################################################################################# import good_downloadclass_barclays_aggregate as c1 p1 = c1.perform() directory_where_datafiles_are_downloaded = p1.execute(download_last_x_days) print 'OK', directory_where_datafiles_are_downloaded ################################################################################# ################################################################################# import config import os import good_readvalues_marketvalue_barclays_aggregate as ofile pclass = ofile.perform() newdata_dict = pclass.execute(download_last_x_days, directory_where_datafiles_are_downloaded) import datetime filedatetime = datetime.datetime.today() filedatetime_string = filedatetime.strftime('%Y%m%d%H%M%S') # Get path of savedjsonfile savedjsonfile = os.path.join(config.localdatafileoutputpath, 'dailyreturns-barclays-usaggregate.json') def create_saved_dict(p_savedjsonfile): import json
# -*- coding: cp1252 -*- ddir = 'C:\\Batches\\AutomationProjects\\Investment Strategy\\ETL\\Downloads\\Unprocessed\\barclays\\' download_last_x_days = -10 ################################################################################# import config import os import good_readvalues_marketvalue_barclays_aggregate as o #print config.localunprocessedfolder #localunprocessedfolderext = os.path.join(config.localunprocessedfolder,'barclays') p = o.perform() newdata_dict = p.execute(download_last_x_days, ddir) for key, value in sorted(newdata_dict.iteritems()): print value['currdate'], value['periodreturn']