Fast or Focused? Making Low Latency Accurate
o Exploiting multi-core processors:
With the rapid increase of the number of cores available on the same processor, new techniques to exploit this new chip architecture are essential in successful software development. The acceleration techniques, such as by-pass kernels or by-pass network layers, require a rethink of software design. This leads to maximising the processing power available on the same chip rather than on another server.
Liquidity providers and price takers have very distinctive aims, in some instances, leading to contradicting trading execution objectives. The current latency push is as much derived by technology change, as it is a marketing tool by vendors and venues that indiscriminately and solely focus on latency to prove excellence. In effect, a fit-for-all formula, which is uniquely based on latency, is counter-productive. Latency should be approached with an understanding of the current technology challenges and what the upcoming changes, whilst taking into account the overall execution and trading goals.
Defining latency requirements
Latency requirements are primarily defined by the business objectives, with a broad distinction between liquidity providers and price takers.
For liquidity providers, the latency that matters is relative latency, defined as their ability to be faster than their peers and the exchanges. The aim is then to execute within the shortest time possible both in terms of detecting price discrepancy and in terms of execution. This has become the primary driver of the current arms race for exchanges and venues, (to attract liquidity) as well as the liquidity providers (to beat their competitors). Interestingly, liquidity takers focus mostly on top of the book price (and spread), which tend to have low fill rates, and the important latency is the single order latency. For price takers, the latency that matters is absolute latency, which is their ability to take liquidity in a fragmented market place. This category is focused on the time it takes to execute an overall investment strategy. Consequently, they are mostly interested in the fill rate and have a much higher appetite to take liquidity within the order book.
An example to illustrate the difference between these categories, we can consider two systems, with different latency and hit ratios: System A, has an average hit ratio of 90% and round trip latency of 1ms, and system B has hit ratio of 30% at 0.5ms. Therefore, for every 1,000 orders executed, system A would provide better results (+50%) for a price taker than system B. In real life, the former example, System A (price taker-oriented) and System B (liquidity provider-oriented) would have much larger performance differences. System A would create real capital risks for a liquidity provider, and System B would have very poor execution performance.
In reality, it is much more complex than the example above, with latency, hit ratio and standard deviation (which in some cases, provide the predictability of the execution), entering into the equation. The table below illustrates some large differences between the two main categories:
As explained in the analysis above, a narrow emphasis on latency would not only give poor results, but it also carries risks. A better approach is to look at smarter latency as the shortest amount of time that it takes to execute an order/instructions with the highest success rate and lowest capital and execution risk.