Lag

Lag shifts your features on a partition index, creating a lookback feature offset by an amount. Lag supports generating multiple lags in one transform by generating each unique combination of columns and amounts from your inputs.
Parameters
Name
Type
Description
Is Optional
columns
column_list
names of column(s) you want to lag
​
amounts
int_list
Magnitude of amounts you want to use for the lag. Positive values result in a historical offset; negative amounts result in forward-looking offset.
​
partition
column_list
name of column(s) to partition by for the lag
True
order_by
column_list
name of column(s) to order by in the final data set
True
Example
ds = rasgo.get.dataset(id)
​
ds2 = ds.lag(columns=['OPEN', 'CLOSE'], amounts=[1,2,3,7], order_by=['DATE, 'TICKER'], partition=['TICKER'])
ds2.preview()
Source Code
RasgoTransforms/lag.sql at main · rasgointelligence/RasgoTransforms
GitHub

Name	Type	Description	Is Optional
columns	column_list	names of column(s) you want to lag
amounts	int_list	Magnitude of amounts you want to use for the lag. Positive values result in a historical offset; negative amounts result in forward-looking offset.
partition	column_list	name of column(s) to partition by for the lag	True
order_by	column_list	name of column(s) to order by in the final data set	True

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