Complex Analytics for Financial Services
Long before “Big Data” became one linguist’s word of the year for 2012, daily trading operations generated massive volumes of data. The Financial Services industry was one of the earliest communities to leverage scientific computing software and the quantitative skills of scientists for applications such as algorithmic trading and risk analysis.
SciDB, an advanced computational database, brings new levels of scalability and functionality, along with a more cost-effective solution for high performance computing, to the financial services industry.
Scale up Complex Analytics
|Run complex math functions like covariance, regressions, PCA, SVD on matrices too large to fit in memory on a single node.|
|Built-in support for time-series data without expensive or custom databases.|
Support for Audits and Reproducibility
|SciDB’s no-overwrite data management is critical for reproducing results or rerunning simulations with the exact data used at the time. Keep all the original data and updated data needed to support audits.|
Missing Data Reason Codes
|Paradigm4 supports multiple, custom-defined null values (such as missing datapoint, market closed, trading halted, or feed unavailable) so that applications can substitute context-sensitive values.|
Analyze More, Program LessA Webinar about Using SciDB for Computational Finance. Read more.
About Paradigm4Paradigm4 is the company behind the open source SciDB project. We develop it, support it, build enterprise extensions, and provide hands-on expertise.