Commoditized execution services and regulatory changes for best execution mandated by recent government regulations have pitted brokers against each other to offer value-added algorithmic trading services to their institutional clients. Brokers are engaged in an arms race to provide algorithmic trading strategies to the buy-side that come equipped with analysis tools for traders to better understand how to use them. In most cases, buy-side traders see the results of an algorithmic strategy at the end of the day or even the next day, which makes it harder to navigate with whom to trade in order to achieve best execution. A movement to real-time transaction cost analysis is happening amongst a few of the larger brokers who spend many months developing applications capable of processing and analyzing massive amounts of quote and trade data to achieve near instantaneous analysis. From a technology perspective, a complete real-time solution must be comprised of market data feed handlers, a high frequency database, an analytics engine, a FIX engine, EMS/OMS capabilities, a pub/sub interface and the capability to store the data for subsequent use.

A leading broker-dealer was rolling out a suite of new algorithmic trading tools on their proprietary trading desk. Their intent was to build and test complex trading strategies for internal use that would eventually be offered out to institutional fund managers. The broker wanted a way to search for real-time opportunities and automatically apply an appropriate algorithm based on pre-trade decision support tools. They also wanted to build other tools on the same platform to measure executions in real-time versus eight different calculated benchmarks to enable concrete validation. The goal was to combine real-time equities data, executions data from their EMS and proprietary trade data on the same platform, and publish the benchmarks to their EMS, OMS and Compliance Engine. After going through a detailed RFP process, the broker decided on Vhayu. All internal and client-supported applications are currently running on a single server that has built-in fault tolerance. The solution was customized, tested and launched into production in less than three months.

• Enables monitoring of execution quality of algorithms as orders are being worked
• Reduces trading costs associated with outside TCA services
• Calculates thousands of simultaneous analytics in milliseconds
• Analyzes market data down to tick level
• Allows for user-customizable benchmarks and analytics
• Works with any FIX-based EMS or OMS