thanks.
Most Java coders will be familiar with the auto-generated documentation that can be created with JavaDocs. The idea is to add comments into the code in a semi-structured way that can then be extracted into an easy to navigate help file. The C++ world also has a number of documentation auto-generators, with Microsoft's SandCastle and Doxygen being two leaders. The article describes the use of Doxygen to create HTML help file from structured comments in MQL5 code. The experiment worked very well and I believe the help documentation that Doxygen produces from MQL5 code will add a great deal of value.
In this article, we will create an Expert Advisor for automated entry lot calculation based on risk values. Also the Expert Advisor will be able to automatically place Take Profit with the select ratio to Stop Loss. That is, it can calculate Take Profit based on any selected ratio, such as 3 to 1, 4 to 1 or any other selected value.
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.
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.
The scope of use of fractional differentiation is wide enough. For example, a differentiated series is usually input into machine learning algorithms. The problem is that it is necessary to display new data in accordance with the available history, which the machine learning model can recognize. In this article we will consider an original approach to time series differentiation. The article additionally contains an example of a self optimizing trading system based on a received differentiated series.
14 patterns selected from a large variety of existing candlestick formations.
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.
In the previous articles, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. We already have collections of historical orders and deals, market orders and positions, as well as the class for convenient selection and sorting of orders. In this part, we will continue the development of the base object and teach the Engine Library to track trading events on the account.
