- [Presenter] Now that we understand how to build basic…algorithms around simple rules like moving average,…it's time to broaden our view about…what rules might we be interested in.…I've gone through and gathered data…from 1990 though 2017 on the VIX.…And I've graphed that data for us here.…And if this graph looks familiar,…it's because we've visited it before.…What we see in blue is the moving average line,…and in yellow, we have the adjusted close price for the VIX.…And since we're buying and selling on a daily basis,…those are the two variables we're most interested in.…
Now what do you observe here?…Well one thing that stands out to me…is that the adjusted close, those daily values,…they're much more volatile than the moving average.…And we already know that, right.…That's the basis on which we previously built…our moving average rule of thumb.…But, there are times where…as we observe, particularly say, between 1990 and 2001,…there's periods of time where the adjusted close…remains elevated above the moving average…
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.