def SU_save_stock_block(client=ms.client):
client.flyshare.drop_collection('stock_block')
__coll = client.flyshare.stock_block
__coll.create_index('code')
__err = []
try:
util_log_info('##JOB09 Now Saving STOCK_BlOCK ====')
__coll.insert_many(
util_to_json_from_pandas(fetch_get_stock_block('tdx')))
__coll.insert_many(
util_to_json_from_pandas(fetch_get_stock_block('ths')))
except:
pass
def __saving_work(code, __coll):
util_log_info('##JOB07 Now Saving ETF_MIN ==== %s' % (str(code)))
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = __coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
else:
start_time = '2015-01-01'
util_log_info(
'##JOB07.%s Now Saving %s from %s to %s ==%s ' %
(['1min', '5min', '15min', '30min', '60min'
].index(type), str(code), start_time, end_time, type))
if start_time != end_time:
__data = fetch_get_index_min(str(code), start_time,
end_time, type)
if len(__data) > 1:
__coll.insert_many(
util_to_json_from_pandas(__data[1::]))
except:
__err.append(code)
def fetch_get_stock_info(name):
data = QATs.get_stock_basics()
data_json = util_to_json_from_pandas(data)
for i in range(0, len(data_json) - 1, 1):
data_json[i]['code'] = data.index[i]
return data_json
def __saving_work(code, __coll):
util_log_info('##JOB02 Now Saving XDXR INFO ==== %s' % (str(code)))
try:
__coll.insert_many(
util_to_json_from_pandas(fetch_get_stock_xdxr(str(code))))
except:
__err.append(str(code))
def save_stock_day(code, start, end, coll):
util_log_info('##JOB01 Now Updating STOCK_DAY==== %s' % (str(code)))
data = util_to_json_from_pandas(
fetch_get_stock_day(str(code), start, end, '00'))
if len(data) > 0:
coll.insert_many(data)
else:
pass
def __get_index_day_data(self, __bid):
__data = util_to_json_from_pandas(
fetch_index_day(
str(__bid.code)[0:6],
str(__bid.datetime)[0:10],
str(__bid.datetime)[0:10], 'pd'))
if len(__data) == 0:
pass
else:
__data = __data[0]
return __data
def __saving_work(code):
util_log_info('##JOB10 Now Saving STOCK_TRANSACTION ==== %s' %
(str(code)))
try:
__coll.insert_many(
util_to_json_from_pandas(
fetch_get_stock_transaction(str(code),
str(__stock_list[code]),
str(now_time())[0:10])))
except:
__err.append(str(code))
def SU_save_stock_list(client=ms.client):
client.flyshare.drop_collection('stock_list')
__coll = client.flyshare.stock_list
__coll.create_index('code')
__err = []
try:
util_log_info('##JOB08 Now Saving STOCK_LIST ====')
__coll.insert_many(util_to_json_from_pandas(fetch_get_stock_list()))
except:
pass
def saving_work(i):
util_log_info('Now Saving ==== %s' % (i))
try:
data_hfq = fetch_get_stock_day(i,
startDate='1990-01-01',
if_fq='02',
type_='pd')
data_json = util_to_json_from_pandas(data_hfq)
__coll.insert_many(data_json)
except:
util_log_info('error in saving ==== %s' % str(i))
def __get_data(__bid):
'隔离掉引擎查询数据库的行为'
__data = util_to_json_from_pandas(
fetch_stock_day(
str(__bid.code)[0:6],
str(__bid.date)[0:10],
str(__bid.date)[0:10], 'pd'))
if len(__data) == 0:
pass
else:
__data = __data[0]
return __data
def fetch_get_trade_date(endDate, exchange):
data = QATs.trade_cal()
da = data[data.isOpen > 0]
data_json = util_to_json_from_pandas(data)
message = []
for i in range(0, len(data_json) - 1, 1):
date = data_json[i]['calendarDate']
num = i + 1
exchangeName = 'SSE'
data_stamp = util_date_stamp(date)
mes = {
'date': date,
'num': num,
'exchangeName': exchangeName,
'date_stamp': data_stamp
}
message.append(mes)
return message
def __saving_work(code, coll_stock_day):
try:
util_log_info('##JOB01 Now Saving STOCK_DAY==== %s' % (str(code)))
ref = coll_stock_day.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
if ref.count() > 0:
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
start_date = ref[ref.count() - 1]['date']
else:
start_date = '1990-01-01'
util_log_info(
' UPDATE_STOCK_DAY \n Trying updating %s from %s to %s' %
(code, start_date, end_date))
if start_date != end_date:
coll_stock_day.insert_many(
util_to_json_from_pandas(
fetch_get_stock_day(str(code), start_date, end_date,
'00')[1::]))
except:
__err.append(str(code))
def __saving_work(code, __coll):
try:
ref_ = __coll.find({'code': str(code)[0:6]})
end_time = str(now_time())[0:10]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['date']
else:
start_time = '1990-01-01'
util_log_info(
'##JOB06 Now Saving ETF_DAY==== \n Trying updating %s from %s to %s'
% (code, start_time, end_time))
if start_time != end_time:
__coll.insert_many(
util_to_json_from_pandas(
fetch_get_index_day(str(code), start_time,
end_time)[1::]))
except:
__err.append(str(code))
def fetch_get_stock_day(name,
startDate='',
endDate='',
if_fq='01',
type_='json'):
if (len(name) != 6):
name = str(name)[0:6]
if str(if_fq) in ['qfq', '01']:
if_fq = 'qfq'
elif str(if_fq) in ['hfq', '02']:
if_fq = 'hfq'
elif str(if_fq) in ['bfq', '00']:
if_fq = 'bfq'
else:
util_log_info('wrong with fq_factor! using qfq')
if_fq = 'qfq'
data = QATs.get_k_data(str(name),
startDate,
endDate,
ktype='D',
autype=if_fq,
retry_count=200,
pause=0.005).sort_index()
data['date_stamp'] = data['date'].apply(lambda x: util_date_stamp(x))
data['fqtype'] = if_fq
if type_ in ['json']:
data_json = util_to_json_from_pandas(data)
return data_json
elif type_ in ['pd', 'pandas', 'p']:
data['date'] = pd.to_datetime(data['date'])
data = data.set_index('date', drop=False)
data['date'] = data['date'].apply(lambda x: str(x)[0:10])
return data
def to_json(self):
return util_to_json_from_pandas(self.data)
def fetch_backtest_info(user=None, account_cookie=None, strategy=None, stock_list=None, collections=ms.client.flyshare.backtest_info):
return util_to_json_from_pandas(pd.DataFrame([item for item in collections.find(util_to_json_from_pandas(pd.DataFrame([user, account_cookie, strategy, stock_list], index=['user', 'account_cookie', 'strategy', 'stock_list']).dropna().T)[0])]).drop(['_id'], axis=1))
def fetch_backtest_history(cookie=None, collections=ms.client.flyshare.backtest_history):
return util_to_json_from_pandas(pd.DataFrame([item for item in collections.find(util_to_json_from_pandas(pd.DataFrame([cookie], index=['cookie']).dropna().T)[0])]).drop(['_id'], axis=1))
def from_dataframe(self, dataframe):
bid_list = []
for item in util_to_json_from_pandas(dataframe):
bid_list.append(self.from_dict(item))
return bid_list
def from_dataframe(self, dataframe):
self.list = [
MarketBid().from_dict(item)
for item in util_to_json_from_pandas(dataframe)
]
return self.list
def fetch_get_stock_realtime():
data = QATs.get_today_all()
data_json = util_to_json_from_pandas(data)
return data_json
