Blog
Discover the latest product updates, resources, tutorials and insights for quantitative investment practitioners right here.
ChatGPT’s SQL Translation: Pros, Cons & Room for Improvement
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...
SFS Proudly Joins CFA Society of Boston’s Annual Market Dinner
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...
Peter Millington Attends the Snowflake Data for Breakfast
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...
Why Should We Consider the Use of Machine Learning in Quantitative Finance?
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...
It All Started with a Quble
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...
Houston, We Had a Problem
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...
Differentiate Your Next Investment Study
Get Started with Quotient’s Screener Builder Module Tutorial It is a sound practice to think about which general stock universe you are interested in before bringing in excess data and potential noise to your stock selection process or study. You might unintentionally...
SFS Founder discusses Quantitative Analysis on the LifeBlood Podcast
Pete Millington, CFA, founder, and chief technology officer of Scientific Financial Systems, recently appeared on the LifeBlood Podcast to discuss quantitative analysis, how to curate the right data for investment management, how quantitative and qualitative analysis...
SFS AND Refinitiv: Spend more time on research and developing models with read-to-use data and Python-based factor construction. No SQL coding required
SFS is excited to announce that it has partnered with Refinitiv, the global provider of financial market data and infrastructure. The integration of SFS’ flagship product Quotient™ with Refinitiv Quantitative Analytics (RQA) reduces data management time with seamless...
Market Conversations: Analytic Tools in Finance
Introduction to Quotient™ Quble: A Financial Analysis Tool
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...
Point In Time Data Sets
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...
Discover the future of financial data analysis
Watch a demonstration of Quotient™, our flagship financial data analysis product.