NovaSparks Announces Alliance with Orc for the Seamless Integration of Ultra-Low Latency Deterministic Market Data Feeds

Paris and Boston, MA - 29 October 2012

Traders will experience more predictable speed for market data and improved trading performance

Orc, a leading provider of technology and services for the global financial industry, and NovaSparks, a deterministic FPGA market data company, announce an alliance for Orc clients to use high speed FPGA data feeds from NovaSparks.

Market Makers and Principal Traders can seamlessly switch to the nanosecond-speed FPGA feeds without changing algorithms or screen set ups in the Orc Trading Products.

Orc conducted extensive research to determine the benefits of FPGA technology and found compelling performance results. Customers that take advantage of this alliance will experience more predictable speed for market data and overall improved trading performance. Orc clients can adopt this ultra-low latency deterministic market data infrastructure without any modifications to their current algorithms or Orc products.

"One of the ironies of tight markets and smaller IT budgets is that solutions are actually getting better because providers like Orc and NovaSparks hone in on core competencies and create strong alliances for companion services," said Adam D. Honore, Research Director at Aite Group.

Market makers and volatility traders are constantly facing technology risks around speed. "Orc is continuously investing in R&D to ensure clients can adopt the newest technology with 100% compatibility to their current trading algos," says Joakim Dahlstedt, CTO Orc. "Our close integration with NovaSparks' Deterministic Market Data Matrix© gives clients an immediate and seamless path to increase their trading performance without any modifications to their current trading programs."

"Orc offers the premier trading platform for market makers and we are honored to help them leverage an ultra-low latency deterministic market data infrastructure," states Yves Charles, CEO NovaSparks.

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