Builder of Risk Intelligence

Radiant Quant

Radiant Quant develops intelligence that enhances the performance of financial research and investment strategies.

By combining data with behavioral insight, we study how risk forms, spreads, and resolves. What emerges is not prediction, but understanding — the ability to see the tension beneath stability and the opportunity within uncertainty.

Identity

Radiant Quant operates as a small, independent research project. We do not manage capital, run funds, or offer discretionary strategies.

Our work focuses on building systematic risk layers that help investors interpret market conditions with greater clarity and discipline.

Belief

Most investment losses do not come from selecting the wrong assets, but from remaining exposed during periods of rising systemic stress.

Risk builds silently through liquidity, volatility , financial conditions and with several data long before it becomes visible in price.

Our work is centered on measuring that tension earlier. As catching storms before it happens

What We Do

Radiant Quant builds systematic risk layers that plug into existing portfolio management and decision workflows, enhancing judgment without replacing it.

Our first public release focuses on Financial Conditions Intelligence (FCI), supporting risk-on, risk-off, and capital preservation decisions.

Founder

Radiant Quant was founded by Uğur Demir.

The work is shaped by a background in market analysis, risk frameworks, and navigation based decision making , translating complex stress signals into practical decision layers.

Team

Radiant Quant is built with the contributions of a small group of developers who have worked closely on the project.

Kürşat Sezgin has played a central role in the development of the platform, contributing extensively across both front-end and back-end systems.

Yağız Aydın has supported the project as a full-stack developer, providing valuable technical insight and development support throughout the build process.

Principles

  • Clarity over complexity
  • Risk first, returns after
  • Systems over opinions
  • Independence over noise