Corwin Yu, Director of Trading at PhaseCapital, sits down with FIXGlobal to discuss his trading architecture, the proliferation of Complex Event Processing (CEP) and why he would rather his brokers just not call.
FIXGlobal: What instruments does your system cover? Corwin Yu: At the moment, we trade the S&P 500, and we have expanded that to include the Russell 2000, although not as an individual instrument, but as an index. We also trade the E-Mini futures on the Russell 2000 and also for the S&P500. We have done some investigation on doing the same exact type of trading with Treasuries using the TIPS indices and the TIPS ETFs and a few of the similar futures regarding those as well. We are not looking at expanding the equity side except to consider adding ETFs, indices, or futures of indices.
FG: Anything you would not add to your list? CY: We gravitate to liquid items with substantial historical market data because we really do not enter into a particular trading strategy unless there is market data to do sufficient back testing. Equities was a great fit because it has history behind it and great technology for market data, likewise for futures, where market data coverage has recently expanded. Options is a possibility, but the other asset class that is liquid but not a good fit is commodities. We shy away from emerging markets that are not completely electronic and do not have good market data. While we have not made moves into the emerging markets, we know that some other systematic traders have found opportunities there.
FG: How much of your architecture is original and how often do you review it for upgrades? CY: In terms of hardware, we maintain a two year end-of-life cycle, so whatever we have that is two years old, we retire to the back-test pool and purchase new hardware. We are just past the four year mark right now, so we have been through two hardware migrations. Usually this process is a wakeup call as to how technology has changed. When we bought our first servers, they were expensive four-core machines with a maximum memory of 64 GB. We just bought another system that can handle 256 GB through six-core processors. We are researching a one year end-of-life cycle because two years was a big leap in terms of technology and we could have leveraged some of that a year ago.
Citihub’s Paul Chew and Richard Donaldson lay out the latest in Asia Pacific latency reduction and discuss how firms should prioritize their technology investments.
No one in AsiaPac used to care much about latency. However, when the Tokyo Stock Exchange (TSE) launched arrowhead in 2010 reducing their matching engine latency from 1 second to 5 milliseconds (a 200 fold improvement) it created a paradigm shift in the trading landscape by eliminating the exchange as the chief cause of latency and shifting the focus back onto market participants.
What’s more, this was not an isolated event – August 2011 will see the culmination of a US $200m investment by the Singapore Stock Exchange (SGX) to create the world’s fastest matching engine (SGX REACH) with average response times of 90 microseconds. The Australia Stock Exchange (ASX) invested US $35m to drive down their latency from 30 milliseconds to 300 microseconds; at the end of this year the Hong Kong Stock Exchange (HKEx) is expected to launch its new matching engine reducing average order response times from 130 milliseconds to 9 milliseconds (a 15 fold improvement).
At the same time, volumes are rising. TSE’s daily average equity order volume jumped 22% to 8.239 million orders in 2010 after the launch of arrowhead. Even before the proposed latency improvements by HKEx, they recorded a 41% increase in volume during the same period.So how will increased volumes and a reduction in exchange matching latency impact buy/sell-side firms? Surely it’s a benefit to doing business? Actually, with market participants now contributing to the majority of order processing latency, it creates both a challenge and an opportunity.
Increased stress will be placed on market participants’ trading systems because message volumes are growing and the time interval between messages from the exchange is falling. Conversely, market participants that can support growing volumes and drive down their own latency will create a competitive advantage. So what will this really mean for market participants and where should they target their limited investment dollars?
eTrading Platform Maturity
Our industry experience in Asia Pacific across buy/sell-side firms and vendors indicates a broad range of capability and focus. This is evident from contrasting client feedback and has given rise to what we have termed eTrading Platform Maturity:
Tier One: Platform Stability – “We care about stability, availability and reliability, not latency.”
Tier Two: Instrumentation – “We care about platform latency but we need to improve the way we measure and analyze it.”
Tier Three: Platform Optimization – “We don’t care about absolute latency as long as we’re first on the order book.”
Fundamentally, latency is one of the key barometers of system health. Significant increases in measured latency are indicative of a stressed platform which can lead to outages impacting reputation and resulting in lost revenue. We believe all firms should first establish a reliable platform that copes with daily business demands with predictable and consistent levels of latency before chasing the next tier of eTrading Platform Maturity.
Of course this is all a balancing act, often requiring business and technology teams to prioritize stability over new product development and increased functionality. Smart investment in platform stability can be achieved through simple measurement and analysis of latency to target improvements providing these are supported with the appropriate post-implementation controls.
In order to address the balancing act of how and where to invest we have defined the Latency Framework (see Figure 1) and Instrumentation Capability Curve (Figure 2). These frameworks are used to determine the impact of volume and latency on platform stability and performance, to relate instrumentation to capability maturity and also to establish where to target platform improvements for greatest impact. For example, a key to determining the inherent capacity and performance of a system is through statistical profiling of changes in latency as volumes increase to the point at which the system becomes unstable.