With BMLL’s analytics, exchanges and data vendors can now quickly add complementary data products to their own data marketplace powered by TickSmith and provide even more value to their customers. LONDON (PRWEB) May 27, 2021
TickSmith, creators of the Enterprise Data Web Store and BMLL, the leading, independent provider of harmonised, Level 3 historical data and analytics, announced a collaboration aimed at helping data producers such as stock exchanges, trading venues and data owners generate new revenue streams by creating and distributing analytics derived from their data.
TickSmith’s technology enables organizations to set up a completely customizable data web store where end-users can browse, access and purchase data they need, providing a proven, secure, end-to-end solution that removes the complexities of selling and buying data.
BMLL allows data producers to apply complex statistical techniques to these big-data sets, creating derived data with valuable insights into market impact, pre & post-trade analytics, order book simulation, compliance and risk. These derived data sets can be made available side-by-side with other data the producers want to monetize.
“While the pandemic has really accelerated the demand for data, with our enterprise data store it has never been so easy to diversify and expand your data offering.” says TickSmith CEO, Francis Wenzel. He continues, “With BMLL’s analytics, exchanges and data vendors can now quickly add complementary data products to their own data marketplace powered by TickSmith and provide even more value to their customers.”
BMLL CEO, Paul Humphrey, says, “When applied to market data, our analytics provide insights that are extremely valuable to trading participants, from compliance to analysts and traders.” He continued, “Hedge funds, quants and execution traders on both the buy and sell side already use BMLL’s analytics to backtest and optimize their trading strategies and find alternative sources of alpha generation. BMLL’s platform and tools can now be used to enhance data on TickSmith’s platform.”
About TickSmith
TickSmith, with its Enterprise Data Web Store, simplifies the online data shopping and distribution experience for data buyers and provides the necessary tools for data producers to connect, package, unify and monetize their data. Currently enabling the world’s leading exchanges and financial institutions to generate new revenue streams and increase their customer base, the Enterprise Data Web Store provides a proven, secure, end-to-end solution that removes the complexities of selling and buying data.
By leveraging modern distribution channels, data producers can attract data buyers eager to consume new sources of data on a daily basis. Deployed in a Single-Tenant SaaS model in as fast as 2 weeks, TickSmithʼs out-of-the-box Enterprise Data Web Store is built on a scalable modular platform.
About BMLL Technologies
BMLL Technologies is the leading, independent provider of harmonised, Level 3 historical data and analytics to the world’s most sophisticated capital market participants.
BMLL offers banks, brokers, asset managers, hedge funds and global exchange groups immediate and flexible access to the most granular Level 3, T+1 order book data and advanced analytics, enabling them to accelerate research, optimise trading strategies and generate alpha at unparalleled speed and scale.
Founded in 2014 in the machine learning laboratories of the University of Cambridge, the platform enables researchers and quants across global financial services firms to apply complex statistical techniques to BMLL’s unique big-data sets with applications such as market impact, pre & post trade analytics, order book simulation and compliance. Users no longer need to buy, curate and harmonise data. With BMLL, they gain cost-effective, instant access to a cloud-native Data Science environment via a single web portal, with a long history of the most granular, full order book data across global equities, futures and ETFs for back-testing and simulation, delivered directly into their workflows.
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