def test_maximizeAlpha_3(self):
"""限定单个权重值"""
obj = MaximizeAlpha(self.alphas)
cvx_objective = obj.to_cvxpy(None)
w = obj.new_weights
constraints = [
cvx.abs(w) <= 0.2,
]
prob = cvx.Problem(cvx_objective, constraints)
desired_value = 0.18
desired_weights = pd.Series(
[0.2, 0.2, 0.2, -0.2, -0.2], index=self.stocks)
self.check(prob, obj, desired_value, desired_weights)
def test_maximizeAlpha_2(self):
"""限定空头权重和为1，不允许多头"""
obj = MaximizeAlpha(self.alphas)
cvx_objective = obj.to_cvxpy(None)
w = obj.new_weights
constraints = [
cvx.sum(cvx.abs(w)) <= 1.0,
w <= 0.0,
]
prob = cvx.Problem(cvx_objective, constraints)
desired_value = 0.3
desired_weights = pd.Series([0, 0., 0., 0, -1.0], index=self.stocks)
self.check(prob, obj, desired_value, desired_weights)
def test_maximizeAlpha_4(self):
"""限定总权重值，且单个空头权重不得小于-0.2，即最大空头绝对值不得超过0.2"""
obj = MaximizeAlpha(self.alphas)
cvx_objective = obj.to_cvxpy(None)
w = obj.new_weights
constraints = [
cvx.sum(cvx.abs(w)) <= 1.0,
w >= -0.2,
]
prob = cvx.Problem(cvx_objective, constraints)
desired_value = 0.22
desired_weights = pd.Series(
[0., 0.4, 0.4, -0., -0.2], index=self.stocks)
self.check(prob, obj, desired_value, desired_weights)
def test_maximizeAlpha_5(self):
"""
单纯限定总权重，而不限定多头或空头值
"""
obj = MaximizeAlpha(self.alphas)
cvx_objective = obj.to_cvxpy(None)
w = obj.new_weights
constraints = [
cvx.sum(cvx.abs(w)) <= 1.0,
# w >= -0.2,
]
prob = cvx.Problem(cvx_objective, constraints)
desired_value = 0.30
desired_weights = pd.Series([0., 0., 0., -0., -1.], index=self.stocks)
self.check(prob, obj, desired_value, desired_weights)
def test_maximizeAlpha_6(self):
"""
如果限定总权重，且限定多头及多头权重值
最会依次取正收益，直至权重和为限定值
"""
obj = MaximizeAlpha(self.alphas)
cvx_objective = obj.to_cvxpy(None)
w = obj.new_weights
constraints = [
cvx.sum(cvx.abs(w)) <= 1.0,
w >= 0,
]
prob = cvx.Problem(cvx_objective, constraints)
desired_value = 0.20
desired_weights = pd.Series(
[0., 0.5, 0.5, -0., -0.], index=self.stocks)
self.check(prob, obj, desired_value, desired_weights)
def check(self, cons_obj, desired_value, desired_weights, init_w_s=None):
# 基础限制：单个权重不超过0.5，空头不超过-1.0，总体不超过1.5
obj = MaximizeAlpha(self.alphas)
cvx_objective = obj.to_cvxpy(None)
w = obj.new_weights
w_s = obj.new_weights_series
constraints = [con for con in cons_obj.to_cvxpy(w, w_s, init_w_s)]
constraints += [cvx.norm(w, 1) <= 1.5, w <= 0.5, w >= -1.0]
prob = cvx.Problem(cvx_objective, constraints)
prob.solve()
# print('求解状态', prob.status)
# print('最优解', prob.value)
# print('权重序列\n', obj.new_weights_value)
# print('初始权重序列\n', init_w_s)
self.assertAlmostEqual(desired_value, prob.value, 2)
assert_series_equal(obj.new_weights_value,
desired_weights,
check_exact=False,
check_less_precise=3) # 精度：小数点后3位