Over the past year, Scientific Financial Systems has provided Consulting Services to a number of clients. Our experienced team of Quantitative Finance and Technology professionals are helping people like you in three critical areas: Data Integration or Migration...
Over the last 15 years, we’ve all seen Python mature into the language of choice for most quant developers and data scientists, thanks to its simplicity, versatility, and list of powerful third-party libraries. To that list, SFS is adding its brand-new Python library,...
Scientific Financial Systems’ flagship product Quotient™ provides an advanced analytical engine that simplifies data manipulation, factor building, backtesting, portfolio construction, and much more. Under the covers, SFS’s Quble technology powers Quotient’s financial...
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...
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.
