Performing financial analytics on time series data

In this section, we will visualize some statistical properties of time series data used in financial analytics.

Plotting returns

One of the classic measures of security performance is its returns over a prior period. A simple method for calculating returns in pandas is pct_change, where the percentage change from the previous row is computed for every row in the DataFrame.

In the following example, we use ABN stock data to plot a simple graph of daily percentage returns:

In [ ]:
     %matplotlib inline
     import quandl

     quandl.ApiConfig.api_key = QUANDL_API_KEY
     df = quandl.get('EURONEXT/ABN.4')
     daily_changes = df.pct_change(periods=1)
     daily_changes...