I'm Ivan Hudec, StrategyQuant's AI Solutions Architect. I specialize in StrategyQuant — the top platform for building trading algorithms — and the agentic AI that turns a plain-English idea into a verified strategy.
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 markets: I started in 2007 on a proprietary futures-and-options desk, trading across the world's major exchanges, then moved deeper into data science and machine learning. Today I design the agentic AI behind StrategyQuant — software used by professional traders, hedge funds and CTAs — and I've published 135 free indicators and tools to its Codebase. Trading, data science, machine learning and agentic AI are usually four different people; the rare part is having all four in one.
Track record
I would rather show you the public record than ask you to take my word for it.
135 free tools on the StrategyQuant Codebase. Custom indicators, databank columns and Monte Carlo tests, downloaded and used by traders worldwide — browse the full catalogue.
StrategyQuant's AI Solutions Architect. Indicators I wrote — Vortex, Laguerre RSI, Kaufman and more — ship inside the platform itself.
Interviewed by StrategyQuant about how I build and test strategies. Read the story →
Since 2007 in the markets. From a live futures-and-options desk to the agentic AI I build today — nearly two decades, one thread: systems you can trust with real money.
A Backtest Is a Statistical Test, Not a Proof
Generate 10,000 strategies and the best backtest will look brilliant whether or not any real edge exists. The multiple-testing view of strategy development — and the discipline that follows from it.
July 12, 2026
How I Evaluate AI Agents That Build Trading Strategies
An AI demo is a backtest: it looks brilliant on the cases it was tuned on. Evals are the new backtests — the oracle-gated, out-of-sample discipline I borrowed from quant research to test my own agents.
July 12, 2026
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