Algorithmic Trading Demo
McObject’s Algorithmic Trading Demo Application simulates time-critical quantitative analysis and data management tasks used in automated trading. Its objective is to show that a distributed in-memory database system (IMDS) for capital markets can process complex queries and maintain a high transaction rate, in part by distributing the database to traders’ workstations.
The application generates market data to simulate incoming updates to a basket of securities managed by a Master database (eXtremeDB Financial Edition). It is premised on the idea that a group of traders will share a real-time market data feed, which is filtered to remove trade data not related to the basket.
Using eXtremeDB’s High Availability sub-system, the demo creates multiple read-only replica databases used by individual traders, at their workstations, for quantitative analysis underlying trading decisions. In launching the demo, the user can choose to run one of three pre-defined queries, or run all three simultaneously.
Because the queries execute against a local in-memory database, they’re very fast. And because the queries hit the remote/replica databases rather than the master in-memory database, they have little impact on the ability of the master database to keep up with the workload.
Download the Algorithmic Trading Demo Application
Get the readme file with more details about the demo.
McObject’s Algorithmic Trading Demo Application.
The demo’s GUI application, called ConsoleMaster, offers three speedometer-like gauges of DBMS performance.
The left speedometer measures the transaction rate on the master database.
The middle speedometer tracks the number of objects updated per second (again, on the master database), which should be greater than the number of transactions per second (because some transactions involve more than one object).
The third speedometer measures the number of query result rows returned by the traders’ queries.