Maxim Korshunitskiy / Profile

Many developers face the same problem - how to get to the trading terminal sandbox without using unsafe DLLs. One of the easiest and safest method is to use standard Named Pipes that work as normal file operations. They allow you to organize interprocessor client-server communication between programs. Take a look at practical examples in C++ and MQL5 that include server, client, data exchange between them and performance benchmark.

The development of trading strategies primarily focuses on searching for patterns for entering and exiting the market, as well as maintaining positions. If we are able to formalize some patterns into rules for automated trading, then the trader faces the question of calculating the volume of positions, the size of the margins, as well as maintaining a safe level of mortgage funds for assuring open positions in an automated mode. In this article we will use the MQL5 language to construct simple examples of conducting these calculations.

The article describes how to implement Interprocess Communication between MetaTrader 5 client terminals using named pipes. For the use of the named pipes, the CNamedPipes class is developed. For the test of its use and to measure the connection throughput, the tick indicator, the server and client scripts are presented. The use of named pipes is sufficient for real-time quotes.

The article proposes Kagi chart indicator with various charting options and additional functions. Also, indicator charting principle and its MQL5 implementation features are considered. The most popular cases of its implementation in trading are displayed - Yin/Yang exchange strategy, pushing away from the trend line and consistently increasing "shoulders"/decreasing "waists".

The article introduces a structure for an Expert Advisor that trades multiple symbols and uses several trading systems simultaneously. If you already identified the optimal input parameters for all your EAs and got good backtesting results for each of them separately, ask yourself what results you would get if testing all EAs simultaneously, with all your strategies put together.

This is the continuation of Another MQL5 OOP class article which showed you how to build a simple OO EA from scratch and gave you some tips on object-oriented programming. Today I am showing you the technical basics needed to develop an EA able to trade the news. My goal is to keep on giving you ideas about OOP and also cover a new topic in this series of articles, working with the file system.

The article describes an example of Renko charting and its implementation in MQL5 as an indicator. Modifications of this indicator distinguish it from a classic chart. It can be constructed both in the indicator window and on the main chart. Moreover, there is the ZigZag indicator. You can find a few examples of the chart implementation.

This article is dedicated to the Three Line Break chart, suggested by Steve Nison in his book "Beyond Candlesticks". The greatest advantage of this chart is that it allows filtering minor fluctuations of a price in relation to the previous movement. We are going to discuss the principle of the chart construction, the code of the indicator and some examples of trading strategies based on it.

The article describes the automation of trend lines plotting based on the Fractals indicator using MQL4 and MQL5. The article structure provides a comparative view of the solution for two languages. Trend lines are plotted using two last known fractals.

If specific neural network programs for trading seem expensive and complex or, on the contrary, too simple, try NeuroPro. It is free and contains the optimal set of functionalities for amateurs. This article will tell you how to use it in conjunction with MetaTrader 5.

This article is dedicated to a new and perspective direction in machine learning - deep learning or, to be precise, deep neural networks. This is a brief review of second generation neural networks, the architecture of their connections and main types, methods and rules of learning and their main disadvantages followed by the history of the third generation neural network development, their main types, peculiarities and training methods. Conducted are practical experiments on building and training a deep neural network initiated by the weights of a stacked autoencoder with real data. All the stages from selecting input data to metric derivation are discussed in detail. The last part of the article contains a software implementation of a deep neural network in an Expert Advisor with a built-in indicator based on MQL4/R.

This article describes the theory of exchange pricing and clearing specifics of Moscow Exchange's Derivatives Market. This is a comprehensive article for beginners who want to get their first exchange experience on derivatives trading, as well as for experienced forex traders who are considering trading on a centralized exchange platform.