Barclays Research Data Science have released their first open source code tools to help users query the most popular financial research databases.

Today, Barclays Research Data Science released their first #opensource code to the @github community. The tools will help users query the most popular financial research databases and harmonise #bigdata sets. Our latest blog post explains more.

— Barclays Corporate & Investment Bank (@BarclaysCIB) August 23, 2022

Ryan Preclaw, head of research investment sciences and Adam Kelleher, chief data scientist for research at Barclays investment bank, said in a report that analysts are using diverse data sources alongside traditional financial data such as filings, earnings call transcripts, which requires them to use modern technology for data analysis.

“A single project might require SQL queries, boto get requests, Spark read operations, manipulating local excel sheets, and more,” they said.

As a result Barclays’ research data science team built a tool for themselves to make it more efficient to use a new data set, which they said has been a major contributing factor to more-than-linear rate of productivity growth with time.

“When we work with a new data set, usually as part of a research project, we encapsulate the ETL (extract, transform, load) code and add it to the tool,” they added. “This makes it a push-button capability that is ready the next time we need to use it.”

Subsequently, they can usually perform the same operation with only a single line of code.

Barclays believes these tools will be useful for others who do financial research as they create higher quality control for queries and enable reproducibility by standardizing pre-processing on broadly available data sets.

“Our software is designed to be extensible,” said the report. “It’s divided into sections by data source, so it’s easy to add code to support new data vendors or sets.”

The tool is designed to abstract data sources from the end user and to present a standardized interface for working with financial data through a data access layer and an API layer.

The report highlighted that the data layers provide a clean interface to raw data. One of the main advantages is that if there are 100 applications using the database, then if the database changes the tool just has to m are one change rather than 100 if queries are implemented in the application code The API layer uses the data access layer to grab data and produce a standard format for analysis.

The API layer uses the data access layer to grab data, then reformats them into a standard format for analysis.

“The simplicity of the interface makes data logistics trivial, so analysts can focus their time and mental energy on the aspects of their work where their unique skills are additive, rather than on rote data logistics,” added Preclaw and Kelleher.