Opening Cross: To Shine Fresh Light on Data, Try an ‘Olympic’ Torch

Of course, there’s a certain amount of tailoring the analysis to deliver the conclusions you want going on here (lthe smaller the country with a half-decent medal slew, the more successful they’ll be by these measures). But by the same chalk, it’s only by performing analysis that looks at these kinds of different measures that you discover ways to look at the same information from a different perspective, and potentially create new trading strategies.

For example, it takes someone looking afresh at the swaps futures market and its potential for hedging and arbitrage to build momentum around the hitherto less actively traded three- and four-year tenors to persuade Eris Exchange to enlist market makers to quote firm two-sided prices in these tenors to meet client demand for more active markets for hedging bank loans or for arbitrage against CME futures.

But looking at things in a new way isn’t as simple as taking a step back or squinting: finding outliers or correlations, or testing how a strategy would behave requires lots of data, and the tools to make sense of it, such as hardware ticker plant vendor Exegy’s new Journal Replay solution—a managed service that allows firms to record and replay data at multiples of the actual throughput rates and speeds to test their infrastructures, or to provide a definitive historical replay to justify trading decisions and timings for best execution purposes. Exegy isn’t the first to provide a capture-and-replay system, but the difference with Journal Replay, officials say, is that it can consolidate hundreds of data streams to simulate the entire market being replayed, rather than a vendor solution that merely replays that vendor’s data, while being a managed service removes the burden of managing all that historical data and analytics off the customer so they simply set the parameters, run the analysis and receive the results.

Sometimes winning is as simple as being fastest and first past the post. Well, it sounds “simple,” at least, though those data athletes who make it look so easy might disagree. Sure, the principle is straightforward, but the execution can be highly complex—not to mention expensive. It’s ironic that many in the low-latency space use imagery of Formula One racing because of its associations with speed, when it could equally be because the expense of implementing a winning low-latency trading strategy (including the costs of acquiring data, building or buying trading systems, the expertise required to create and test algo trading strategies, and the costs of space in co-location centers and microwave connectivity) rivals that of running a successful F1 team (or frankly, even an unsuccessful one!).

As with most things in life, none of the above come to pass without a lot of hard work. Sure, there are genetic freaks of nature (and I mean that in a good way) like Michael Phelps who are seemingly wired specifically to win Olympic medals for swimming. But the truth is, if Phelps sat on the couch all day eating Hot Pockets and watching The Real Housewives of… and Andy Cohen’s Watch What Happens Live, instead of working out and practicing, he’d be less like a stingray and more like a whale. And while I believe that to a large extent, you make your own luck, the circumstances that lead one to dive into a pool (or a dark pool) for the first time can be as random as a number generator. So when it comes to getting the most out of data, use all the tools and luck at your disposal.