We have made two small additions to Pipeline:
PercentChange: a built-in
Factorthat calculates the percent change of an input over a given window length
peer_count: a method called on
Classifiersthat gives the number of occurrences of each distinct category in the parent
PercentChange takes any numeric
BoundColumn as its input, as well as a window length of at least 2. It computes the percent change between the value corresponding to the beginning of the lookback window and the current day. For example,
PercentChange with a window length of 2 would compute the daily percent change for all days in the specified range. This
Factor is virtually identical to the
CustomFactor shown here, but we now account for the use of negative numbers by taking the absolute value of the denominator in the percent change calculation:
(new_value - old_value) / abs(old_value)
You should proceed with caution when your data contains negative numbers, as the percent change formula can sometimes give counter-intuitive results
peer_count is a
Classifier method that counts, for each
Classifier category, the total number of assets that share the same category label.
Classifiers output categorical data, and it can be useful to not only know the category that an asset falls under, but also the total number of assets that belong to that category. For example, you might want to filter out outlier categories that contain a small number of assets. This
Filter, as well as a few other examples, are demonstrated in the attached notebook.
Thanks for reading!