def collect_train_data(begin,end): conn,cursor = db.db_connect() for market in config.MARKETS: code_list = stock.get_stock_by_market(market,cursor) for code in code_list: collect_stock_train_data(begin,end,code,market,cursor) conn.commit() db.db_close(conn,cursor)
def collect_price(begin, end): conn, cursor = db.db_connect() enddate = datetime.datetime(int(end[0:4]), int(end[5:7]), int(end[8:10])) begindate = datetime.datetime(int(begin[0:4]), int(begin[5:7]), int(begin[8:10])) begin = begindate.strftime('%Y%m%d') end = enddate.strftime('%Y%m%d') for market in config.MARKETS: code_list = stock.get_stock_by_market(market, cursor) for code in code_list: if market == 'sh': download_price_file(begin, end, code, 0) else: download_price_file(begin, end, code, 1) read_price_file(code + '.csv', market, cursor) os.remove(code + '.csv') conn.commit() db.db_close(conn, cursor)
p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) for line in p.stdout.readlines(): #don't need these optimazation info. if line.find('could speed up CPU computations') < 0: print line, retval = p.wait() if __name__ == '__main__': conn, cursor = db.db_connect() today = datetime.date.today().strftime("%Y-%m-%d") if len(sys.argv) == 1: date = today if len(sys.argv) == 2 and sys.argv[1] != None: date = sys.argv[1] for market in config.MARKETS: code_list = stock.get_stock_by_market(market, cursor) pool = multiprocessing.Pool(processes=config.EST_PROCESS_NUM) for i in xrange(len(code_list)): code = code_list[i] command = 'python -m daily.estimate.est' + ' ' + code + ' ' + str( 30) + ' ' + '30' + ' ' + date pool.apply_async(start_est_process, (command, )) pool.close() pool.join() db.db_close(conn, cursor)