There are several flavors of trading strategies. The most basic type is based on buying and selling a single asset. An example of this type of strategy might be just trading the S&P 500. You could track the performance of the S&P over time and enter positions based on past performance. For example, if the S&P has been doing well for a while, you might enter a long position under the assumption that it has momentum. Another common strategy is to find pairs of assets that seemed to be related and trade based on their relative movements. These are called pairwise strategies. For example, say there are two big companies in the beverage industry, these companies are probably subject to many in the same market affects, the price of ingredients, the cost of shipping et cetera. If one stock starts to appreciate more than the other, you might think that the laggard will eventually catch up. A pairwise strategy would make trades to try to capture this differential. Another class of trading strategy extends this idea to groups of stocks, these are called cross-sectional strategies also known as equity statistical arbitrage or equity market neutral investing. This is a popular type of strategy and involves comparing hundreds to thousands of stocks to determine which to hold in long and short portfolios with the goal of benefiting from transient market phenomena without being subject to overall market movements. The comparison is often based on price and or volume or fundamental information. A momentum signal where you rank stocks based on the strength of prior returns over a given period is an example of a cross-sectional strategy. Finally, there’s a class of strategies based primarily on new types of data, such as satellite imagery, social media, geolocation or consumer transaction data. Large hedge funds and asset managers are most interested in strategies three and four. These are the two we spend most of the time on in this nano-degree. Why is this? Two reasons: first, large professional market participants by definition have large amounts of capital to put to work. As such, they desire strategies with high capacity, this means the strategy can be put to work at a meaningful asset level and this is most often achieved by trading very many stocks. Cross-sectional strategies all other things being equal, have the highest capacity. Second, professional market participants are looking for differentiated ideas. Given the proliferation of alternative data, many funds hope to uncover signals in hard-to-find, expensive and difficult to work with data. We will talk about that later in this nano degree.