- [Instructor] Now that we understand some of the basics…of algorithmic trading, I want to start digging in…and looking at how we can use data…to actually build algorithmic trading models.…What you'll observe in this sheet, is in column A,…we've got a series of dates over time.…And in columns B, C, and D,…we have three different securities.…Column B is for an ETF, or an exchange traded fund,…that's focused on oil producers.…Column C is also an oil company-based ETF.…
And then column D is for the price of oil.…So you might ask yourself, well, we've got…two different ETF's and both of those ETF's…are based on oil companies.…You might think then, that these two ETF's…should move in sync with one another.…And in fact that's exactly what we observe.…I've put together some graphs for you over here…on the right-hand side to illustrate that.…What we observe is that in the period 2012 through 2017,…OIH and XOP tend to track one another,…but the relationship varies over time.…
XOP starts out having a much higher valuation than OIH does,…
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.