CloudQuant teams up with RavenPack to expand the use of alternative data.
CloudQuant LLC added RavenPack analytics within their trading strategy incubator. Crowd researchers can now use RavenPack historical data to discover tradable alpha signals on CloudQuant’s online Python and JupyterLab-based tools.
RavenPack is the leading provider of big data analytics for the financial services industry. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large analyzing large amounts of unstructured content. The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows.
CloudQuant is the cloud-based trading strategy incubator. Quantitative analysts around the world create and test trading strategies leveraging free institutional grade technology. By providing the capital, technology, and trading acumen to develop and utilize trading strategies, CloudQuant offers a mutually beneficial profit sharing agreement enabling both parties to profit.
Use of historical data
Crowd-based research tools are increasing in popularity along with the rapidly growing data science field. RavenPack and Cloudquant are finding that crowd researchers desire access to Wall Street professional-quality tools and datasets, which enable them to thrive in the professional investment field.
Morgan Slade, CEO of CloudQuant said that they are thrilled to include RavenPack analytics in their ecosystem as they have become a vital source of alpha for quantitative investors. Their community is already finding promising signals that originate from the very popular RavenPack datasets.
“We were impressed with how CloudQuant provides anyone with Python-coding skills the opportunity to mine our datasets for alpha signals and earn compensation for their contributions,” said Amando Gonzalez, CEO of RavenPack. “We strongly support initiatives designed to give data scientists the tools that liberate ideas to improve financial modeling.”
