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The article deals with a simple approach to creating an automated trading system based on the chart linear markup and offers a ready-made Expert Advisor using the standard properties of the MetaTrader 4 and 5 objects and supporting the main trading operations.
We provide a special installer for the MetaTrader 4 trading platform on macOS. It is a full-fledged wizard that allows you to install the application natively. The installer performs all the required steps: it identifies your system, downloads and installs the latest Wine version, configures it, and then installs MetaTrader within it. All steps are completed in the automated mode, and you can start using the platform immediately after installation.
Cross-Platform Expert Advisor: Stops
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.
How to conduct a qualitative analysis of trading signals and select the best of them
The article deals with evaluating the performance of Signals Providers. We offer several additional parameters highlighting signal trading results from a slightly different angle than in traditional approaches. The concepts of the proper management and perfect deal are described. We also dwell on the optimal selection using the obtained results and compiling the portfolio of multiple signal sources.
Creating and testing custom symbols in MetaTrader 5
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.
Using cloud storage services for data exchange between terminals
Cloud technologies are becoming more popular. Nowadays, we can choose between paid and free storage services. Is it possible to use them in trading? This article proposes a technology for exchanging data between terminals using cloud storage services.
We provide a special installer for the MetaTrader 4 trading platform on macOS. It is a full-fledged wizard that allows you to install the application natively. The installer performs all the required steps: it identifies your system, downloads and installs the latest Wine version, configures it, and then installs MetaTrader within it. All steps are completed in the automated mode, and you can start using the platform immediately after installation.
This article considers new capabilities of the darch package (v.0.12.0). It contains a description of training of a deep neural networks with different data types, different structure and training sequence. Training results are included.
Universal Expert Advisor: Accessing Symbol Properties (Part 8)
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.
We provide a special installer for the MetaTrader 4 trading platform on macOS. It is a full-fledged wizard that allows you to install the application natively. The installer performs all the required steps: it identifies your system, downloads and installs the latest Wine version, configures it, and then installs MetaTrader within it. All steps are completed in the automated mode, and you can start using the platform immediately after installation.
Cross-Platform Expert Advisor: Stops
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.
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.
This article is a continuation of the series of articles about deep neural networks. Here we will consider selecting samples (removing noise), reducing the dimensionality of input data and dividing the data set into the train/val/test sets during data preparation for training the neural network.