Data has a better idea

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We transform asset-forecasting into a shopping experience.

Imagine being on Amazon but instead of shopping for books you are shopping for alternative data, machine - & deep - learning models. Preview data sources, their statistics, as well as their user opinions. In the next step you add it to your cart - if you think they are useful. Before checkout, you can build tailor made forecasting models based on the underlying data easily. Our competitors provide you merely with data analytics. We don’t - we help you to gain additional information out of the vast amount of data sources. In addition to that, we help our non-professional users by making the process of creating their first forecasting models as easy as possible.

What we do

We deliver data driven decisions to everyone.

Data has a better idea. We create a central data marketplace for financial alternative data as a baseline for investment decisions. There is a vast amount of data distributed all over the world. We gather this data and make it easily accessible to you. Additionally, we provide you with an automated machine-learning engine in order to find your best suitable models for your selected data. If you still think that this is too much work for you, the following will relieve you: Our automated machine-learning engine is also able to select the best data for your target asset to maximize your profit and minimize your risk.

Who we are

We have a passion for machine & deep learning in financial market analysis.

Cortecs is an academic spin-off of the University of Vienna. We are one of the winners of the Spin-off Fellowship program and are funded by the Austrian Research Promotion Agency. Cortecs is supported by the Rocket Science startup academy and the research group Theory and Applications of Algorithms of the University. Our team consists of computer scientists, data scientists and high-ranking financial experts.


Universität Wien
Bundesministerium Bildung, Wissenschaft und Forschung
Wissenstransfer Ost


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Währinger Straße 29
1090 Vienna
University of Vienna
Faculty of Computer Science