Clustering Dow Jones stocks with scikits-learn

Clustering is a type of machine learning algorithm, which aims to group items based on similarities. In this example, we will use the log returns of stocks in the Dow Jones Industrial Index to cluster. Most of the steps of this recipe have already passed the review in previous chapters.

How to do it...

First, we will download the EOD price data for those stocks from Yahoo Finance. Second, we will calculate a square affinity matrix. Finally, we will cluster the stocks with the AffinityPropagation class.

1. Downloading the price data.

   We will download price data for 2011 using the stock symbols of the DJI Index. In this example, we are only interested in the close price:

   # 2011 to 2012
   start = datetime.datetime(2011, 01, 01)
   end = datetime.datetime(2012, 01, 01)
   
   #Dow Jones symbols
   symbols = ["AA", "AXP", "BA", "BAC", "CAT", "CSCO", "CVX", "DD", "DIS", "GE", "HD", "HPQ", "IBM", "INTC", "JNJ", "JPM", "KFT", "KO", "MCD", "MMM", "MRK", "MSFT", "PFE",...