def QA_fetch_index_min_adv(code,
start,
end=None,
frequence='1min',
if_drop_index=False,
collections=DATABASE.index_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
__data = []
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
if isinstance(code, str):
for item in collections.find({
'code': str(code),
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': frequence
}):
__data.append([
str(item['code']),
float(item['open']),
float(item['high']),
float(item['low']),
float(item['close']),
float(item['vol']), item['datetime'], item['time_stamp'],
item['date']
])
__data = DataFrame(__data,
columns=[
'code', 'open', 'high', 'low', 'close',
'volume', 'datetime', 'time_stamp', 'date'
])
__data['datetime'] = pd.to_datetime(__data['datetime'])
return QA_DataStruct_Index_min(
__data.query('volume>1').set_index(['datetime', 'code'],
drop=if_drop_index))
elif isinstance(code, list):
return QA_DataStruct_Index_min(
pd.concat([
QA_fetch_index_min_adv(code_, start, end, frequence,
if_drop_index).data for code_ in code
]).set_index(['datetime', 'code'], drop=if_drop_index))
def QA_fetch_index_min_adv(code,
start,
end,
type_='1min',
if_drop_index=False,
collections=QA_Setting.client.quantaxis.index_min):
'获取股票分钟线'
if type_ in ['1min', '1m']:
type_ = '1min'
elif type_ in ['5min', '5m']:
type_ = '5min'
elif type_ in ['15min', '15m']:
type_ = '15min'
elif type_ in ['30min', '30m']:
type_ = '30min'
elif type_ in ['60min', '60m']:
type_ = '60min'
__data = []
if isinstance(code, str):
for item in collections.find({
'code': str(code),
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': type_
}):
__data.append([
str(item['code']),
float(item['open']),
float(item['high']),
float(item['low']),
float(item['close']),
float(item['vol']), item['datetime'], item['time_stamp'],
item['date']
])
__data = DataFrame(__data,
columns=[
'code', 'open', 'high', 'low', 'close',
'volume', 'datetime', 'time_stamp', 'date'
])
__data['datetime'] = pd.to_datetime(__data['datetime'])
return QA_DataStruct_Index_min(
__data.query('volume>1').set_index(['datetime', 'code'],
drop=if_drop_index))
elif isinstance(code, list):
return QA_DataStruct_Index_min(
pd.concat([
QA_fetch_index_min_adv(code_, start, end, type_,
if_drop_index).data for code_ in code
]).set_index(['datetime', 'code'], drop=if_drop_index))
def QA_fetch_index_min_adv(code,
start,
end=None,
frequence='1min',
if_drop_index=False,
collections=DATABASE.index_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
__data = []
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
return QA_DataStruct_Index_min(
QA_fetch_index_min(code, start, end, format='pd',
frequence=frequence).set_index(['datetime', 'code'],
drop=if_drop_index))
def QA_fetch_index_min_adv(code,
start,
end=None,
frequence='1min',
if_drop_index=True,
collections=DATABASE.index_min):
'''
'获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return:
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
# __data = [] 没有使用
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_index_min_adv， 不支持start end是相等的
# print("QA Error QA_fetch_index_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (code, start, end))
# return None
res = QA_fetch_index_min(code,
start,
end,
format='pd',
frequence=frequence,
collections=collections)
if res is None:
print(
"QA Error QA_fetch_index_min_adv parameter code=%s start=%s end=%s frequence=%s call QA_fetch_index_min return None"
% (code, start, end, frequence))
else:
res_reset_index = res.set_index(['datetime', 'code'],
drop=if_drop_index)
# if res_reset_index is None:
# print("QA Error QA_fetch_index_min_adv set index 'date, code' return None")
return QA_DataStruct_Index_min(res_reset_index)
def get_lof_min(self, codelist, start, end):
codelist = promise_list(codelist)
# if 'XS' not in codelist[0]:
# codelist = pd.Series(codelist).apply(
# lambda x: x+'.XSHG' if x[0] != 6 else x+'.XSHE').tolist()
columns_raw = [
'datetime', 'order_book_id', 'open', 'high', 'low', 'close',
'volume', 'total_turnover'
]
res = self.client.query_dataframe(
"SELECT * FROM quantaxis.lof_cn_1min WHERE ((`datetime` >= '{}')) \
AND (`datetime` <= '{}') AND (`order_book_id` IN ({}))"
.format(start, end, "'{}'".format(
"','".join(codelist)))).loc[:, columns_raw].drop_duplicates(
['datetime', 'order_book_id'])
return QA_DataStruct_Index_min(
res.assign(
datetime=pd.to_datetime(res.datetime),
code=res.order_book_id,
amount=res.total_turnover,
type='1min',
).set_index(['datetime', 'code']).sort_index())
