Most quants you will come across are theory driven. They start with some economically feasible explanation of why the markets behave in a certain way and test these theories to see whether they can be used to predict the future with any success. Many quants think that their theories are somewhat unique to them, which is part of why so many of them are so secretive. But this turns out, almost always, to be a delusion. Meanwhile, many outside the quant trading world believe that the kinds of strategies quants use are complex and based on complicated mathematical formulae. But this generally also turns out to be false.

In fact—and in defiance of both the presumed need for secrecy and the claims that what quants do cannot be understood by those without doctoral degrees—most of what theory-driven quants do can be relatively easily fit into one of five categories of phenomena: trend, reversion, value/yield, growth, and quality. It is worth noting that the kinds of strategies that quants utilize are actually exactly the same as those that can be utilized by discretionary traders seeking alpha. These five categories can be further understood by examining the data that they use: price-related data and fundamental data. As we will see throughout this book, understanding the inputs to a strategy is extremely important to understanding the strategy itself. The first two categories of strategies, trend and mean reversion, are based on price-related data. The ...

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