We provide expert services in algorithmic trading systems development.

We specialize in StrategyQuant, the top software for creating trading algorithms.


About Me

I am Ivan, a Machine Learning Engineer and Algorithmic Trading Consultant with over 14 years of experience in quantitative finance. My passion lies in developing AI-driven trading strategies, building automated decision-making systems, and optimizing financial models for better performance. With a deep background in Python, Java, MQL4/5, and machine learning frameworks like scikit-learn and XGBoost, I specialize in creating robust and adaptive trading solutions. Throughout my career, I have worked with traders, hedge funds, and fintech companies to turn data into actionable insights and profitable strategies. At AlgoTrading.Space, I bring together my expertise in algorithmic trading and machine learning to help clients navigate the complexities of financial markets with confidence.

From Our Blog

Featured
Divergence Comparison Blocks – How to Trade Divergences with StrategyQuant
Divergence Comparison Blocks – How to Trade Divergences with StrategyQuant

In this article, we introduce our new divergence comparison blocks—a tool designed specifically to identify and analyze divergences. These blocks aim to simplify the detection of potential trend reversals, helping traders recognize weakening momentum or emerging changes. In addition to a discussion on divergence theory and practical implementation tips, our main focus is on presenting this solution. Our divergence comparison blocks offer a clear and efficient method to analyze market data, ultimately streamlining decision-making for traders and technical analysts.

IsGreater/IsLower Adaptive Block
IsGreater/IsLower Adaptive Block

The IsGreater Is/Lower Adaptive comparison block does one main thing: it checks if one indicator is greater than another, but only generates a signal if similar situations were profitable in the past.

Analysis of the Effectiveness of Adaptive Blocks
Analysis of the Effectiveness of Adaptive Blocks

The objective of this research is to verify whether generating strategies using adaptive blocks yields better results compared to traditional, non-adaptive blocks. As part of the experiment, we designed a method that allows us to compare thousands of strategies and perform statistical analysis on their performance.

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