When the movie Minority Report came out in 2002, I was working as a VP in the Quant group at Fidelity Investments. Among all the high-tech gadgets introduced in the movie, what stood out to me the most was ease in which Tom Cruise’s character interacted with data and analyzed his problem. I wanted that!
As a Quant, I have always been frustrated that stating the problem is complicated and time-consuming. Worse was knowing that every Quant is doing the same mundane setup work in mapping data items and coding up complex math problems! Then comes the problem of integrating all the independent tools needed to create an end-to-end research and asset management platform.
Is this resource intensive effort part of the value proposition to our clients or where we found competitive advantage over other Quants in the industry? Of course not!
Just look at how Quant Funds like Goldman Sachs, Lazard and Panagora describe their quant investment process. They don’t talk about how much time and energy got put into mapping vendor data or building a backtesters, portfolio construction engines, and downstream analytics. They talk about the things clients want to hear, like the robustness of investment processes, how well portfolios perform, how consistent is that outperformance and did the portfolios adhere to the agreed upon styles.
Enter Quotient. Several years ago, Scientific Financial Systems’ founding team of experienced Quant Finance practitioners decided that data scientists needed a major upgrade in the tools they use to perform complex, cutting-edge analytics. After 20,000+ hours of development, we released in 2019 Version 1.0 of our Python-centric, data vendor agnostic, platform independent, quant software suite to handle these common yet critical needs of our industry.
Quotient allows users to easily access, analyze, and integrate data to build financial models, find alpha, and share results and strategies across investment teams. The application also addresses problems as diverse as large and high-dimensional time-series datasets, machine learning algorithms, dataflow management and self-organizing networks, interactive visualization, and automated reconciliation of frequencies, currencies and security identifiers. When released, Version 1.0 provided users with integration and extensive schema mappings to data from Refinitiv’s Quantitative Analytics and QA Cloud, and Bloomberg’s Server API.
I’m excited to announce that this month we just released Version 2.0 of our cutting-edge application. With a redesigned back end, Quotient can now manage much larger data problems, has integrated Jupyter Notebooks and improved functionality across the application. Expanding on our goal to support any data anywhere, Quotient 2.0 now connects to Snowflake Data Warehouses and allows seamless integration into its models of data maintained by users or from vendors like S&P Global. We’ve also made Quotient even easier to deploy locally or to your corporate cloud service using Docker containers.
In summary, Quotient 2.0 is another leap forward in our harnessing the power and functionality of Python for data science and providing users a platform for easily creating custom analytics and visualization.
Perhaps soon we’ll be fitting you with a Minority Report analysis glove.