Seamless Onboarding of Algo-Trading Customers

A leading multinational investment bank, part of the Fortune 500, sought to enhance its legacy P&L generation platform. The goal was to improve the onboarding process for high-volume algo-trading customers, address scalability issues, and enable real-time reporting.

  • Mobile, Web & Emerging Applications
  • API & Microservices Engineering
  • Data Integration

Business Issue

The client faced several challenges with their legacy P&L generation platform. The onboarding process for high-volume algo-trading customers was hindered by a traditional RDBMS-based database system, affecting transaction processing. Scalability was another major issue, as the existing architecture relied heavily on a legacy database for all data extraction use cases, putting the system under heavy stress. Additionally, the lack of a robust reporting solution limited visibility into various processing parameters, making real-time reporting difficult.


To address these issues, a scalable performance solution was built on a distributed cache system, capable of handling extremely high transaction volumes. An in-memory data grid solution was developed to aggregate and transform data in real-time, providing paged results instantly. Customized snapshot data for configured periods from the current business day was also implemented. An adaptable pricing solution using microservices and NIO architecture was created to manage a large volume of products and configure new price sources. Finally, efficient data integration facilitated the seamless push of data to downstream systems.


The implemented solutions significantly reduced latency in high-volume reporting to less than five seconds, handling 2 million requests per day and processing 500K transactions per minute. The integration with the new platform also led to a significant improvement in the UI/UX experience of the screens, enhancing overall user satisfaction.