def test_NetGroupExposure_1(self):
"""检验输入"""
with self.assertRaises(TypeError):
NetGroupExposure('a', 'b', 'c')
# with self.assertRaises(AssertionError):
# labels = {'AAPL': 'TECH', 'MSFT': 'TECH', 'TSLA': 'CC', 'GM': 'CC'}
# # 限定不平衡
# min_exposures = {'TECH': -0.5, 'CC': -0.25}
# max_exposures = {'TECH': 0.5}
# NetGroupExposure(labels, min_exposures, max_exposures)
with self.assertRaises(Exception):
labels = {'AAPL': 'TECH', 'MSFT': 'TECH', 'TSLA': 'CC', 'GM': 'CC'}
min_exposures = {'TECH': -0.5, 'CC': -0.25}
max_exposures = {'TECH': 0.5, 'CC': -0.5} # 最大值小于最小值
ne = NetGroupExposure(labels, min_exposures, max_exposures)
self.check(ne, 1, 1, 1)
def test_NetGroupExposure_4(self):
"""分组双边限制(下限为负，上限为正)"""
cons_obj = NetGroupExposure(self.labels, self.min_weights,
self.max_weights)
desired_value = 0.25
desired_weights = pd.Series(
[-0.2028, 0.1029, -0.284156, 0.084156, -0.062988, 0.3629, 0.4],
index=self.stocks)
self.check(cons_obj, desired_value, desired_weights)
def test_NetGroupExposure_3(self):
"""分组净空头单边限制"""
# 最大值负数
max_weights = pd.Series([-0.05] * 4, index=self.max_weights.keys())
cons_obj = NetGroupExposure(self.labels, self.min_weights, max_weights)
desired_value = 0.08
desired_weights = pd.Series(
[-0.2908, 0.1908, -0.3606, 0.1606, -0.2485, 0.1985, -0.05],
index=self.stocks)
self.check(cons_obj, desired_value, desired_weights)
def test_NetGroupExposure_2(self):
"""分组净多头单边限制"""
min_weights = pd.Series([0.0, 0.1, 0.2, 0.3],
index=self.min_weights.keys())
cons_obj = NetGroupExposure(self.labels, min_weights, self.max_weights)
desired_value = 0.21
# 精度累死
desired_weights = pd.Series(
[-0.1612, 0.1612, -0.1207, 0.2207, -0.068, 0.368, 0.400],
index=self.stocks)
self.check(cons_obj, desired_value, desired_weights)
def test_NetGroupExposure_5(self):
"""取消单组最大或最小限制"""
min_weights = pd.Series([-0.3, 0.1, 0., 0.3],
index=self.min_weights.keys())
max_weights = pd.Series([0.35] * 4, index=self.max_weights.keys())
# 取消A组最小限制
min_weights.loc['A'] = NotConstrained # 可多分配空头
# 取消C组最大限制
max_weights.loc['C'] = NotConstrained
cons_obj = NetGroupExposure(self.labels, min_weights, max_weights)
desired_value = 0.41
desired_weights = pd.Series([-1, -0.000197, -0., 0.1, 0, 0.0498, 0.35],
index=self.stocks)
self.check(cons_obj, desired_value, desired_weights)