def make_rotating_equity_info(num_assets, first_start, frequency, periods_between_starts, asset_lifetime):
 return pd.DataFrame({'symbol': [chr((ord('A') + i)) for i in range(num_assets)], 'start_date': pd.date_range(first_start, freq=(periods_between_starts * frequency), periods=num_assets), 'end_date': pd.date_range((first_start + (asset_lifetime * frequency)), freq=(periods_between_starts * frequency), periods=num_assets), 'exchange': 'TEST', 'exchange_full': 'TEST FULL'}, index=range(num_assets))

def make_rotating_equity_info ( num_assets , first_start , frequency , periods_between_starts , asset_lifetime ) : return pd . DataFrame ( { 'symbol' : [ chr ( ( ord ( 'A' ) + i ) ) for i in range ( num_assets ) ] , 'start_date' : pd . date_range ( first_start , freq = ( periods_between_starts * frequency ) , periods = num_assets ) , 'end_date' : pd . date_range ( ( first_start + ( asset_lifetime * frequency ) ) , freq = ( periods_between_starts * frequency ) , periods = num_assets ) , 'exchange' : 'TEST' , 'exchange_full' : 'TEST FULL' } , index = range ( num_assets ) )
create a dataframe representing lifetimes of assets that are constantly rotating in and out of existence .