Keys in designing quant strategies
Quantitative trading is a sophisticated area of finance. It takes a significant amount of time to gain the necessary knowledge and skill to construct your own trading strategies. It also can require extensive time spent on programming, but that doesn’t mean it has to be completely out of reach for the novice trader. This article discusses how to handle some of the basics to get you started.
Trading algorithms typically start with an observation of market activity or a theory on the how the world works, followed by a plan to capitalize on those theories in the financial markets. It could also use some external data or event that the developer believes affects a stock's performance.
Recently, Twitter (TWTR) was selected to be in the S&P 500. As there are billions of dollars following the S&P 500, so it makes sense that on that announcement Twitter would have a lot of capital flowing into it, causing it to quickly rise (see “Twitter’s S&P bump,” below).
The Twitter example provides the germ of an idea for a trading strategy. We can look for stocks likely to be added to the S&P 500 Index during the next year. This is relatively easy because the S&P 500 includes the 500 largest stocks listed on the NYSE or Nasdaq stock market as measured by market capitalization. The S&P 500 criteria is a well-defined formula, so it is simple enough to estimate which ones might be added and subtracted each month (see “S&P turnover,” below).
From there we want to create a signal. Given the announcements can happen overnight, we only need to make the investment at close each day for the assets that will be added to the index. We can’t be 100% sure what stocks will be added, so we’ll have to buy a basket of stocks most likely to move into the index.