Henri Waelbroeck, director of research at machine learning trade execution system Portware, says rather poetically that the system “reads the tea leaves” in market data to distinguish different sorts of orders and execute trades more efficiently.
Portware uses artificial intelligence to help traders select the best algorithm for particular market conditions, asset class, broker, venue etc., interacting with the order flow and computing a mind-boggling array of variables in real time.
Say you are buying a stock, and you predict there is likely to be more orders hitting the bid side of the spread in the next five minutes, you should be able to operate an efficient algorithm that only posts limit orders and collects the spread as it executes. Using an algorithm that crosses the spread in this instance would be wasteful since you expect order flow to be coming your way.
Waelbroeck, formerly a professor at the Institute of Nuclear Sciences at the National University of Mexico, whose specialisms include genetic algorithms and chaos theory, said: “Just throwing machine learning at problems usually doesn’t give a very good answer. You need to have a good analytical understanding of what’s going on and this usually gives you a baseline model and then you find opportunities to insert machine learning tactically to exploit opportunities to improve the models.”
The Portware Brain, which is currently in beta, embraces an entire spectrum of complexity from high volume, low latency trading to longer horizon investing and trade scheduling. One of things it does is look at a portfolio manager’s trading history: some will be trading with the trend, in which case they are going to be competing with other managers for the same trades; others might be contrarian. “Of course it’s never as simple as that,” says Waelbroeck, “it depends on the circumstances; they can be contrarians one moment and trend followers the next”.