- First step in developing quantitative trading algorithms…is to gather data.…The reality is that we're going to need different types…of data for different types of investments…that are out there.…Training these days is done in all kinds of products.…From commodities and equities to currencies, fixed incomes…and of course derivatives like…the VIX and the associated products…that we're talking about in many of these sections.…But in addition to the basic historical data…on pricing that we need for any type…of algorithm we're going to develop,…we often need external data.…
Recall that as an example when Renaissance Technologies…was giving an example of the type of training that they do…they talked about the correlation…between weather patterns and stock prices.…We might want to gather some similar data.…One of the best ways to do that…is through an Excel add-in called FRED…or the Federal Reserve Economic Database.…And it's available through the St. Louis Federal Reserve.…If you don't have it, you should go Google…
Professor Michael McDonald provides a brief primer on securities markets. He explains how data helps investors forecast performance and automate trading. Then he moves into the practical steps: coming up with algorithmic trading rules and developing and testing an algorithm. Finally, he shows how the algorithm can be applied and eventually expanded to other securities. Anyone working in financial services, or interested in investing in the stock market, will be able to use these tutorials to understand and develop simple trading algorithms of their own.
- Define what a share of stock is.
- Classify the type of trading that attempts to capitalize on the bid-ask spread.
- Name the rule that can be used as a metric for Fed interference in the market.
- State the first step in a data analysis project.
- Identify the type of characteristic algorithmic trading relies on.
- Break down how VAR is used to manage risk.