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Abstract Background

Automating Algorithms For Block Trades Improving Accuracy And Productivity


An investment advisory firm with AUM of $140 million

Business Challenge

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



Productivity Improvement

From an average of 16hrs to 30 mins

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Accuracy Improvement

Eliminated human errors

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