RavenPack is a leading provider of big data analytics for financial services that enables hedge funds, banks and asset managers to query and visualize unstructured data including insights from thousands of news and social media sources.
“We are thrilled to include RavenPack analytics in our ecosystem as they have become a vital source of alpha for quantitative investors,” said Morgan Slade, CEO of CloudQuant. “Our community is already finding promising signals that originate from the very popular RavenPack datasets.”
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.
“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.”
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. CloudQuant LLC, who officially launched in 2017, is a wholly owned subsidiary of Kershner Trading Group LLC.
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.
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