Event-Driven Interface Used by Brokers and Hedge Funds to Achieve Sub-second Executions for High-Frequency Algorithmic Trading Strategies
Los Gatos, CA, October 31, 2005 – Vhayu Technologies today announced the release of EventStream version 2.0 to generate faster event triggers, complex analytics and other real-time notifications integrated within Vhayu Velocity™. The Vhayu Velocity engine is a fast, reliable software infrastructure that enables trading applications to capture, store, analyze and act upon massive amounts of real-time and historical market data without debilitating latency. Velocity allows firms to store and analyze the data simultaneously using a patented, proprietary Tick Processing and Persistence method.
EventStream 2.0 is optimized to reduce the amount of new code to be written to enable event publishing. When using EventStream, developers simply have to implement their logic for creating or changing events. The entire foundation is a fully functional, server-class publisher. Currently in production by Vhayu customers, EventStream eliminates additional delays by publishing the data within the engine instead of third-party Pub/Sub software running outside the Vhayu framework.
"Our customers look to us for any edge they can get to achieve faster executions", said Jeff Hudson, CEO of Vhayu. "The EventStream code is executed prior to data storage to ensure low latency, which is especially critical when running hundreds of simultaneous analytics. For example, EventStream is capable of publishing tick-by-tick VWAP of each component of the S&P 500 to multiple trading applications subscribing to the information, all in a matter of milliseconds."
Because algorithmic trading analyzes past patterns to identify new opportunities, EventStream was architected to provide access to incoming ticks, intraday data and years of historical tick data in a single module. Complex alerts can be configured to scan the entire depth-of-book in real-time and historically for market movement patterns that most trading applications miss because of the overwhelming amount of data.