History of MQL5.community development

Graphical Interfaces X: Updates for Easy And Fast Library (Build 3)

The next version of the Easy And Fast library (version 3) is presented in this article. Fixed certain flaws and added new features. More details further in the article.

MQL5 Programming Basics: Files

This practice-oriented article focuses on working with files in MQL5. It offers a number of simple tasks allowing you to grasp the basics and hone your skills.

Graphical Interfaces X: The Standard Chart Control (build 4)

This time we will consider the Standard chart control. It will allow to create arrays of subcharts with the ability to synchronize horizontal scrolling. In addition, we will continue to optimize the library code to reduce the CPU load.

Graphical Interfaces X: Updates for Easy And Fast Library (Build 3)

The next version of the Easy And Fast library (version 3) is presented in this article. Fixed certain flaws and added new features. More details further in the article.

Neural network: Self-optimizing Expert Advisor

Is it possible to develop an Expert Advisor able to optimize position open and close conditions at regular intervals according to the code commands? What happens if we implement a neural network (multilayer perceptron) in the form of a module to analyze history and provide strategy? We can make the EA optimize a neural network monthly (weekly, daily or hourly) and continue its work afterwards. Thus, we can develop a self-optimizing EA.

Statistical Distributions in MQL5 - taking the best of R and making it faster

The functions for working with the basic statistical distributions implemented in the R language are considered. Those include the Cauchy, Weibull, normal, log-normal, logistic, exponential, uniform, gamma distributions, the central and noncentral beta, chi-squared, Fisher's F-distribution, Student's t-distribution, as well as the discrete binomial and negative binomial distributions, geometric, hypergeometric and Poisson distributions. There are functions for calculating theoretical moments of distributions, which allow to evaluate the degree of conformity of the real distribution to the modeled one.