In the previous articles (Part 1, Part 2, Part 3), we experimented with shapes and angles whose values were passed to the perceptron and the neural network built on the basis of the DeepNeuralNetwork.mqh library. We also conducted experiments on optimization methods in the strategy tester. An important task in the current experiments was to track the influence of the amount of transmitted data and the depth of history we take this data from. ...
In a previous article Creating an EA that works automatically (Part 9): Automation (I), we looked at how to create a breakeven and trailing stop system that uses two different modes. One of them uses a stop line on OCO positions, while the other uses a pending order as a stop level. more...
In the previous article Creating an EA that works automatically (Part 08): OnTradeTransaction, it was explained how we can take advantage of the MetaTrader 5 platform by using a rather interesting event handling function. We will start now with building the first level of automation in our EA.[/QUOTE] more...
It is about a method to overcome this challenge by benefiting from the iCustom function and creating your custom indicator following your terms and based on your preferences. We will also see an example, as we will create a custom Heiken Ashi technical indicator and we will use this custom indicator in trading system examples. more...
Monkey Algorithm (MA) is a metaheuristic search algorithm. This article will describe the main components of the algorithm and present solutions for the ascent (upward movement), local jump and global jump. The algorithm was proposed by R. Zhao and W. Tang in 2007. The algorithm simulates the behavior of monkeys as they move and jump over mountains in search of food. It is assumed that the monkeys come from the fact that the higher the mountain, the more ...
Go to Profile
Mark as Read
