def make_simple_equity_info(sids, start_date, end_date, symbols=None):
 num_assets = len(sids)
 if (symbols is None):
  symbols = list(ascii_uppercase[:num_assets])
 return pd.DataFrame({'symbol': list(symbols), 'start_date': pd.to_datetime(([start_date] * num_assets)), 'end_date': pd.to_datetime(([end_date] * num_assets)), 'exchange': 'TEST', 'exchange_full': 'TEST FULL'}, index=sids, columns=('start_date', 'end_date', 'symbol', 'exchange', 'exchange_full'))

def make_simple_equity_info ( sids , start_date , end_date , symbols = None ) : num_assets = len ( sids ) if ( symbols is None ) : symbols = list ( ascii_uppercase [ : num_assets ] ) return pd . DataFrame ( { 'symbol' : list ( symbols ) , 'start_date' : pd . to_datetime ( ( [ start_date ] * num_assets ) ) , 'end_date' : pd . to_datetime ( ( [ end_date ] * num_assets ) ) , 'exchange' : 'TEST' , 'exchange_full' : 'TEST FULL' } , index = sids , columns = ( 'start_date' , 'end_date' , 'symbol' , 'exchange' , 'exchange_full' ) )
create a dataframe representing assets that exist for the full duration between start_date and end_date .