def bat(timeout):
_time1 = datetime.datetime.now()
from QUANTAXIS.QAFetch.QAQuery_Advance import QA_fetch_stock_block_adv
code = QA_fetch_stock_block_adv().code
print(len(code))
x = QA_Tdx_Executor(timeout=timeout)
print(x._queue.qsize())
print(x.get_available())
database = DATABASE.get_collection('realtime_{}'.format(
datetime.date.today()))
print(database)
database.create_index([('code', QA_util_sql_mongo_sort_ASCENDING),
('datetime', QA_util_sql_mongo_sort_ASCENDING)])
for i in range(100000):
_time = datetime.datetime.now()
if QA_util_if_tradetime(_time): # 如果在交易时间
data = x.get_realtime_concurrent(code)
data[0]['datetime'] = data[1]
x.save_mongo(data[0])
print('Time {}'.format(
(datetime.datetime.now() - _time).total_seconds()))
time.sleep(1)
print('Connection Pool NOW LEFT {} Available IP'.format(
x._queue.qsize()))
print('Program Last Time {}'.format(
(datetime.datetime.now() - _time1).total_seconds()))
else:
print('Not Trading time {}'.format(_time))
time.sleep(1)
def run(self):
sleep = int(self.sleep_time)
_time1 = datetime.datetime.now()
database = DATABASE.get_collection('realtime_{}'.format(
datetime.date.today()))
database.create_index([('code', QA_util_sql_mongo_sort_ASCENDING)])
database.create_index([('datetime', QA_util_sql_mongo_sort_ASCENDING)])
from QUANTAXIS.QAFetch.QAQuery_Advance import QA_fetch_stock_block_adv
code = QA_fetch_stock_block_adv().code
while True:
_time = datetime.datetime.now()
if QA_util_if_tradetime(_time): # 如果在交易时间
data = x.get_realtime_concurrent(code)
data[0]['datetime'] = data[1]
x.save_mongo(data[0])
print('Time {}'.format(
(datetime.datetime.now() - _time).total_seconds()))
time.sleep(sleep)
print('Connection Pool NOW LEFT {} Available IP'.format(
x._queue.qsize()))
print('Program Last Time {}'.format(
(datetime.datetime.now() - _time1).total_seconds()))
else:
print('Not Trading time {}'.format(_time))
time.sleep(sleep)
def bat():
_time1 = datetime.datetime.now()
from QUANTAXIS.QAFetch.QAQuery_Advance import QA_fetch_stock_block_adv
code = QA_fetch_stock_block_adv().code
print(len(code))
x = QA_Tdx_Executor()
print(x._queue.qsize())
print(x.get_available())
database = DATABASE.get_collection(
'realtime_{}'.format(datetime.date.today()))
print(database)
database.create_index([('code', QA_util_sql_mongo_sort_ASCENDING),
('datetime', QA_util_sql_mongo_sort_ASCENDING)])
for i in range(100000):
_time = datetime.datetime.now()
if QA_util_if_tradetime(_time): # 如果在交易时间
data = x.get_realtime_concurrent(code)
data[0]['datetime'] = data[1]
x.save_mongo(data[0])
print('Time {}'.format(
(datetime.datetime.now() - _time).total_seconds()))
time.sleep(1)
print('Connection Pool NOW LEFT {} Available IP'.format(
x._queue.qsize()))
print('Program Last Time {}'.format(
(datetime.datetime.now() - _time1).total_seconds()))
else:
print('Not Trading time {}'.format(_time))
time.sleep(1)
def get(self):
try:
code = self.get_argument('code', default='000001')[0:6]
database = DATABASE.get_collection(
'realtime_{}'.format(datetime.date.today()))
current = [QA_util_dict_remove_key(item, '_id') for item in database.find({'code': code}, limit=1, sort=[
('datetime', pymongo.DESCENDING)])]
self.write(current[0])
except:
self.write('wrong')
def bat(timeout=0.2, sleep=1, thread=2, size=80):
sleep = int(sleep)
_time1 = datetime.datetime.now()
from QUANTAXIS.QAFetch.QAQuery_Advance import QA_fetch_stock_block_adv
code = QA_fetch_stock_block_adv().code
print(len(code))
x = QA_Tdx_Executor(timeout=float(timeout),
thread_num=int(thread),
max_size=int(size))
print(x._queue.qsize())
print(x.get_available())
database = DATABASE.get_collection('realtime_{}'.format(
datetime.date.today()))
print(database)
# mongodb在排序时，复合索引并不能像关系型数据库一样提升效率，反而会降低排序性能
# 在测试的过程，发现datetime倒序索引在倒序查询时性能更高，但写入性能较低
# 此表属于写多读少，所以索引继续采用正序
database.create_index([('code', QA_util_sql_mongo_sort_ASCENDING)])
database.create_index([('datetime', QA_util_sql_mongo_sort_ASCENDING)])
while True:
_time = datetime.datetime.now()
if QA_util_if_tradetime(_time): # 如果在交易时间
data = x.get_realtime_concurrent(code)
data[0]['datetime'] = data[1]
x.save_mongo(data[0])
print('Time {}'.format(
(datetime.datetime.now() - _time).total_seconds()))
time.sleep(sleep)
print('Connection Pool NOW LEFT {} Available IP'.format(
x._queue.qsize()))
print('Program Last Time {}'.format(
(datetime.datetime.now() - _time1).total_seconds()))
else:
print('Not Trading time {}'.format(_time))
time.sleep(sleep)
def bat(timeout=0.2, sleep=1):
sleep = int(sleep)
_time1 = datetime.datetime.now()
from QUANTAXIS.QAFetch.QAQuery_Advance import QA_fetch_stock_block_adv
code = QA_fetch_stock_block_adv().code
print(len(code))
x = QA_Tdx_Executor(timeout=float(timeout))
print(x._queue.qsize())
print(x.get_available())
database = DATABASE.get_collection(
'realtime_{}'.format(datetime.date.today()))
print(database)
# mongodb在排序时，复合索引并不能像关系型数据库一样提升效率，反而会降低排序性能
# # 在测试的过程，发现datetime倒序索引在倒序查询时性能更高，但写入性能较低
# # 此表属于写多读少，所以索引继续采用正序
database.create_index([('code', QA_util_sql_mongo_sort_ASCENDING)])
database.create_index([('datetime', QA_util_sql_mongo_sort_ASCENDING)])
while True:
_time = datetime.datetime.now()
if QA_util_if_tradetime(_time): # 如果在交易时间
data = x.get_realtime_concurrent(code)
data[0]['datetime'] = data[1]
x.save_mongo(data[0])
print('Time {}'.format(
(datetime.datetime.now() - _time).total_seconds()))
time.sleep(sleep)
print('Connection Pool NOW LEFT {} Available IP'.format(
x._queue.qsize()))
print('Program Last Time {}'.format(
(datetime.datetime.now() - _time1).total_seconds()))
else:
print('Not Trading time {}'.format(_time))
time.sleep(sleep)
def save_mongo(self, data, client=DATABASE):
database = DATABASE.get_collection('realtime_{}'.format(
datetime.date.today()))
database.insert_many(QA_util_to_json_from_pandas(data))
def save_mongo(self, data, client=DATABASE):
database = DATABASE.get_collection(
'realtime_{}'.format(datetime.date.today()))
database.insert_many(QA_util_to_json_from_pandas(data))
from QUANTAXIS.QAFetch.QATdx import QA_fetch_get_stock_list
from QUANTAXIS.QAFetch.QATdx_adv import QA_Tdx_Executor
from QUANTAXIS.QAUtil.QADate_trade import QA_util_if_tradetime
from QUANTAXIS.QAUtil.QASetting import DATABASE
from QUANTAXIS.QAUtil.QASql import (QA_util_sql_mongo_sort_ASCENDING,
QA_util_sql_mongo_sort_DESCENDING)
_time1 = datetime.datetime.now()
code = QA_fetch_get_stock_list().code.tolist()
print(len(code))
x = QA_Tdx_Executor()
print(x._queue.qsize())
print(x.get_available())
database = DATABASE.get_collection('realtime_{}'.format(datetime.date.today()))
print(database)
database.create_index([('code', QA_util_sql_mongo_sort_ASCENDING),
('datetime', QA_util_sql_mongo_sort_ASCENDING)])
for i in range(100000):
_time = datetime.datetime.now()
if QA_util_if_tradetime(_time): # 如果在交易时间
data = x.get_realtime_concurrent(code)
data[0]['datetime'] = data[1]
x.save_mongo(data[0])
print('Time {}'.format(
(datetime.datetime.now() - _time).total_seconds()))
