Data Science & Research

data science & Research

Data scientists and researchers improve our trading strategies

How does Optiver continuously price hundreds of thousands of financial products in real-time? One word: Data.

Data science and research are at the very core of Optiver’s pricing strategies. Our teams tackle challenges at the intersection of technology and trading – where some of the most complex and valuable financial market data resides. With world-class skills that cut through the noise, Optiver’s data science and research teams directly contribute to the precision and scalability of our trading business.

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Data analytics

It all starts with the exchanges, where we monitor a constant stream of trades and prices. All of this information must be collected, processed and relayed to both human traders and automated strategies alike.

These data sets present their own complexities, including a typically very low signal-to-noise ratio. Our analysts must combine data science with a solid understanding of data lineage and trading dynamics to cut through the noise. In this way, we can continuously improve trading logics and remain performant even during periods of market turbulence.

Data science

Data scientists at Optiver apply their advanced engineering and machine learning skills to develop and improve our trading strategies. They sit on our trading floor – the beating heart of our business – to share market insights with traders, quantitative researchers and developers. Our data scientists’ observations and recommendations inform crucial trading decisions in real-time, making them trusted and highly-valued colleagues.

Optiver’s data scientists collaborate with data analysts, data engineers, quantitative researchers and traders. It’s a fascinating role that requires a strong coding background and problem-solving skills.

Mathematical modelling

Optiver’s researchers develop sophisticated models that take market dynamics into account to consistently price tens of thousands of instruments. These models must be flexible enough to account for the nuances of the market while at the same time remaining sound and robust. By working together with traders and software engineers, our researchers strive to continuously unlock solutions that further strengthen our pricing.

Machine learning

Machine learning is heavily utilised at Optiver to predict how markets might perform in the future. Based on these implied findings, data scientists convert immense amounts of historical data into trading signals. The goal is improving our prediction accuracy. To do so, Optiver is constantly looking for innovative ways to derive useful insights from the data.

Infrastructure & pipeline

Optiver’s data science activities wouldn’t exist without our state-of-the-art data infrastructure. Our growing team of data engineers is dedicated to building a cutting-edge, big data pipeline to ensure the real-time availability of terabytes of market data. Each year our Amsterdam office alone processes market data in the magnitude of petabytes. With the help of highly skilled data analysts, who combine data processing skills with a thorough understanding of strategies, we make sure the data tells a story.

Quantitative research

Quantitative research is the foundation upon which Optiver’s trading activities are built. Our research teams – experts with MSc and PhD degrees in a variety of STEM subjects – utilise a scientific approach to research and design our world-class trading algorithms. This means applying and developing state-of-the-art stochastic models to price options and predict market volatility, as well as utilising Monte Carlo methods to determine how to perform differentiation on discrete realisations of stochastic processes. Our researchers develop statistical arbitrage strategies by working with petabytes of low latency, high-frequency market data sets, an extensive high-powered computing back-testing framework and much more.

Optiver’s researchers, steeped in academic methods, invite their teammates and traders to challenge each hypothesis. Can it be mathematically improved? Does it pose a potential risk? Does it exploit a market behavioural pattern? Can it sustain its output goals? Constant testing, analysis, refinement and innovation ensure our quantitative models remain at the cutting-edge of capital markets.

Ashley – Amsterdam

“The industry itself is ever-evolving and becoming a bigger part of modern society’s development. With every new innovation in the financial market landscape, new data science challenges present themselves as opportunities for people in the industry to make things better.”

trading application engineer

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