R&D Tax Relief Software
Industry Insight
Case Study Project
The R&D efforts in this project aimed to develop an advanced computational trading model capable of dynamically adapting to new trading behaviours and cost structures. Traditional trading systems were unable to process asynchronous data streams or adjust to market rule changes, necessitating the development of custom data management libraries and predictive models.
The key challenges included integrating disparate data streams, automating real-time market adaptability, and ensuring system accuracy while maintaining high-speed operations. To overcome these uncertainties, the team engineered new C++ scripts to embed exchange-specific logic, developed flexible Python libraries for handling large volumes of asynchronous data, and implemented an automated observability framework to detect and adjust to market changes in real time. These innovations significantly advanced the field by enabling an automated, scalable trading system that dynamically responds to evolving market conditions—surpassing the limitations of existing structured trading environments.
