我使用excel来最小化变量，最近我开始使用cvxopt。我试图弄清楚在给定两个约束的情况下如何最小化一个值。我有两个return数据帧，并将权重w1和w2乘以return并减去它们。我发现通过更改权重来最小化回报差异的夏普比率。这里的约束是w1 = 1和sum of w2= 1
在Excel中，我使用solver添加并添加约束$S$4 = 1和$s$5= 1。我试图弄清楚如何在python cvxopt中做到这一点。以下是我为cvxopt写的创建高效边界的代码。我真的很感谢您的帮助。
'import numpy as np
import matplotlib.pyplot as plt
import cvxopt as opt
from cvxopt import blas, solvers
import pandas as pd'
`
def random_portfolio(returns1, returns2):
#Returns the mean and standard deviation of returns for a random portfolio
p1 = np.asmatrix(np.nanmean(returns1, axis=1))
w1 = np.asmatrix(rand_weights(returns1.shape[0]))
mu1 = w 1* p1.T
p2 = np.asmatrix(np.nanmean(returns2, axis=1))
w2 = np.asmatrix(rand_weights(returns2.shape[0]))
mu2 = w 1* p1.T
final = mu1- mu2
mean_ret = mean(final)
voltality = std(final)
sharpe = mean_ret/voltality
n = len(returns1)
G = -opt.matrix(np.eye(n)) # negative n x n identity matrix
h = opt.matrix(0.0, (n ,1))
A = opt.matrix(1.0, (1, n))
b = opt.matrix(1.0)
portfolios = solvers.qp(-sharpe, G, h, A, b)['x']
returns = [blas.dot(mu, x) for x in portfolios]
risks = [np.sqrt(blas.dot(x, C*x)) for x in portfolios]
return mean_ret, voltality, sharpe
`