Automating Algorithms For Block Trades Improving Accuracy And Productivity
An investment advisory firm with AUM of $140 million
Our client, a registered investment advisory firm with assets under management of $140 million wanted to eliminate the manual processing of block trades which was:
Cumbersome and prone to errors due to the manual sifting of multiple spreadsheets.
A productivity sink, due to the fact that these trades had to be done one account at a time. Since they had proprietary algorithms that used both external market data as well as internal client sensitive data, none of the existing re-balancing software in the industry could do exactly what they needed done.
Menerva developed a rebalancing software solution that implemented:
A single view of the data to be used for processing
The client’s proprietary algorithms using Python along with its data processing libraries for the functional logic and Apache Spark to scale the data engineering processes
Productivity of block trade functions which normally took anywhere from 8 to 24 hours dropped to under 30 minutes
Client could be confident that the trades were being executed accurately as the manual dependencies were eliminated
From an average of 16hrs to 30 mins
Eliminated human errors