Hi - I’m playing with the sharadar datasets and trying to work through some of the code along blocks. I’ve got some things working pretty well - but i don’t understand some of the metrics being used in the fundamentals ones. It looks like the metrics being used are semi-static - i understand that EPS data for example is only updated quarterly, but things like MARKETCAP/EV/PRICE when querying quarterly fundamentals are only updated quarterly. I guess this is part of the reindex like forward fill that is happening - but it means in the code along that it is not updating for price changes when calculating daily metrics? Or am I missing something obvious? Can see what i mean in the code below from the code library - ev/ebit doesn’t change? Is there a better way than to recalculate the market cap every day from actual prices and the reported shares outstanding? Thanks for any understanding, i must be missing something.

from quantrocket.fundamental import get_sharadar_fundamentals_reindexed_like

# Request EV/EBIT. The dimension “ART” (= “As reported - trailing twelve months”) excludes restatements.

fundamentals = get_sharadar_fundamentals_reindexed_like(closes, fields=[“EVEBIT”], dimension=“ART”, domain=“sharadar”)

enterprise_multiples = fundamentals.loc[“EVEBIT”]

# Ignore negative enterprise multiples, which indicate negative earnings

enterprise_multiples = enterprise_multiples.where(enterprise_multiples > 0)

enterprise_multiples.tail()

ConId 113652 113738 113756 113760 113761 113764 113787 113810 113837 113892 … 199962 199972 199976 199977 199984 199989 199990 199997 199998 9198200

Date

2015-12-24 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN … 66.0 13.0 NaN 13.0 13.0 20.0 21.0 14.0 NaN 22.0

2015-12-28 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN … 66.0 13.0 NaN 13.0 13.0 20.0 21.0 14.0 NaN 22.0

2015-12-29 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN … 66.0 13.0 NaN 13.0 13.0 20.0 21.0 14.0 NaN 22.0

2015-12-30 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN … 66.0 13.0 NaN 13.0 13.0 20.0 21.0 14.0 NaN 22.0

2015-12-31 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN … 66.0 13.0 NaN 13.0 13.0 20.0 21.0 14.0 NaN