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Trading since 2016, algorithmic trading since 2019, machine learning and programming since 2021.
I develop expert advisors, trading robots, indicators, smart contracts, cryptocurrency token and coin codebases, business automation software, and turnkey AI models.
Currently working on an institutional-grade trading system for my own hedge fund and on my own AI blockchain.
Author of 100+ international articles published in different languages worldwide.
Небольшой процентник капает на счет каждую ночь, это своего рода кэшбек от брокера за активную торговлю роботов.
Каждому кто приобретает акционные версии роботов - я могу настроить такого рода ребейт с прямым переводом ребейта на счет каждую ночь.
По процентам чисто с ребейтов за апрель вышло + 0,75%, плюс еще роботы сами набили +12,52% на все пополнения.
Принцип прост - постоянно пополняем счет, роботы постоянно набивают прибыль на все пополнения, ребейты также увеличиваются. Дальше в систему вступает его величество сложный процент, который и выводит вас на финансовую свободу. Наш с женой пассивный доход от инвестиций за год уже впервые превысил 1 млн. тенге, это около 20 000 рублей полностью пассивно - ежемесячно. Но прибылью мы не пользуемся, а реинвестируем и пускаем в работу - хоть через 10 лет пожить как миллиардеры))))
Всего накопительных счетов сейчас 11 - это и вклады, и депозиты, и брокерские счета в РФ / Казахстане, и криптобиржи, и брокерские счета у Форекс - дилеров.
Главная суть системы: контролировать расходы, чтобы тратить не все, то что не потратили, запускаем в инвестиции, и они уже создают нам капитал на дистанции.
In this article, we will explore what pair trading is and how correlation trading works. We will also create an EA for automating pair trading and add the ability to automatically optimize this trading algorithm based on historical data. In addition, as part of the project, we will learn how to calculate the differences between two pairs using the z-score.
What is angular analysis of financial markets? How to use price action angles and machine learning to make accurate forecasts with 67% accuracy? How to combine a regression and classification model with angular features and obtain a working algorithm? What does Gann have to do with it? Why are price movement angles a good indicator for machine learning?
We determine the overbought and oversold condition of the market according to chaos theory: integrating the principles of chaos theory, fractal geometry and neural networks to forecast financial markets. The study demonstrates the use of the Lyapunov exponent as a measure of market randomness and the dynamic adaptation of trading signals. The methodology includes an algorithm for generating fractal noise, hyperbolic tangent activation, and moment optimization.
We revisit the Ilan grid Expert Advisor and integrate Q-learning in MQL5 to build an adaptive version for MetaTrader 5. The article shows how to define state features, discretize them for a Q-table, select actions with ε-greedy, and shape rewards for averaging and exits. You will implement saving/loading the Q-table, tune learning parameters, and test on EURUSD/AUDUSD in the Strategy Tester to evaluate stability and drawdown risks.
Do you want to know how to benefit from the difference in interest rates? This article considers how to use swap arbitrage in Forex to earn stable profit every night, creating a portfolio that is resistant to market fluctuations.
We are creating an adaptive self-learning trading expert advisor based on DQN machine learning, with multidimensional causal inference. The EA will successfully trade simultaneously on 7 currency pairs. And agents of different pairs will exchange information with each other.
In this article, we will examine the movements of synthetic currencies using Python and MQL5 and explore how feasible Forex arbitrage is today. We will also consider ready-made Python code for analyzing synthetic currencies and share more details on what synthetic currencies are in Forex.
Today we will take a look at my first arbitrage robot — a liquidity provider (if you can call it that) for synthetic assets. Currently, this bot is successfully operating as a module in a large machine learning system, but I pulled up an old Forex arbitrage robot from the cloud, so let's take a look at it and think about what we can do with it today.
В отличие от остальных моих алгоритмов, не требует обучения и оптимизации, обучается на лету и за пару дней выходит в прибыль. Постоянно дообучается на лету. Выходит в прибыль с любой точки графика на любой паре.
Осталось совместить это с арбитражным Сварогом и поставкой данных из Мидаса, и с удаленным риск менеджером. Но эта часть системы самодостаточна.
This article presents the development of an arbitrage analysis panel in MQL5. How to get fair exchange rates on Forex in different ways? Create an indicator to obtain deviations of market prices from fair exchange rates, as well as to assess the benefits of arbitrage ways of exchanging one currency for another (as in triangular arbitrage).
We are making a remote professional risk manager for Forex in Python, deploying it on the server step by step. In the course of the article, we will understand how to programmatically manage Forex risks, and how not to waste a Forex deposit any more.
We are going to develop a professional indicator for currency strength analysis in MQL5. This step-by-step guide will show you how to develop a powerful trading tool with a visual dashboard for MetaTrader 5. You will learn how to calculate the strength of currency pairs across multiple timeframes (H1, H4, D1), implement dynamic data updates, and create a user-friendly interface.