23Beta testers involved
493100Simulations executed
67Algorithms created
301Server instances running
9285Cups of coffee
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.
ON-DEMAND RESOURCES
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.
PRECISE SIMULATIONS
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
Our well-documented API will give you a full power of Librade platform in Java, Python, R and javascript. No matter how advanced and resource hungry your computations are, with Librade you have your own server pools just few clicks away.
PRICING
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.
LATEST POSTS
Dive down to our thoughts, insights...
9 Apr 2019 / By Eduard Baumohl
Quantile coherency networks of international stock markets
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.
11 Jul 2018 / By Eduard Baumohl
Network-based asset allocation strategies
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).