At Scientific Financial Systems, we have mapped a lot of vendor data into Quotient – mostly from MS SQL Server databases. With the ever-growing popularity of Snowflake, vendors are releasing versions of their data products on this highly scalable, performant, and...
Scientific Financial Systems was proud to attend the CFA Society of Boston’s Annual Market Dinner event in Boston last night. It was a great turnout from the Boston investment community. Morgan Housel delivered an excellent keynote talk about market risk and investor...
In today’s fast-paced business environment, data is more valuable than ever. Companies are looking for ways to extract meaningful insights from their data to gain a competitive edge. To do so, they need a robust data warehousing solution that can support their data...
During our time in Quant Finance, regression analysis was generally the best tool we had for determining the effectiveness of factors and models. We at SFS, we were especially comfortable performing regressions when the relationship between our variables was clearly...
In our last SFS blog, we talked about how the technical challenges in the performance and scaling of our Quotient architecture were solved through the integration of our backend with Snowflake’s Snowpark for Python. We called Snowpark a “game changer” for developers...
Scientific Financial Systems (SFS) knew that in Quotient™ we developed a powerful and flexible Python-based data science SaaS application that could improve the effectiveness of quant finance teams. Our careers in Quant research and fund management led us to...
Get Started with Quotient’s Screener Builder Module Tutorial
CFA Pete Millington in conversation with George Grombacher
SFS is excited to announce that it has partnered with Refinitiv, the global provider of financial market data and infrastructure.
Featuring Asriel Levin of Finsera and Pete Millington, founder of Scientific Financial Systems.
The technological world is fast evolving, that's why financial analysts need knowledge in Python, SQL, and Quble. Data records form the backbone of all financial analysis. Quble takes financial modeling to the next level by using a revolutionary approach to data...
Advantage Point in time financial data sets have been built so that users can access historical financial statements including a date of when the financial statements were known. Non-point in time data sets do not include a date of when the information became...
Watch a demonstration of Quotient™, our flagship financial data analysis product.