Stock prices periodically dip and go up. We will have a look at the probability distribution of the stock price log returns.
Let's start by downloading the historical data for a stock; for instance, AAPL. Next, calculate the daily log returns (http://en.wikipedia.org/wiki/Rate_of_return) of the close prices. We will skip these steps because they were already done in the previous recipe.
If necessary, install Matplotlib and SciPy. Refer to the See Also section for the corresponding recipes.
Now comes the interesting part.
Calculate breakout and pullback.
Let's say we want to trade five times per year, or roughly every 50 days. One strategy would be to buy when the price drops by a certain percentage—a pullback, and sell when the price increases by another percentage—a breakout.
By setting the percentile appropriate for our trading frequency, we can match the corresponding log returns. SciPy offers the
scoreatpercentilefunction, which we...