def aggregate(self, table_name, pipeline):
"""
:param table_name:
:param pipeline: a list, 每一个元素相当于一个管道操作，常见的操作包括
匹配('$match')、属性域选择('$project')
:return:
"""
try:
cursor = BaseModel(table_name, self.location,
self.dbname).aggregate(pipeline)
# data = pd.DataFrame()
# if cursor.count():
data = pd.DataFrame(list(cursor))
cursor.close()
return data
except Exception as e:
ExceptionInfo(e)
return pd.DataFrame()
def read_data(cls, table_name, field=None, **kw):
"""
一个简易的数据读取接口
:param table_name:
:param field:
:param kw:
:return:
"""
try:
cursor = BaseModel(table_name, cls.location,
cls.dbname).query(kw, field)
data = pd.DataFrame()
if cursor.count():
data = pd.DataFrame(list(cursor))
except Exception as e:
ExceptionInfo(e)
finally:
cursor.close()
return data
def min(cls, table_name, field='_id', **kw):
"""
找到满足kw条件的field列上的最小值
:param table_name:
:param field:
:param kw:
:return:
"""
try:
if not isinstance(field, str):
raise TypeError('field must be an instance of str')
cursor = BaseModel(table_name, cls.location,
cls.dbname).query(sql=kw, field={field: True})
if cursor.count():
d = pd.DataFrame(list(cursor))
m = d.loc[:, [field]].min()[field]
else:
m = None
cursor.close()
return m
except Exception as e:
raise e
def lasted_ticker(cls, code, date, table_name='ticker'):
try:
if isinstance(code, str):
sc = code
elif isinstance(code, list):
sc = {'$in': code}
else:
raise TypeError("'code' must be str or list of str")
if isinstance(date, dt.datetime):
d = dt.datetime(date.year, date.month, date.day)
t = {'$gte': date - dt.timedelta(minutes=1), '$lte': date}
pass
else:
raise TypeError("this 'date' must be datetime")
cursor = BaseModel(table_name, cls.location,
cls.dbname).aggregate([{
'$match': {
'stock_code': sc,
'date': d
}
}, {
'$match': {
'datetime': t
}
}])
data = pd.DataFrame(list(cursor))
if len(data):
data = data.sort_values(['stock_code', 'datetime'],
ascending=False)
data = data.drop_duplicates(['stock_code'], keep='first')
data = data.reset_index(drop=True)
cursor.close()
return data
pass
except Exception as e:
ExceptionInfo(e)
return pd.DataFrame()
def read_data(cls,
code,
start_date,
end_date,
field=None,
timemerge=False,
**kw):
"""
:param field:
:param code:
:param start_date:
:param end_date:
:param timemerge:
:return:
"""
try:
sql = dict(stock_code=code,
date={
'$gte': start_date,
'$lte': end_date
})
sql = dict(sql, **kw)
cursor = BaseModel('kline_tick', cls.location,
cls.dbname).query(sql, field)
if cursor.count():
data = pd.DataFrame(list(cursor))
data = cls.merge_time(data) if timemerge else data
cursor.close()
return data
else:
cursor.close()
return pd.DataFrame()
except Exception as e:
ExceptionInfo(e)
return pd.DataFrame()
