INVESTANT

Algorithmic Sports Trading

Modelling and trading the world of sports match by match with a pure data-driven approach.

COMPETENCE

AREAS

ALGORITHMIC MODELLING

 

Our models are taking every possible input factor into account, from available offers, league of the match, historical results, etc.

MARKET MAKING

 

Due to our broad approach and large bankroll we're supplying the markets with liquidity.

MACHINE LEARNING

 

With thousands of matches being played every day, no human can keep up with processing the vast amounts of data.

BACKTESTING

 

Over 10 years of detailed historical data is available to our models to identify lasting edges in the market.

RISK MANAGEMENT

 

It's not done with identifying a trade itself. Complex financial risk models continuously adjust the expected value of our portfolio.

EXECUTION

 

No trading without millisecond execution of orders. Our unbeatable speed of execution puts every model ahead of the competition.

 

OUR VISION

We believe that sports trading will become a new and unrelated asset class in financial trading and we're building the cornerstone to a more data-driven and efficient world for the sake of everyone.

Trading is hard and requires top-notch technology and brains. As we put years into developing self-learning algorithms we leverage every piece of information we can get our hands on and improve day by day. Our technological head-start is and will be a crucial factor in this strongly growing market.

Already, we're unique in our approach as no single trade is touched by a trader from input stream to execution. All we do is enable the models and execution bots doing their job. This is how we imagine technological disruption should look like in this market.

This will lead to more fair matches, trading experiences, lower operating cost, and higher payouts to every individual.

 

 

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CONTACT

OUR TEAM

OUR ADDRESS

Gaustadalléen 21, 0349 Oslo, Norway

Email: marius@investant.tech

For any general inquiries, please fill in the following contact form:

 

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