def test__get_getNumpy(self):
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end)
self.assertIsInstance(df, pd.DataFrame, "应返回类型：pd.DataFrame，实际返回数据类型：{}".format(type(df)))
arrary = qm.getNumpy(code, start, end)
self.assertIsInstance(arrary, np.ndarray, "应返回类型：np.ndarray，实际返回数据类型：{}".format(type(arrary)))
self.assertTrue(len(arrary) > days // 10, "返回数据数量应该大于0。")
def test_get_noData(self):
code = '600001' # 不存在的股票代码
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end)
self.assertTrue(df is None, "{}已退市，2020年返回数据数量应该等于0,{}。".format(code, df))
def test_getMin(self):
code = '000001'
days = 30 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end, frequence='1min')
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
self.assertTrue(len(df[df['close'] > 1000]) < days // 10, "大部分股票价格小于1000")
print(df.tail(10))
def test_get(self):
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end)
self.assertIsInstance(df, pd.DataFrame,"应返回类型：pd.DataFrame，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.tail())
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_Stock_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_min_stock(self):
code = '000001'
days = 10 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end, frequence='1min')
if df is not None and len(df.index.names) == 1:
df = df.set_index(['date', 'code'], drop=True)
ds = qd(frequence=8).dataStruct(df)
ds2 = QA_DataStruct_Stock_min(df)
self.assertIsInstance(ds, QA_DataStruct_Stock_min)
self.assertIsInstance(ds2, QA_DataStruct_Stock_min)
def test_data_struct_stock(self):
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end)
if df is not None:
df = df.set_index(['date', 'code'], drop=True)
ds = qd().dataStruct(df)
ds2 = QA_DataStruct_Stock_day(df)
self.assertIsInstance(ds, QA_DataStruct_Stock_day)
self.assertIsInstance(ds2, QA_DataStruct_Stock_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_stock_day_adv(code, start, end)
self.assertTrue(len(df) == len(df2), "和QA返回的数据,长度不一致")
# 两种方式检测numpy数据一致性
obo = self.differOneByOne(df.data, df2.data)
self.assertTrue(np.array_equal(df, df2), "和QA返回的数据不一致{}".format(obo))
def _get_diffQA(self, code, days):
start = (datetime.datetime.now() - datetime.timedelta(days)).date()
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.get(code, start, end)
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
df2 = QA_fetch_stock_day(code, start, end, format='pd')
# todo 全自动运行时会报错。单独测试不会报错？？
if len(df) !=len(df2):
print("_get_diffQA : {} {} {} {}".format(start, end,df.head(), df2.head()))
print('columns:\n {}\n{}'.format(df.columns, df2.columns))
print(df.equals(df2))
else:
print('数据长度{}'.format(len(df)))
self.assertTrue(len(df) == len(df2), "和QA返回的数据,长度不一致{}:{}".format(len(df), len(df2)))
# 两种方式检测DataFrame数据一致性
obo = self.differOneByOne(df2, df)
self.assertTrue(df.equals(df2), "和QA返回的数据不一致{}".format(obo))
return df
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_stock_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))