- Case Study -
Implementing a hedge fund's new trade cost analysis platform
Large Boston hedge fund.
The client needed to integrate trade order and execution related data for systematic decision making activities applied to both pre-trade and post-trade activities.
The hedge fund's solution was fully and seamlessly designed, implemented, tested, and rolled into production for use by their geographically distributed team of professionals.
We provide a wide array of cutting-edge services required to fully build a fast data pipeline.
We offer a "simple" approach to designing so called complex systems with an emphasis on Design and DevOps.
Upgrading a key piece of trade plumbing
Transaction Cost Analysis (or 'TCA') lets investment managers determine the effectiveness of their portfolio transactions. Most buy-side asset managers have in place some kind of TCA platform, including this hedge fund. However, the existing solution which generated pre-defined analytics did not allow the asset manager to do in-depth exploration and analysis of the underlying trade and market data. The hedge fund wanted to apply their quantitative research know-how directly to the data to generate cutting edge TCA using advanced data science techniques. After performing a buy-vs-build review of available vendors, the client concluded existing solutions lacked flexibility to seamlessly integrate data from any existing and future data sources and limited ability for integration with Python for research .
Full implementation throughout global operations
Recognizing this, the large hedge fund partnered with Advanti to support the transformation of its global trade cost analysis capability. Advanti embarked on a comprehensive program to integrate a best-in-class transaction cost analysis data solution into the hedge fund's platform, including by build-out of custom analytics and executing comprehensive testing to ensure near perfect data quality.
A front-to-back integration
Following close collaboration and coordination between Advanti and the hedge fund, we developed a 100% cloud-based solution, using AWS, Lambda, ECR, Parquet, S3, and consumption layer in Python and Dask. A great emphasis was placed on writing test cases for shaping data into clean data, that could then later be consumed by Apache Superset and Python Pandas, Dask, and currently Ray.
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