We are Tomtit
Capital.

Tomtit Capital was founded in 2025 by Mark Synytsia with a focused conviction: that the dominant frameworks in wealth management and institutional trading were structurally losing their edge in markets increasingly shaped by algorithmic participation, machine learning, and real-time information flows.

What began as a research initiative became a firm. The founding team — researchers, engineers, and mathematicians who share an intellectual restlessness about markets — came together around a single operating principle: every strategy must be explainable from first principles before it is traded.

We are not a large organisation. We are deliberate about staying small. Every person here shapes the firm's direction, culture, and research agenda. We have no interest in scale for its own sake.

Tomtit Capital exists at the intersection of quantitative science, machine learning, and market structure research. We are drawn to problems that resist easy answers — the kind that require deep statistical thinking, computational creativity, and genuine intellectual honesty.

We believe that markets are information-processing systems, and that our collective job is to extract signal from noise with greater precision and rigour than anyone else in our space. That means building every model from scratch, testing every assumption out-of-sample, and retiring strategies without sentiment when the evidence says the edge has gone.

It also means building a firm that reflects those standards internally — in how we hire, how we communicate, how we make decisions, and how we measure success.

Mark Synytsia founded Tomtit Capital at 18. He is the firm's Chief Investment Officer and the architect of its quantitative research framework. His work centres on the application of machine learning to systematic trading, with a particular focus on market microstructure and regime detection.

Mark's founding premise was straightforward: that the era of discretionary alpha was narrowing, and that the next generation of trading firms would be built by those who understood both mathematics and markets at a deep, mechanistic level — not those who simply deployed existing strategies at scale.