Uncategorized

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.

In our last blog, George Kastner detailed how changing business processes or systems can be complex but also can be rewarding.

Change is often difficult and uncomfortable. But it also encourages innovation and leads to better business opportunities

Our flagship product, Quotient™, provides modern, efficient, and powerful analysis combined with simplified access to a wide array of financial content.

Access Quotient’s Interactive Tour from the Help Menu, as shown below. The Interactive Tour from Quotient’s Home Page directs new users on key elements including the Data Visual, Navigation Panel and the Application Ribbon. The Interactive Tour starts with following...

In our recent blog “Intro to Qubles,” we discussed how Quotient leverages the respective strengths of Python and SQL to create a proprietary Python data container class that we call “Qubles.” For sourced data, we display the Python and SQL code that drive that the...

Quotient makes factor construction transparent through what we call the “lineage” functionality. With the lineage functionality, users can specify the raw data item components of a derived data item to be returned along with the computation of a derived data item....

Factors are the key driver for active quantitative investment research.  Factors are derived data items.  The raw data item used to generate factors can be technical price and trading volume data, and fundamental data including financial statements and...

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...