. This is the most well covered field and thus we will not consider it. Bars represent a very artificial entity; therefore we will work with something closer to proto-data, namely the price ticks.
This article describes the process of creating an extension for the MetaTrader terminal. The solution discussed helps to automate the optimization process by running optimizations in other terminals. A few more articles will be written concerning this topic. The extension has been developed using the C# language and design patterns, which additionally demonstrates the ability to expand the terminal capabilities by developing custom modules, as well as the ability to create custom graphical user interfaces using the functionality of a preferred programming language.
In this article, we will consider popular candlestick patterns and will try to find out if they are still relevant and effective in today's markets. Candlestick analysis appeared more than 20 years ago and has since become quite popular. Many traders consider Japanese candlesticks the most convenient and easily understandable asset price visualization form.
In this article we will perform an experiment: we will color optimization results. The color is determined by three parameters: the levels of red, green and blue (RGB). There are other color coding methods, which also use three parameters. Thus, three testing parameters can be converted to one color, which visually represents the values. Read this article to find out if such a representation can be useful.
In the previous article, we analyzed 14 patterns selected from a large variety of existing candlestick formations. It is impossible to analyze all the patterns one by one, therefore another solution was found. The new system searches and tests new candlestick patterns based on known candlestick types.
The article considers applying the separate optimization method during various market conditions. Separate optimization means defining trading system's optimal parameters by optimizing for an uptrend and downtrend separately. To reduce the effect of false signals and improve profitability, the systems are made flexible, meaning they have some specific set of settings or input data, which is justified because the market behavior is constantly changing.
Based on universal tools designed for working with Kohonen networks, we construct the system of analyzing and selecting the optimal EA parameters and consider forecasting time series. In Part I, we corrected and improved the publicly available neural network classes, having added necessary algorithms. Now, it is time to apply them to practice.
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).
The article provides an overview of the terminal's capabilities for creating and working with custom symbols, offers options for simulating a trading history using custom symbols, trend and various chart patterns.
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.
