Blog
Discover the latest product updates, resources, tutorials and insights for quantitative investment practitioners right here.
Snowflake Summit 2023: New Features for Financial Analytics
As a proud partner of Snowflake, we at Scientific Financial Systems (SFS) were pleased to attend the 2023 Snowflake Summit from June 26- 29 in Las Vegas. At Summit, we joined other data professionals, investment management practitioners, and technology enthusiasts to...
Maximizing Coding Efficiency with AI: A Week in Review
Introduction In the ever-evolving landscape of software development, artificial intelligence (AI) is increasingly playing a pivotal role. Over the past week, we've leveraged AI to enhance the coding process, providing invaluable assistance in tackling complex...
Unlocking the Power of Snowpark for Python: Accelerating Financial Analytics
On Wednesday, May 31, 2023, at 12:00 PM ET, Scientific Financial Systems founder Pete Millington will be co-hosting a one-hour Webinar on the benefits of leveraging Snowflake’s Snowpark for Python to empower Financial Analysts, with SFS development partners...
Machine Learning in Quant Investing – 01: Overview
So much has been written lately about the integration of Quantitative Investing and Machine Learning. Are you wondering about whether to apply ML to your investment process or feel like your team isn’t progressing fast enough? We hope that our new blog series on...
Innovative Insights: CQA Spring Conference 2023 Takeaways
Takeaways from the CQA Spring Conference 2023 by Peter Millington, Founder and CTO of Scientific Financial Systems (SFS) Peter Millington, founder, and CTO of SFS, recently attended the 2023 CQA Spring Conference in Las Vegas. This annual event brings together top...
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...
Discover the future of financial data analysis
Watch a demonstration of Quotient™, our flagship financial data analysis product.