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Developing a cross-platform grider EA
Developing a cross-platform grider EA
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.
MetaTrader 5 and Python integration: receiving and sending data
MetaTrader 5 and Python integration: receiving and sending data
Comprehensive data processing requires extensive tools and is often beyond the sandbox of one single application. Specialized programming languages are used for processing and analyzing data, statistics and machine learning. One of the leading programming languages for data processing is Python. The article provides a description of how to connect MetaTrader 5 and Python using sockets, as well as how to receive quotes via the terminal API.
Extracting structured data from HTML pages using CSS selectors
Extracting structured data from HTML pages using CSS selectors
The article provides a description of a universal method for analyzing and converting data from HTML documents based on CSS selectors. Trading reports, tester reports, your favorite economic calendars, public signals, account monitoring and additional online quote sources will become available straight from MQL.
The power of ZigZag (part II). Examples of receiving, processing and displaying data
The power of ZigZag (part II). Examples of receiving, processing and displaying data
In the first part of the article, I have described a modified ZigZag indicator and a class for receiving data of that type of indicators. Here, I will show how to develop indicators based on these tools and write an EA for tests that features making deals according to signals formed by ZigZag indicator. As an addition, the article will introduce a new version of the EasyAndFast library for developing graphical user interfaces.
Martingale as the basis for a long-term trading strategy
Martingale as the basis for a long-term trading strategy
In this article we will consider in detail the martingale system. We will review whether this system can be applied in trading and how to use it in order to minimize risks. The main disadvantage of this simple system is the probability of losing the entire deposit. This fact must be taken into account, if you decide to trade using the martingale technique.
Separate optimization of a strategy on trend and flat conditions
Separate optimization of a strategy on trend and flat conditions
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.
The power of ZigZag (part I). Developing the base class of the indicator
The power of ZigZag (part I). Developing the base class of the indicator
Many researchers do not pay enough attention to determining the price behavior. At the same time, complex methods are used, which very often are simply “black boxes”, such as machine learning or neural networks. The most important question arising in that case is what data to submit for training a particular model.
Applying Monte Carlo method in reinforcement learning
Applying Monte Carlo method in reinforcement learning
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.
100 best optimization passes (part 1). Developing optimization analyzer
100 best optimization passes (part 1). Developing optimization analyzer
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).
Using OpenCL to test candlestick patterns
Using OpenCL to test candlestick patterns
The article describes the algorithm for implementing the OpenCL candlestick patterns tester in the "1 minute OHLC" mode. We will also compare its speed with the built-in strategy tester launched in the fast and slow optimization modes.
Reversing: Reducing maximum drawdown and testing other markets
Reversing: Reducing maximum drawdown and testing other markets
In this article, we continue to dwell on reversing techniques. We will try to reduce the maximum balance drawdown till an acceptable level for the instruments considered earlier. We will see if the measures will reduce the profit. We will also check how the reversing method performs on other markets, including stock, commodity, index, ETF and agricultural markets. Attention, the article contains a lot of images!
Reversal patterns: Testing the Head and Shoulders pattern
Reversal patterns: Testing the Head and Shoulders pattern
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.
Reversal patterns: Testing the Double top/bottom pattern
Reversal patterns: Testing the Double top/bottom pattern
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.
Gap - a profitable strategy or 50/50?
Gap - a profitable strategy or 50/50?
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.
EA remote control methods
EA remote control methods
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.
Reversing: The holy grail or a dangerous delusion?
Reversing: The holy grail or a dangerous delusion?
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.
Using indicators for optimizing Expert Advisors in real time
Using indicators for optimizing Expert Advisors in real time
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.
Combining trend and flat strategies
Combining trend and flat strategies
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?
50,000 completed orders in the MQL5.com Freelance service
50,000 completed orders in the MQL5.com Freelance service
Members of the official MetaTrader Freelance service have completed more than 50,000 orders as at October 2018. This is the world's largest Freelance site for MQL programmers: more than a thousand developers, dozens of new orders daily and 7 languages localization.
Integrating MQL-based Expert Advisors and databases (SQL Server, .NET and C#)
Integrating MQL-based Expert Advisors and databases (SQL Server, .NET and C#)
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.
Deep Neural Networks (Part VII). Ensemble of neural networks: stacking
Deep Neural Networks (Part VII). Ensemble of neural networks: stacking
We continue to build ensembles. This time, the bagging ensemble created earlier will be supplemented with a trainable combiner — a deep neural network. One neural network combines the 7 best ensemble outputs after pruning. The second one takes all 500 outputs of the ensemble as input, prunes and combines them. The neural networks will be built using the keras/TensorFlow package for Python. The features of the package will be briefly considered. Testing will be performed and the classification quality of bagging and stacking ensembles will be compared.
Comparative analysis of 10 flat trading strategies
Comparative analysis of 10 flat trading strategies
The article explores the advantages and disadvantages of trading in flat periods. The ten strategies created and tested within this article are based on the tracking of price movements inside a channel. Each strategy is provided with a filtering mechanism, which is aimed at avoiding false market entry signals.
Implementing indicator calculations into an Expert Advisor code
Implementing indicator calculations into an Expert Advisor code
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.
Visual strategy builder. Creating trading robots without programming
Visual strategy builder. Creating trading robots without programming
This article presents a visual strategy builder. It is shown how any user can create trading robots and utilities without programming. Created Expert Advisors are fully functional and can be tested in the strategy tester, optimized in the cloud or executed live on real time charts.
Deep Neural Networks (Part VI). Ensemble of neural network classifiers: bagging
Deep Neural Networks (Part VI). Ensemble of neural network classifiers: bagging
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.
Expert Advisor featuring GUI: Creating the panel (part I)
Expert Advisor featuring GUI: Creating the panel (part I)
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 Decision Forest in Reinforcement learning
Random Decision Forest in Reinforcement learning
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.
Processing optimization results using the graphical interface
Processing optimization results using the graphical interface
This is a continuation of the idea of processing and analysis of optimization results. This time, our purpose is to select the 100 best optimization results and display them in a GUI table. The user will be able to select a row in the optimization results table and receive a multi-symbol balance and drawdown graph on separate charts.
Trade Operations in MQL5 - It's Easy
Trade Operations in MQL5 - It's Easy
Almost all traders come to market to make money but some traders also enjoy the process itself. However, it is not only manual trading that can provide you with an exciting experience. Automated trading systems development can also be quite absorbing. Creating a trading robot can be as interesting as reading a good mystery novel.
Multi-symbol balance graph in MetaTrader 5
Multi-symbol balance graph in MetaTrader 5
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.
Deep Neural Networks (Part V). Bayesian optimization of DNN hyperparameters
Deep Neural Networks (Part V). Bayesian optimization of DNN hyperparameters
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.
Visualizing trading strategy optimization in MetaTrader 5
Visualizing trading strategy optimization in MetaTrader 5
The article implements an MQL application with a graphical interface for extended visualization of the optimization process. The graphical interface applies the last version of EasyAndFast library. Many users may ask why they need graphical interfaces in MQL applications. This article demonstrates one of multiple cases where they can be useful for traders.