Better Data Science,
Better Investment Decisions
Quotient™, Scientific Financial Systems’ flagship product, provides an enterprise solution for investment managers that provides a data driven view of investment opportunities and strategies, along with more control and greater flexibility.
Quotient™ enables financial institutions to analyze rapidly large amounts of data and provide data-driven results and recommendations.
Quotient™ partners with and integrates with some of the largest global financial data providers and provides an advanced analytical engine for data manipulation, factor building, back-testing and portfolio construction.
Simon Whitten, Director of Research at Syntax Indices
Implementing Scientific Financial’s Quotient™ will provide your Firm with Significant Economic Benefits:
Improve Research Team Effectiveness
We all know that the best research teams have high levels of collaboration to help reduce risk. Quotient™ was designed to allow your team to share the same platform to promote transparency and standardization of formulas and approaches.
- Users share a common research platform
- Promotes Collaboration
- Reduces Single Person Risk
- Makes Transparency and Standardization of Formulas easy
More time on research and developing quantitative models
Spend less time developing your research architecture and more time on generating research output and actionable quantitative models. Use a solution built by practitioners with a track record of success.
- End-to-end research workflow
- Define derived data items
- Build screens and multi-level models
- Extensively backtest and forecast using advanced machine learning techniques to enhance alpha
- Leverage factor examples and tutorials to get users working quickly
Effectively Integrate Vendor and Internal data source
Newly acquired data sources can be quickly integrated and ready for use. Vendor and platform agnostic data sourcing allows seamless integration into models.
- Supports sql, cloud sources such as snowflake, csv files, excel and more
- Data standardized for frequency, currency, and corporate actions
- Data presented in an organized and self-documented manner
- Supports point-in-time data sources for improved predictive power
Latest Articles & Insights
Why Should We Consider the Use of Machine Learning in Quantitative Finance?
During my time in Quant Finance, regression analysis was generally the best tool I had for determining the effectiveness of factors and models. I was especially comfortable performing regressions when the relationship between my variables was clearly linear, my...
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...
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...
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