Technology has reshaped our world and transformed virtually every industry — including investing. Today’s investors have access to an endless amount of information and trading tools that help them make smart investments.
Additionally, quantitative trading has grown in popularity as the markets have declined. While many retail investors do not have the expertise to build out quant models and automate trading bots to operate within the specified parameters, there is a much simpler approach that operates on a similar basis.
Mills, who led the global equities business out of Toronto since 2005, decided to depart after “productive discussions” the firm had on succession planning, according to an internal memo.
Once the full framework has been designed, implemented and debugged should you start looking for ways to speed up and upgrade the inner loop of the back-tester (the order handling module). It is a lot easier to take a working program and make it faster than it is to take an overly optimized program and make it work.
I am not sure that “data science” will ever quite get to that point, where coding machine-learning models will be a baseline expectation of every first-year analyst. But you could easily imagine it getting close, to the point that every reasonably sophisticated investment firm will have a data-science department, and every reasonably competent analyst will at least be able to formulate useful questions for those departments and make good use of the answers.
Quant funds, which rely on complex algorithms to navigate global markets, have suffered this year because of the rise in volatility. They were caught flat-footed in February when markets turned turbulent on concern over rising interest rates, followed by trade wars with China and the election in Italy.