Frequency resampling primer

Risk factor models often rely upon data that is of different frequencies. A typical example is when using quarterly macroeconomic data, such as prices, unemployment rates, etc., with financial data, which is often daily (or even intra-daily). To use both data sources in the same model, higher frequency data needs to be resampled to match the lower frequency data.

The DataFrame and Series Pandas objects have a built-in .resample() method that specifies the lower frequency. This method is chained with a method to create the lower-frequency statistic, such as .mean() for the average of the data within the new frequency period, or .min() for the minimum of the data.

In this exercise you'll practice converting daily returns data to weekly and quarterly frequency.

Convert returns to quarterly frequency average returns_q using the .resample() and .mean() methods.
Examine the header of returns_q, noting that the .resample() method takes care of the date index for you.
Now convert returns to weekly frequency minimum returns_w, using the .min() method.
Examine the header of returns_w.

Exercise

Frequency resampling primer

Instructions