def test_getAdv(self):
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.getAdv(code, start, end)
self.assertIsInstance(
df, QA_DataStruct_Index_day,
"应返回类型：QA_DataStruct_Stock_day，实际返回数据类型：{}".format(type(df)))
self.assertTrue(len(df) > days // 10, "返回数据数量应该大于0。")
print(df.data.tail())
def test_getMin_datetimestr(self):
code = '000001'
days = 30 * 1.2
start = str(datetime.datetime.now() - datetime.timedelta(days))[:10]
end = str(datetime.datetime.now() - datetime.timedelta(0))[:10]
df = qm.get(code, start, end, frequence='1min')
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
print(df.tail(10))
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df2 = qm.get(code, start, end, frequence='1min')
# str化后的时间为开盘时间，
self.assertTrue(len(df) >= len(df2))
data1, data2 = df, df2
if len(data1) > len(data2):
data1 = data1[-len(data2):]
print("array1f的长度比array2长")
elif len(data1) < len(data2):
data2 = data2[-len(data1):]
print("array2的长度比array1长")
self.assertTrue(data1.equals(data2), "截取相同长度后的数据应该相同")
def test_data_struct_index(self):
"""指数（或etf）DataStruture
"""
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qmi.get(code, start, end)
if df is not None:
df = df.set_index(['date', 'code'], drop=True)
ds = qd(type='index').dataStruct(df)
ds2 = QA_DataStruct_Index_day(df)
self.assertIsInstance(ds, QA_DataStruct_Index_day)
self.assertIsInstance(ds2, QA_DataStruct_Index_day)
def test_getAdv_diffQA(self):
"""和QA返回的数据对比一致性
"""
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.getAdv(code, start, end)
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
df2 = QA_fetch_index_day_adv(code, start, end)
self.assertTrue(
len(df.data) == len(df2.data),
"和QA返回的数据,长度不一致{} {}".format(len(df.data), len(df2.data)))
# 两种方式检测numpy数据一致性
obo = self.differOneByOne(df.data, df2.data)
self.assertTrue(df.data.equals(df2.data), "和QA返回的数据不一致{}".format(obo))
def test_getMin_diffQA(self):
code = '000001'
days = 20 * 1.2
start = str(datetime.datetime.now() - datetime.timedelta(days))
end = str(datetime.datetime.now() - datetime.timedelta(0))
df = qm.get(code, start, end, frequence='1min')
df2 = QA_fetch_index_min(code,
start,
end=end,
format='pd',
frequence='1min')
# todo df的长度比df2长。未找出原因
data1, data2 = df, df2
self.assertTrue(
len(data1) == len(data2),
"和QA返回的分钟线数据长度不一致:{}:{}".format(len(data1), len(data2)))
if len(data1) > len(data2):
print("array1f的长度比array2长")
data1 = data1[-len(data2):]
elif len(data1) < len(data2):
print("array2的长度比array1长")
data2 = data2[-len(data1):]
obo = self.differOneByOne(data1, data2)
self.assertTrue(data1.equals(data2), "和QA返回的分钟线数据不一致:{}".format(obo))