Real-time Liquidity Mapping:Transparency and Insight
Mark Palmer, CEO of StreamBase discusses how real-time liquidity mapping can be used to improve liquidity sourcing tools and what liquidity mapping offers that aggregation does not.
FIXGlobal: What problems can occur if a trader is unable to access their desired venue, counterparty or price point? How have these problems been exacerbated by lower liquidity?
Mark Palmer, StreamBase: There are two obvious problems about not fully understanding liquidity in real-time: first, missed opportunities for the buy-side. And, second, if you’re a broker-dealer, then the problem is the potential loss of revenue resulting from the exposure created by not fulfilling trades immediately.
FG: What are the tools commonly used to source liquidity and how would real-time liquidity mapping improve them?
MP: The most commonly used tools are rear-view mirror oriented. That is, brokers and traders compile a map of liquidity and performance of liquidity providers in databases. They then generate reports that are reviewed at the beginning of each day, when adjustments are made. The problem with this approach is that traders and brokers become exposed to daily fluctuations and system market errors, which can lead to risk and missed opportunities.
FG: With overlapping aggregators of lit and unlit venues, what does liquidity mapping offer that aggregation does not?
MP: Ad-hoc, real-time liquidity mapping offers three features that simple aggregation does not: predictive analytics, interactive visualization of live venue state and alerting.
Predictive analytics allow firms to establish ‘standing’ continuous queries and to insert them into the stream of live venue information. For example, a trader can ask to identify any security with insufficient liquidity above a certain amount in real-time; continuous queries re-evaluate this condition millions of times a day, and traders can identify exception conditions as soon as they occur, rather than waiting for a report to be issued.
In terms of interactive visualization, like interactive warehouse and business intelligence tools, live liquidity mapping provides ad-hoc, user-driven exploration of data through charts, graphs and tables. Unlike traditional tools, however, real-time analytics allow users to explore information that is literally live – with just milliseconds of latency between the time that data changes and the time when the liquidity data is sliced and diced on behalf of the user.
Push-based alerting and notifications can proactively notify traders, risk managers and heads of desks according to live analytics and business function, creating a kind of push-based reporting style of processing that is revolutionary when compared to traditional reporting styles, and essential when analyzing dynamically changing liquidity pools.
FG: Faster, more detailed information enables a better service for clients, but will realtime liquidity maps be a ‘game changer’ given that brokerdealers and agency brokers have been working on retroactive liquidity maps for some time?
MP: According to a recent Aite Group report on broker-dealers, the quality of their Execution Consulting Services is now seen as more important than that of their trading algorithms. What good is a better mousetrap if it can’t be understood and managed? Live analytics allows broker-dealers and agency brokers to provide real-time transparency and insight as is being increasingly demanded by buy-side institutions.
FG: To what extent is the response to liquidity maps on a human time scale and how much will be handled by smart order routing (SOR) technology?
MP: Real-time liquidity maps are essential to understand, optimize and manage SOR technology. They allow more time to be spent on larger orders, as sales traders are freed up by the increasing ability of technology to provide live order information enabling smaller orders to be processed more quickly.
In large broker-dealer and agency broker environments where multiple SOR technologies are deployed, CEP-based real-time analytics platforms can help integrate and correlate multiple streams of execution data from multiple SOR , providing a single, live view of execution and liquidity insight.