Correlation Graph in Python

• Must use either log returns or percentage returns
  • Standard approach is to use log returns
• The following python code will generate a correlation chart of the rolling 50 day window of the returns, showing the last 200 data points:
  • df[‘SP500’].rolling(50).corr(df[‘NDX’])[-200:].plot()
• The slice function is used to grab specific data points from a data frame
  • [start:stop:step]
  • data[100:200:2] would return every other row between rows 100 and 200
• Use matplotlib for prettier graphs to be used in presentations
• Data Inspection
  • data.to_clipboard() #copies data to clipboard to be pasted in excel
  • data.tail() #prints from the end of the data
  • data.head() #prints from the start of the data
• When adding the data to a chart you need to create a figure and set the size
  • fig2 = plt.figure(figsize=(12,8))
• Additionally you will need to define the axis in the figure. The below is creating a figure of 3 plots high and 1 plot wide. The last 1 reflects the plot that the code following this line would be updating
  • ax = fig2.add_subplot(311)
• Create a heat map when comparing correlations of more than 2 markets

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