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Ridvan Sözen is the Co-Founder and Head of Quant Trading at Aizona Tech, a fintech studio that builds ultra-low-latency execution infrastructure for digital-asset markets. Before launching Aizona, he served as a Quantitative Researcher at Finrix, where he prototyped factor-driven equity portfolios for institutional clients. He later joined Kuveyt Türk Treasury as a spot-FX quant trader, designing high-frequency trading pipelines for arbitrage and liquidity management. Ridvan earned a B.A. in Economics from Boğaziçi University (2020) and applies this economic foundation alongside practical C++ / Python development in his day-to-day work. At Aizona he leads a cross-functional team that delivers microsecond-level order-execution engines, depth-imbalance market-making bots, and cross-exchange hedge frameworks connecting to more than 20 trading venues. His research centers on quantitative market-making and latency arbitrage, leveraging order-book microstructure modeling, high-frequency data analytics, and predictive liquidity signals to capture edge before it dissipates.
Ridvan Sözen is the Co-Founder and Head of Quant Trading at Aizona Tech, a fintech studio that builds ultra-low-latency execution infrastructure for digital-asset markets. Before launching Aizona, he served as a Quantitative Researcher at Finrix, where he prototyped factor-driven equity portfolios for institutional clients. He later joined Kuveyt Türk Treasury as a spot-FX quant trader, designing high-frequency trading pipelines for arbitrage and liquidity management. Ridvan earned a B.A. in Economics from Boğaziçi University (2020) and applies this economic foundation alongside practical C++ / Python development in his day-to-day work. At Aizona he leads a cross-functional team that delivers microsecond-level order-execution engines, depth-imbalance market-making bots, and cross-exchange hedge frameworks connecting to more than 20 trading venues. His research centers on quantitative market-making and latency arbitrage, leveraging order-book microstructure modeling, high-frequency data analytics, and predictive liquidity signals to capture edge before it dissipates.
