Updated with a much more memory efficient version that will run across a longer timespan.
I've taken some of your changes along with the original framework and tried cleaning up the code quite a bit. I've also included a new, more efficient way of calculating price returns by using a cumulative price change versus individual (x - y)/x calculations.
To run it you call
run_event_study with these parameters:
def run_event_study(event_data, date_column='asof_date',
benchmark_sid='SPY', days_before=10, days_after=10, top_liquid=500,
Calculates simple & cumulative returns for events and plots stock price movement
before and after the event date.
event_data : pd.DataFrame
DataFrame that contains the events data with date and sid columns as
a minimum. See interactive tutorials on quantopian.com/data
date_column : String
String that labels the date column to be used for the event. e.g. `asof_date`
start_date, end_date : Datetime
Start and end date to be used for the cutoff for the evenet study
benchmark : string, int, zipline.assets._assets.Equity object
Security to be used as benchmark for returns calculations. See `get_returns`
days_before, days_after : int
Days before/after to be used to calculate returns for.
top_liquid : Int
If use_liquid_stocks is True, top_liquid determines the top X amount of stocks
to return ranked on liquidity
use_liquid_stocks : Boolean
If set to True, it will filter out any securities found in `event_data`
according to the filters found in `filter_universe`
While I'll either be updating this thread or creating a new one to feature the notebook, here it is looking at Buyback Announcements.
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