Cross-sectional aggregation

Most signals in the Exabel platform are evaluated for entities, and produce one or more time series for each entity. With the cross-sectional aggregation it is possible to aggregate signals across a universe of entities.

cross_section()

cross_section(signal, method, universe=None)

Perform a cross-sectional analysis of a signal across a universe. Depending on the method, the signal may return one time series, or one time series per entity.

• Parameters:
  • signal (Signal) – the signal to perform the cross-sectional analysis on.
  • method (str) – the cross-sectional method; one of sum, median, mean, min, max, proportion, rank_ascending or rank_descending.
  • universe (str *|*list *[*str ] *|*None) – the optional entity universe, specified as an entity resource name, a tag resource name, or a list of those.

The universe can have a maximum of 10000 entities.

If a universe is not given, the signal is aggregated across the current evaluation entities.

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Note

The current evaluation entities are limited. In dashboards, the evaluation entities may be batched, so make sure to always set universe when using cross_section in dashboards. Plotter is limited to 20 evaluation entities.

When the method is one of rank_ascending, rank_descending or proportion, one time series is returned for each evaluation entity. The signal can then only be evaluated for entities which are also in the cross-sectional universe.

Examples:

Calculate the sum of the close price across a tag:

cross_section(Close_Price, "sum", "tags/user:abcdefg-1234-1234-1234-aabbccddee")

Calculate the average of FactSet sales of two companies:

cross_section(fs_actual('sales'), "mean",
    ["entityTypes/company/entities/F-ASDF-F", "entityTypes/company/entities/F-TROT-F"])

Calculate the average close price across the evaluation entities:

cross_section(Close_Price, "sum")