IT’S A PLATFORM THAT GIVES YOU AN EDGE
Librade was conceived and developed by quants.
We believe that a performant and reliable technology as well as an excellent UI are an important foundation of the Librade system.
The main focus has been derived from experiences of quants. While working on winning algorithms, inefficiencies and imprecisions of available platforms became a continuous limiting factor in their striving for excellent strategy. These experiences made us push and extend the envelope of Librade.
Now, it’s time to fuel Librade with high quality market data and release it.
Does it sound interesting? Contact us and ask for a demo and beta access to Librade platform.
Our focus and USPs
Most likely, your organization is not in the business of running data centers, yet a significant amount of time and money is spent doing just that. Librade is reliable and fast. The user interface is modern and easy to use.
Librade provides a way to acquire and use resources on-demand, so that you pay only for what you consume. You no longer have to purchase based on complex forecasts or projections, risking under provisioning or having too much excess idle capacity.
Librade platform simulator is providing high quality tick data and delivers high precision results. Our smart job scheduler is based on evolutionary algorithm and can find right results through thousands of simulations for you.
VARIOUS ALGORITHM EXECUTORS
Librade offers a pay-as-you-go approach for pricing for all services. Librade pricing is similar to how you pay for utilities like water or electricity. You only pay for the services you consume, and once you stop using them, there are no additional costs or termination fees.
Dive down to our thoughts, insights...
This paper uses the novel quantile coherency approach to examine the tail dependence network of 49 international stock markets in the frequency domain. We find that geographical proximity and state of market development are important factors in stock markets networks.
In this study, we construct financial networks in which nodes are represented by assets and where edges are based on long-run correlations. We construct four networks (complete graph, a minimum spanning tree, a planar maximally filtered graph, and a threshold significance graph) and use three centrality measures (betweenness, eigenvalue centrality, and the expected force).