QTPy
A state-of-the-art, user-friendly Python library designed for data scientists and quant experts. The force behind QTPy is its ability to access data in a federated model without having to integrate it into a single data repository, saving your firm crucial time and valuable resources. Featuring rapid application prototyping and database decoupling, QTPy requires no prior programming proficiency outside of Python or R. Its superior scalability streamlines even the largest of data sets, transcending you fast into the future of data analytics.
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Next Gen AnalyticsACT on your data with cutting edge algorithms that drive potent analytics. Increase your alpha and achieve world-class opportunities in investment, trading and client services. -
Non-Intrusive SoftwareQTPy adapts to your existing data environment with ease. Our non-intrusive software can be installed in-house, mapped internally and propels your analytics to achieve peak research potential. -
Data and ID MappingWe work within your current systems and data repositories to eliminate the need for time-consuming data re-engineering. Our team of deep domain experts builds powerful analytics across countless datasets. -
Fully-Hosted SolutionsExtensive data infrastructure, mapping services and complete in-cloud maintenance. 24 hours a day. 7 days a week. Whether it’s entity-specific, owner- affiliated or ESG-related, QTPy ensures alternate datasets are normalized and mapped to a unique identifier. -
Flexible IntegrationQTPy effortlessly integrates in-house repositories and proprietary data with external, in-cloud and large data sets (i.e. tick data). Get the flexibility you need while conforming to the enterprise standards that matter most. -
Gain Deeper InsightsLeverage our partnerships with some of the most reputable market data vendors to incorporate over 100+ cutting edge data sets, including ESG, Sentiment Analytics and more.
Quantitative Research Platform
To streamline a global asset management firm’s siloed quantitative processes and position them for future AUM growth, Kuberre used a Three-Pronged Data Management Approach. The result? A rich, Python-friendly, federated data virtualization platform that’s scalable, secure and ready for next-gen analytics. The benefits? Significant cost savings, elimination of key-person risk, enterprise collaboration, superior security standards and quants who can redirect value to key investment strategies.
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