假设我们有三个特征,每个特征有252个样本。这里的特征是三种不同股票的收益。目标是最大化总回报,即
我的问题是如何在CVXpy
优化问题中定义这个目标函数
程序的程序视图应如下所示:
import numpy as np
import cvxpy as cvx;
return1 = np.random.normal(loc=0.05, scale=0.25, size=252)
return2 = np.random.normal(loc=0.01, scale=0.2, size=252)
return3 = np.random.normal(loc= -0.05, scale=0.15, size=252)
returns = np.array([return1, return2, return3])
# number of stocks m is number of rows of returns
m = returns.shape[0]
# x variables (to be found with optimization)
x = cvx.Variable(m)
# portfolio return
portfolio_return = cvx.multiply(returns, x)
#objective function
objective = cvx.Minimize(-portfolio_return)
#constraints
constraints = [x >= 0, sum(x) == 1]
#use cvxpy to solve the objective
cvx.Problem(objective, constraints).solve()
#retrieve the weights of the optimized portfolio
x_values = x.value
print(x_values)
但是,它返回一个错误:
ValueError: Cannot broadcast dimensions (3, 252) (3,)
当我们将x表示为x = cvx.Variable(shape=(m,1))
时,我们会得到另一个错误
ValueError: The 'minimize' objective must resolve to a scalar.
Python版本:3.8.8
CVXPY版本:1.1.12
多亏了cvxpy社区,我重新设计了这个问题,现在运行起来没有错误
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