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 considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated.
Comparing several time series during a technical analysis is a quite common task that requires appropriate tools. In this article, I suggest developing a tool for graphical analysis and detecting correlations between two or more time series.
The widget provides websites with a detailed release schedule of 500 indicators and indices, of the world's largest economies. Thus, traders quickly receive up-to-date information on all important events with explanations and graphs in addition to the main website content.
The article deals with the algorithm of developing stock indicators based on real volumes using the CopyTicks() and CopyTicksRange() functions. Some subtle aspects of developing such indicators, as well as their operation in real time and in the strategy tester are also described.
When developing trading algorithms, we often encounter a problem: how to determine where a trend/flat begins and ends? In this article, we try to create a universal indicator, in which we try to combine signals for different types of strategies. We will try to simplify the process of obtaining trade signals in an expert as much as possible. An example of combining several indicators in one will be given.
The article describes custom methods for assessing the trading history. Two classes have been written for downloading and analyzing history. The first of them collects the trading history and represents it as a summary table. The second one deals with statistics: it calculates a number of variables and builds charts for a more efficient evaluation of trading results.
The largest store of ready-made applications for algo-trading now features 13,970 products. This includes 4,800 robots, 6,500 indicators, 2,400 utilities and other solutions. Almost half of the applications (6,000) are available for rent. Also, a quarter of the total number of products (3,800) can be downloaded for free.
Trading account monitoring provides a detailed report on all completed deals. All trading statistics are collected automatically and provided to you as easy-to-understand diagrams and graphs.
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.
This article concludes the series devoted to trading currency pair baskets. Here we test the remaining pattern and discuss applying the entire method in real trading. Market entries and exits, searching for patterns and analyzing them, complex use of combined indicators are considered.
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.
Are you trading using your own strategy? If your system rules can be formally described as software algorithms, it is better to entrust trading to an automated Expert Advisor. A robot does not need sleep or food and is not subject to human weaknesses. In this article, we show how to create Requirements Specification when ordering a trading robot in the Freelance service.
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 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.
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.
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.
The trade Signals service develops in leaps and bounds. Trusting our funds to a signal provider, we would like to minimize the risk of losing our deposit. So how to puzzle out in this forest of trade signals? How to find the one that would produce profits? This paper proposes to create a tool for visually analyzing the history of trades on trade signals in a symbol chart.
In this article, we continue studying the use of CAppDialog. Now we will learn how to set color for the background, borders and header of the dialog box. Also, this article provides a step-by-step description of how to add transparency for an application window when dragging it within the chart. We will consider how to create child classes of CAppDialog or CWndClient and analyze new specifics of working with controls. Finally, we will review new Projects from a new perspective.
Most subscribers choose a trade signal by the beauty of the balance curve and by the number of subscribers. This is why many today's providers care of beautiful statistics rather than of real signal quality, often playing with lot sizes and artificially reducing the balance curve to an ideal appearance. This paper deals with the reliability criteria and the methods a provider may use to enhance its signal quality. An exemplary analysis of a specific signal history is presented, as well as methods that would help a provider to make it more profitable and less risky.
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.
The article demonstrates the development of the ZigZag indicator in accordance with one of the sample specifications described in the article "How to prepare Requirements Specification when ordering an indicator". The indicator is built by extreme values defined using an oscillator. There is an ability to use one of five oscillators: WPR, CCI, Chaikin, RSI or Stochastic Oscillator.
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.
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 ZUP indicator platform allows searching for multiple known patterns, parameters for which have already been set. These parameters can be edited to suit your requirements. You can also create new patterns using the ZUP graphical interfaces and save their parameters to a file. After that you can quickly check, whether these new patterns can be found on charts.
When making trading decisions, we often have to analyze charts on several timeframes. At the same time, these charts often contain graphical objects. Applying the same objects to all charts is inconvenient. In this article, I propose to automate cloning of objects to be displayed on charts.
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 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 compares the classic MQL5 access to indicators with alternative MQL4-style methods. Several varieties of MQL4-style access to indicators are considered: with and without the indicator handles caching. Considering the indicator handles inside the MQL5 core is analyzed as well.
The article features a detailed explanation of how to create a panel on the basis of the CAppDialog class and how to add controls to the panel. It provides the description of the panel structure and a scheme, which shows the inheritance of objects. From this article, you will also learn how events are handled and how they are delivered to dependent controls. Additional examples show how to edit panel parameters, such as the size and the background color.
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.
Most often the first step in the development of a trading system is the creation of a technical indicator, which can identify favorable market behavior patterns. A professionally developed indicator can be ordered from the Freelance service. From this article you will learn how to create a proper Requirements Specification, which will help you to obtain the desired indicator faster.
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 is an intermediate step for those who still writes in MQL4 and has no desire to switch to MQL5. We continue to search for opportunities to write code in MQL4 style. This time, we will look into the macro substitution of the #define preprocessor.
The Strategy Tester in the MetaTrader 5 trading platform provides only two optimization options: complete search of parameters and genetic algorithm. This article proposes a new method for optimizing trading strategies — Simulated annealing. The method's algorithm, its implementation and integration into any Expert Advisor are considered. The developed algorithm is tested on the Moving Average EA.
If you have newly switched to MQL5, then this article will be useful. First, the access to the indicator data and series is done in the usual MQL4 style. Second, this entire simplicity is implemented in MQL5. All functions are as clear as possible and perfectly suited for step-by-step debugging.
