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Bloomberg launches BQuant Enterprise

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Bloomberg has launched BQuant Enterprise, a public/private-cloud-based analytics platform (the BQuant Enterprise Platform) for quantitative analysts and data scientists in financial markets. 

This customisable, turnkey solution accelerates financial services firms’ ability to compete more aggressively by incorporating quantitative approaches to all aspects of their investment processes. In addition, BQuant is the first data science solution from Bloomberg that is designed specifically for financial markets and offers operation-ready access to Bloomberg’s comprehensive range of high-quality, market leading, multi-asset-class financial and alternative data sets.
“The largest global financial firms have fully embraced quantitative investing to improve trading strategies, while reducing expenses. However, the high cost of entry has put the majority of firms at a disadvantage. BQuant Enterprise levels the playing field with a high-performance platform that can operate as your core platform or can be integrated in days with firms’ existing data science research environments,” says Tony McManus, Global Head of Enterprise Data at Bloomberg.
The financial industry’s leading firms have invested heavily in building systems to support quantitative analysts, who use mathematical models, voluminous data sets, and computational power to evaluate investment strategies and generate new ideas. With BQuant Enterprise, front-office teams can quickly reap the benefits of these sophisticated data science capabilities, while IT departments can maximize their technology investments by integrating the solution with their existing infrastructures, databases and workflows.
BQuant Enterprise offers analysts more efficient workflows for creating, validating, and putting their models in production for decision making. The platform comprises finance-specific tools, services, and libraries to support a broad range of quantitative analytics, including factor model evaluation, backtesting strategies, and analysing portfolios, across asset classes. Features include:

Ready-to-Use Data Sets: Access to Bloomberg’s comprehensive range of high-quality, market leading, multi-asset-class financial and alternative linked data sets, in addition to capabilities for using their own internal data

Python-Based Environment: An interactive platform rooted in Python and Jupyter notebooks — the industry-preferred choice among quantitative analysts and data scientists focused on financial markets — that uses Python’s open-source scientific computing ecosystem and empowers advanced users to build their own applications.

Quantitative Workflows: API-first analytics and innovative, domain-specific tools that work together to provide fully customisable, end-to-end workflows, from initial data exploration to backtesting, optimisation and visualisation.

Research Distribution: Effortless sharing of analysis results across the organisation — from quantitative analysts to portfolio managers and other consumers — with capabilities to have the data automatically refresh within the applications.

Enterprise Administration Capabilities and Security: Allows firm administrators to manage environments, code repositories, users and roles.

Cloud-Ready: Compatible with the most popular public clouds, as well as on-premises private and hybrid cloud environments.

Support: Backed by Bloomberg’s exemplary customer support, which helps firms customise their BQuant Enterprise installation.
“Thornburg was early to recognsze the advantages that programmatic workflows could bring to portfolio construction,” says Igor R Kuznetsov, PhD, Portfolio Analytics Manager at Thornburg Investment Management, a USD49 billion independent global investment management firm that provides a range of active investment strategies. “We built a web application to fill this need, but the data was static, and the app could not ingest our highly valued Bloomberg data sources. After selecting BQuant Enterprise, we were able to bypass those limitations. My team could immediately access a wide range of normalized data; spend less time manipulating it; apply additional backtesting capabilities; and generate more ideas faster than before.”
BQuant Enterprise lowers the total cost of ownership for firms to implement a platform for quantitative analytics. Its design offers system interoperability, data portability, and support for open standards, thereby enabling quick and easy integration with customers’ existing infrastructures, databases, and workflows. In addition, the BQuant Enterprise Platform gives IT teams a high level of visibility into user activities so they can add oversight to any BQuant processes that become mission-critical to the firm. Bloomberg’s support team helps IT departments and financial analysts adapt BQuant Enterprise to their unique infrastructure and capability needs, resulting in a fast time to market, a high return on investment, and unprecedented scalability. And firms can rely on Bloomberg’s renowned customer service for on-going support and maintenance.
“In building BQuant Enterprise, our goal was to develop an open architecture based on a powerful tech stack that is readily accessible and infinitely extensible, so it can grow as our customers grow,” says Shawn Edwards, Bloomberg’s Chief Technology Officer. “Utilising the power of the cloud, we’re giving clients a turnkey environment where they can connect to their existing systems, bring their own data, mix it with Bloomberg’s comprehensive data sets, and enhance the collaboration of their investment professionals as they test and deploy new quantitative investment strategies.”

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