World first: ThinkLiquidity granted patent pending status on all new transaction scoring system QuantView

ThinkLiquidity Managing Director Jeff Wilkins today announced the launch of a transaction scoring system featured in its new web application QuantView. The patent pending system delivers robust performance metrics on trades conducted over electronic trading platforms. QuantView data can be used by trade desks and risk managers to quickly and accurately assess order flow and improve risk management.

“There’s no other product available that makes it so easy to see key trading metrics on both a micro and macro level,” said Mr Wilkins. “Smaller shops often do risk management from the bottom up, looking at individual accounts. Our scoring system grades every trade and assembles the data into an Account Scorecard.”

Mr. Wilkins further elaborated “Larger brokerages and trading desks are more concerned about risk limits and want insight into when and what to hedge. We aggregate all the data we have on individual trades to provide context on market exposures. Risk managers can use this data not only to better categorize individual accounts but position themselves to better take advantage of market conditions.”

The transaction scoring system was developed in house by ThinkLiquidity’s quantitative and development teams. The system rates the entry, exit and overall quality of every transaction passing through the computation engine and assigns scores. Wilkins continued, “The scores can be used to determine whether a trader is good, bad, lucky or skilled.”

ThinkLiquidity considers that most OTC brokers view their risk in terms of A Book and B Book. The decision to A Book or B Book a particular account can often be due to external factors rather the caliber of the trader.

QuantView lets brokers view their exposure by various meaningful attributes rather an arbitrary A Book or B Book classification. “It used to be hard to view real time exposure on different segments of your book. QuantView makes it easy. For example, you can filter by average hold time to see how short term traders are positioned. You could filter by average trade size to see the exposure on the big traders. We keep all kinds of statistics that can be used to segment your exposures.” Mr Wilkins added.

The application potential for the data engine is very broad. In addition to the OTC brokerage space, Mr Wilkins foresees proprietary trading firms, market data providers and individual traders as target audiences for QuantView data.

Photograph: Jeff Wilkins, Managing Director, ThinkLiquidity, Grand Rapids Michigan