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)