Existing trading systems are complex sets of software and hardware with key requirements for stability and reliability.
The complexity of these systems has, until now, meant they are very expensive and difficult to easily adapt to new asset classes and market requirements. The older these systems are, the more difficult it becomes to maintain and upgrade them, yet replacement systems are prohibitively costly.
We see complex legacy systems in all corners of the markets – in banks, exchanges, insurers and more. Often, knowledge of the system as a whole has been lost and change has become risky, prone to error and expensive. Small changes can result in system failures, downtime and reputational damage.
MarketGrid has taken an entirely new approach to the development of trading systems and matching engines. We use a data model approach to automatically generate much of the code for the MarketGrid system from the data model we build. This vastly reduces the risk of redundancy in the code, future-proofing the system in a way that no other vendor has done before and minimising cost of ownership over time.
The data model approach also means that the system can be changed and adapted very easily – the code for new functionality is generated automatically and the system adapts itself to embrace new elements within the ecosystem. This also makes MarketGrid limitlessly scalable as the system can simply adapt to new products or growth in users. This approach also means that our implementations much faster and more flexible than those of our competitors.