The Data & Analytics Solution For Investment Professionals

Designed By Investment Professionals
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    Enterprise solution

    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.

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    Rapid Analysis

    Quotient™ enables financial institutions to analyze rapidly large amounts of data and provide data-driven results and recommendations.

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    Trusted data

    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.

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Implementing Scientific Financial's Quotient™ will provide your firm with significant economic benefits:

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
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
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