Expose Big Data in the Research Language of Your Choice
When you need to access data in a database, must you first write code in your research language to request the data, fill in missing values, combine it with ad-hoc data, filter it down, sort it, and then clean it?
We think of data in the form of a cube, having 3 dimensions: universe, time, and data. This concept makes it easier to utilize data when combined with the following features of our design:
- Intuitively access financial entities and their attributes, such as AAPL with ClosePrice, instead of needing to write complex SQL queries.
- Look back through time, across related instruments, or across data sources to replace missing or incorrect data values.
- Retrieve, filter, join, and sort large volumes of data in your analytical tool.
- Reconfigure the data items you use when performing research vs. live trading in order to get the most comprehensive dataset.
- Configure data access calls once and reuse forever across languages, including Matlab, SAS, R, and Python.