Andrey Dik / Profile
- Information
10+ years
experience
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25
products
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16
demo versions
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14
jobs
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0
signals
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0
subscribers
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I have been developing systems based on machine learning technologies since 2007 and in the field of artificial
intelligence, optimization and forecasting.
I took an active part in the development of the MT5 platform, such as the introduction of support for universal parallel
computing on the GPU and CPU with OpenCL, testing and backtesting of distributed
computing in the LAN and cloud during optimization in MT5, my test functions are included in the standard delivery of the terminal.
A series of articles on optimization algorithms:
Genetic algorithms are easy!: https://www.mql5.com/ru/articles/55
Population optimization algorithms: https://www.mql5.com/en/articles/8122
Population optimization algorithms: Particle Swarm (PSO): https://www.mql5.com/ru/articles/11386
Population optimization algorithms: Ant Colony Optimization (ACO): https://www.mql5.com/en/articles/11602
Population optimization algorithms: Artificial Bee Colony (ABC): https://www.mql5.com/ru/articles/11736
Population optimization algorithms: Gray Wolf Optimizer (GWO): https://www.mql5.com/en/articles/11785
Population optimization algorithms: Cuckoo Optimization Algorithm (COA): https://www.mql5.com/en/articles/11786
Population Optimization Algorithms: Fish School Search (FSS): https://www.mql5.com/ru/articles/11841
Population Optimization Algorithms: Firefly Algorithm (FA): https://www.mql5.com/ru/articles/11873
Population Optimization Algorithms: Bat algorithm (BA): https://www.mql5.com/ru/articles/11915
Population Optimization Algorithms: Invasive Weed Optimization (IWO): https://www.mql5.com/ru/articles/11990
All my publications: https://www.mql5.com/en/users/joo/publications
IF YOU LIKE MY ARTICLES AND DEVELOPMENTS IN THE FIELD OF OPTIMIZATION, YOU CAN SUPPORT THE AUTHOR AND BUY OR RENT A POWERFUL LIBRARY OF THE OPTIMIZATION ALGORITHM:
https://www.mql5.com/en/market/product/92455
https://www.mql5.com/en/market/product/93703
or any other of my products:
https://www.mql5.com/en/users/joo/seller
To make an order for MT4 and MT5 through freelancing : https://www.mql5.com/en/job/new?prefered=joo
I make connections to exchanges, there are ready-made connectors.
Recommended Brokers:
https://rbfxdirect.com/ru/lk/?a=dnhp
https://www.icmarkets.com/ru/?camp=4941
1. Increased the speed of the library.
2. The scheme of checking for duplicates has been improved.
https://www.mql5.com/ru/market/product/92455
E. coli bacterium foraging strategy inspired scientists to create the BFO optimization algorithm. The algorithm contains original ideas and promising approaches to optimization and is worthy of further study.
https://www.mql5.com/ru/market/product/92455
The amazing ability of weeds to survive in a wide variety of conditions has become the idea for a powerful optimization algorithm. IWO is one of the best algorithms among the previously reviewed ones.
AO Core is the core of the optimization algorithm, it is a library built on the author's HMA (hybrid metaheuristic algorithm) algorithm. An example of using AO Core is described in the article: https://www.mql5.com/ru/articles/14183 https://www.mql5.com/en/blogs/post/756510 This hybrid algorithm is based on a genetic algorithm and contains the best qualities and properties of population algorithms. High-speed calculation in HMA guarantees unsurpassed accuracy and high search capabilities
In this article, I will consider the Bat Algorithm (BA), which shows good convergence on smooth functions.
In this article, I will consider the Firefly Algorithm (FA) optimization method. Thanks to the modification, the algorithm has turned from an outsider into a real rating table leader.
Fish School Search (FSS) is a new optimization algorithm inspired by the behavior of fish in a school, most of which (up to 80%) swim in an organized community of relatives. It has been proven that fish aggregations play an important role in the efficiency of foraging and protection from predators.
The next algorithm I will consider is cuckoo search optimization using Levy flights. This is one of the latest optimization algorithms and a new leader in the leaderboard.
Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.
In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.
This time I will analyze the Ant Colony optimization algorithm. The algorithm is very interesting and complex. In the article, I make an attempt to create a new type of ACO.
In this article, I will consider the popular Particle Swarm Optimization (PSO) algorithm. Previously, we discussed such important characteristics of optimization algorithms as convergence, convergence rate, stability, scalability, as well as developed a test stand and considered the simplest RNG algorithm.
A professional tool for trading - the divergence indicator between the OsMA and the price, which allows you to receive a signal about a trend reversal in a timely manner or catch price pullback movements (depending on the settings). The indicator settings allow you to adjust the strength of the divergence due to the angle of the OsMA peaks and the percentage change in price, which makes it possible to fine-tune the signal strength. The indicator code is optimized and is tested very quickly as
A professional tool for trading - the divergence indicator between the MACD and the price, which allows you to receive a signal about a trend reversal in a timely manner or catch price pullback movements (depending on the settings). The indicator settings allow you to adjust the strength of the divergence due to the angle of the MACD peaks and the percentage change in price, which makes it possible to fine-tune the signal strength. The indicator code is optimized and is tested very quickly as
A professional tool for trading - the divergence indicator between the WPR and the price, which allows you to receive a signal about a trend reversal in a timely manner or catch price pullback movements (depending on the settings). The indicator settings allow you to adjust the strength of the divergence due to the angle of the WPR peaks and the percentage change in price, which makes it possible to fine-tune the signal strength. The indicator code is optimized and is tested very quickly as
A unique professional tool is a divergence/convergence indicator between MACD and price, which allows you to receive a signal about a trend reversal in a timely manner or catch price pullback movements (depending on the settings). It is indispensable for those who trade divergences manually or with an Expert Advisor. The indicator settings allow you to adjust the strength of the divergence/convergence due to the angle of the MACD peaks and the percentage change in
A professional tool for trading - the divergence indicator between the RVI and the price, which allows you to receive a signal about a trend reversal in a timely manner or catch price pullback movements (depending on the settings). The indicator settings allow you to adjust the strength of the divergence due to the angle of the RVI peaks and the percentage change in price, which makes it possible to fine-tune the signal strength. The indicator code is optimized and is tested very
A professional tool for trading - the divergence indicator between the Stochastic and the price, which allows you to receive a signal about a trend reversal in a timely manner or catch price pullback movements (depending on the settings). The indicator settings allow you to adjust the strength of the divergence due to the angle of the Stochastic peaks and the percentage change in price, which makes it possible to fine-tune the signal strength. The indicator code is optimized and is