In our last SFS blog, we talked well-nigh how the technical challenges in the performance and scaling of our Quotient tracery were solved through the integration of our backend with Snowflakes Snowpark for Python. We tabbed Snowpark a game changer for developers wanting to build faster data supersensual tools on a platform that supports dynamically subtracting processors, memory and users. Okay, that probably describes all developers. ????
Also in that blog, we transiently referred to, dare we say teased well-nigh The Quble – Quotients cadre data numbering and caching engine.
But what is the Quble? And why should you care?
SFS team of Quantitative Analysts and Data Scientists each have many years of Python minutiae wits and of heavily using the Pandas library. Pandas is superstitious and in past lives weve built very ramified factors and models using the data structure and methods of that library. But unfortunately, Pandas, from our quant finance/data science perspective, has some major limitations. limitations that we needed addressed if we were going to build an impactful data science tool.
While the Quble Library was ripened to functionally write these limitations, it took the release of Snowpark to requite us a platform to make it a viable solution.
To develop Quotient, we had to start with The Quble.
Pandas is a proven and popular library in Quant Finance. So lets highlight some ways The Quble improves upon Pandas, how Snowpark makes this possible, and why the Snowpark platform might be a unconfined solution for your projects.
Over the past six months, Quble has been redesigned to place Snowpark at the cadre of SFS new flagship product Quotient for Snowflake. With Snowflake under the covers, Qubles can handle massive problems with user scalable resources.
For a sense of the power of the Python library we built on Snowpark, here are some of Qubles features:
- Quble objects distinguish between alphabetize & value columns (simplifying joins and merges).
- Quble columns moreover have metadata to describe their contents. For example
- Date columns can have time frequencies and Numeric columns can have currencies.
- Such metadata automatically manages frequencies and currencies during joins and transformations.
- Qubles provide streamlined reindexing during joins. This full-length allows for streamlined reconciliation of security identifiers using our Dynamic Security Master.
- Qubles supports hundreds of powerful finance-oriented data manipulation and time-series numbering methods that are written in Python in Snowpark.
- Most importantly, the Quble Library in Snowpark allows users to run calculations that are co-located with their data.
- No need to move data when and along to numbering servers.
- All of the data matching, manipulating and filtering occurs on the data servers.
- User generated results can persist on Snowflake or any volitional data store.
Now lets see The Quble in action
# First import RootLib, which is pre-configured to find your data whether in SQL, CSV or other data structures
from qubles.io.base.rootlib import RootLib
# create handles to a couple of datasets
rf = RootLib()[‘Reuters Fnd (CFT)’]
ds = RootLib()[‘Datastream’]
# Create a Quble using a vital moving sum formula
# rules for dealing with missing data have once been defined
# Note that this is summing wideness trailing Fiscal quarters
sales_sum4q = rf[‘SALES_FQ’].msum1d(4, ‘Fiscal’)
# A few increasingly calculations to get a Sales to Enterprise Value Quble
# mapping identifiers and standardizing currencies all happen overdue the scenes
# Note that Fiscal data combines with Calendarized data to create a calendarized ev.
ev = ds[‘MKT_CAP_M’] rf[‘LTD_FQ’] – rf[‘CASH_FQ’]
sales2ev = sales_sum4q / ev
# easy translation to (and from) pandas!
sales_sum4q.to_pandas_df().head()
This just touches the surface of why we think The Quble is part of what makes Quotient such a powerful and flexible data science tool and can be of interest to you.
Click here to watch a unenduring overview of Quotient. For increasingly information on Quotient for Snowflake and to schedule a demo, please fill out a request form or email us at info@scifinsys.com
———–
We did it again, we brushed over a huge innovation misogynist in Quotient. Did you notice when we mentioned the Dynamic Security Master?Please watch for our next Blog… One Security Master to Rule Them All.
