About
Ivan Hudec
AI Solutions Architect, data scientist and algorithmic-trading consultant. I've spent nearly two decades turning market intuition into systems that run themselves — from a live futures-and-options desk in 2007 to the agentic AI I build at StrategyQuant today.
Trading, data science, machine learning and agentic AI are usually four different people. The rare part — and the reason clients work with me — is having all four in one.
About me
I'm Ivan Hudec — an AI Solutions Architect, data scientist and algorithmic-trading consultant based in Bratislava. I've spent nearly two decades in the financial markets: I started in 2007 running a proprietary futures-and-options desk, trading across the world's major exchanges, then moved deeper into data science and machine learning, and today I design the agentic AI systems behind StrategyQuant — software used by professional traders, hedge funds and CTAs. I work across Python, Java and MQL4/5, with machine-learning tools like scikit-learn and XGBoost and LLM frameworks like n8n, LangChain and RAG. A graduate of Comenius University in Bratislava, I've kept one thread through all of it: turning how markets actually behave into systems that run themselves.
Experience
Work with meAI Solutions Architect — StrategyQuant · 2024–now
I lead the AI product at StrategyQuant. The core is an agentic system that turns a trader's plain-English description into executable, platform-agnostic strategy logic — ready to backtest and deploy. I designed the architecture: an n8n-orchestrated pipeline of custom LLM agents for intent, logic synthesis, constraint validation and output, and I own the prompt engineering, agent reasoning, and the evaluation and monitoring that keep it reliable in production. It serves professional traders, hedge funds and CTAs. Python · n8n · LangChain · RAG.
StrategyQuant Specialist & ML Developer · 2019–now
I first started building strategies with StrategyQuant back in 2014; from 2019 I have worked on the software itself — Java and MQL4/5 extensions, custom ML models for strategy optimization, and hands-on consulting for traders and quant teams putting machine learning into their workflows. I've published 135 custom indicators and tools to the StrategyQuant Codebase. Java · MQL4/5 · Python · scikit-learn · TradeStation EasyLanguage.
Data Scientist & ML Engineer — Vesnalá Lekáreň · 2022–2024
Data science in production, outside of trading: demand-forecasting models, pricing optimization and decision dashboards for the marketing and supply-chain teams of a retail pharmacy chain. Messy real-world data, and models non-technical teams could actually trust.
Founder — algotrading.space · 2012–now
My independent algorithmic-trading and data-science practice: data pipelines, strategy-evaluation systems and ML models for quant and trading clients — strategy selection, drift detection and portfolio analytics.
Head Trader — Colosseum, a.s. · 2007–2011
Ran the proprietary trading desk at a futures & options firm. Traded live across the world's major exchanges — CME, CBOT, EUREX, TOCOM, ICE and NYSE LIFFE — in futures, options and ETFs, managed client accounts, and built the first quantitative models for strategy optimization. This is where the market intuition was earned.
Let’s work together.
Tell me a little about what you're working on and I'll get back to you personally.