Robert Martinez, CRO, Code Willing, Inc.
In the ever-evolving landscape of the financial world, staying ahead of the curve is essential for hedge funds, asset managers, and data providers. One of the most significant challenges these entities face is the construction and maintenance of a historical security master/map/data management solution that keeps pace with real-time market changes. In this blog post, we’ll explore the critical role of such a system and how Code Willing’s CWIQ Code can alleviate the burden, making life easier for data scientists, engineers, and anyone else involved in this data-intensive world.
The Data Dilemma
Global financial markets operate in real-time, with new listings, de-listings, and daily corporate actions constantly reshaping the landscape. These changes affect everything from asset class identifiers to pricing and shares outstanding, making it imperative to stay in the know. Failing to identify these changes or overlooking critical data can disrupt a firm’s daily trading strategies, trade execution, and settlement processes. In essence, the security master/map/data management solution serves as the bedrock for any business aiming to succeed in trading and/or data-driven strategies. Keeping historical and daily data updates accurate is non-negotiable.
“80% of data scientist time is spent cleaning data rather than creating alpha”
The Data Scientist’s Predicament
Many firms find themselves licensing data from multiple reference data sources to create their security master/map/data management solutions. It’s not uncommon for some to allocate a substantial portion of their data scientists and engineers to mapping not only reference data but also incorporating new and existing data sources. It’s been reported that as much as 80% of a data professional’s time is spent on the daunting task of “wrangling/mapping/connecting” data. This extensive data management process leaves little room for researching, building data models, devising trading strategies, and generating data insights. This bottleneck hinders progress and can lead to inaccuracies in historical reference data, impacting strategy/model back-testing and daily trading operations.
Code Willing Solution: 100% data scientist time spent creating alpha & insights
The Code Willing Solution
Code Willing, founded in 2015, has been at the forefront of solving this data management conundrum. Our Data Team has been tirelessly mapping and connecting data from renowned reference data providers, global exchange data, and over 150 other data providers for our clients. However, our approach goes beyond historical data. We’ve established a systematic mapping and data quality process that ensures your data is updated daily with precision and reliability.
Our global team of data scientists and engineers with years of expertise, is dedicated to managing the intricacies of data mapping, freeing up your in-house resources to focus on more value-added tasks. Whether you have existing data or exchange licenses with reference data providers, global exchanges, or other data sources, Code Willing has the infrastructure, experience, and well-defined processes to deliver historical and ongoing updates tailored to your specific requirements.
Conclusion
In the fast-paced world of financial markets, the importance of accurate and up-to-date data cannot be overstated. Firms relying on a historical security master/map/data management solution must ensure its accuracy to drive successful trading operations and data-driven strategies. With Code Willing’s CWIQ Code, you can offload the burdens of data management, allowing your team to focus on what truly matters: staying competitive and innovative in the ever-changing world of finance. If you have the data, we have the map – let’s connect and build a robust future together.
The reason they spend 80% of their time cleaning DATA …is because they cannot find ALPHA. They wouldn’t know where to look. They avoid even trying looking for ALPHA – bc they
You cannot teach “Soros” or “DE Shaw” or “RenTec” or “WorldQuant”. Did someone teach Larry and Sergey – “how to do Google?” Or Larry Ellison how to start an enterprise database company at 120? Or Isaac Newtown – how to write The Principia Mathematica?
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