This is an example of how to conduct an event study in Quantopian.
FYI Anyone who lands on this page. There's an event study built into AlphaLens.
"""
create_full_tear_sheet(factor_data, long_short=False, group_neutral=False, by_group=False)
create_event_returns_tear_sheet(factor_data, prices, avgretplot=(3, 11),
long_short=False, group_neutral=False, by_group=False)
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
each period, the factor quantile/bin that factor value belongs to,
and (optionally) the group the asset belongs to.
- See full explanation in utils.get_clean_factor_and_forward_returns
long_short : bool
Should this computation happen on a long short portfolio? if so, then
mean quantile returns will be demeaned across the factor universe.
Additionally factor values will be demeaned across the factor universe
when factor weighting the portfolio for cumulative returns plots
group_neutral : bool
Should this computation happen on a group neutral portfolio? if so,
returns demeaning will occur on the group level.
Additionally each group will weight the same in cumulative returns
plots
by_group : bool
If True, display graphs separately for each group.
"""
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