def make_jagged_equity_info(num_assets, start_date, first_end, frequency, periods_between_ends, auto_close_delta):
 frame = pd.DataFrame({'symbol': [chr((ord('A') + i)) for i in range(num_assets)], 'start_date': start_date, 'end_date': pd.date_range(first_end, freq=(periods_between_ends * frequency), periods=num_assets), 'exchange': 'TEST', 'exchange_full': 'TEST FULL'}, index=range(num_assets))
 if (auto_close_delta is not None):
  frame['auto_close_date'] = (frame['end_date'] + auto_close_delta)
 return frame

def make_jagged_equity_info ( num_assets , start_date , first_end , frequency , periods_between_ends , auto_close_delta ) : frame = pd . DataFrame ( { 'symbol' : [ chr ( ( ord ( 'A' ) + i ) ) for i in range ( num_assets ) ] , 'start_date' : start_date , 'end_date' : pd . date_range ( first_end , freq = ( periods_between_ends * frequency ) , periods = num_assets ) , 'exchange' : 'TEST' , 'exchange_full' : 'TEST FULL' } , index = range ( num_assets ) ) if ( auto_close_delta is not None ) : frame [ 'auto_close_date' ] = ( frame [ 'end_date' ] + auto_close_delta ) return frame
create a dataframe representing assets that all begin at the same start date .