You’re a trader for a global asset management firm. You are sitting in a leather gaming chair. It’s comfortable. You have a headset on that covers your ears and face and puts a series of three-dimensional monitors before your eyes. Your dominant hand wears a thin electronic glove that you barely notice.
Through your visor you can see alternative versions of the same data sets presented in an organizational structure you find most useful. You’ve set up customized views of the various relationships between the instruments you trade and you can gross-up the ebb and flow of pricing behavior into a montage of exposures.
A trading instruction arrives from your portfolio manager in the form of a visual picture, perhaps a heat map of some sort, which shows the multi-currency changes in exposure the portfolio manager wants to make across segments of his fund. The trading instructions you receive carry an imbedded data set that holds the underlying variables and assumptions that the PM used in making his investment decisions. That data set and the reallocation of exposures is now input into your analytic views of the marketplace on your screens and into the algorithmic trading models you may decide to use for this trade.
You run a series of analyses in real time that you then move around your virtual screens by simply shifting your index finger up and down and around. You like one of the combinations of securities, execution times, prices and trade parameters that appear as one of several proposed trading solutions. You give the strategy a name, something your sell-side sales trader will get a kick out of.