【发布时间】：2021-01-08 11:58:34
【问题描述】：
我正在学习如何编写代码并选择了 Python。我的首要任务是优化投资组合以及 Efficient Frontier。我发现@s666 写了一段很棒的代码。与一般优化情况一样，约束如下：1) 权重之和 = 1，以及 2) 没有股票的权重 >1。
在下面显示的代码中，您能否告诉我如何添加第三个约束，其中考虑的 3 只股票除了上面提到的约束（即 0.2>AAPL
提前谢谢你....
import numpy as np
import pandas as pd
from pandas_datareader import data as d
import matplotlib.pyplot as plt
#list of stocks in portfolio
stocks = ['AAPL','AMZN','MSFT']#,'YHOO']
#download daily price data for each of the stocks in the portfolio
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2018, 12, 31)
data = pd.DataFrame([d.DataReader(ticker, 'yahoo', start, end)['Adj Close'] for ticker in stocks]).T
data.columns = stocks
#convert daily stock prices into daily returns
returns = data.pct_change()
#calculate mean daily return and covariance of daily returns
mean_daily_returns = returns.mean()
cov_matrix = returns.cov()
#set number of runs of random portfolio weights
num_portfolios = 25000
#set up array to hold results
#We have increased the size of the array to hold the weight values for each stock
results = np.zeros((3+len(stocks),num_portfolios))
for i in range(num_portfolios):
#select random weights for portfolio holdings
weights = np.array(np.random.random(len(stocks)))
#rebalance weights to sum to 1
weights /= np.sum(weights)
#calculate portfolio return and volatility
portfolio_return = np.sum(mean_daily_returns * weights) * 252
portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights))) * np.sqrt(252)
#store results in results array
results[0,i] = portfolio_return
results[1,i] = portfolio_std_dev
#store Sharpe Ratio (return / volatility) - risk free rate element excluded for simplicity
results[2,i] = results[0,i] / results[1,i]
#iterate through the weight vector and add data to results array
for j in range(len(weights)):
results[j+3,i] = weights[j]
#convert results array to Pandas DataFrame
results_frame = pd.DataFrame(results.T,columns=['ret','stdev','sharpe'] + [ticker for ticker in stocks])
#locate position of portfolio with highest Sharpe Ratio
max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
#locate positon of portfolio with minimum standard deviation
min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
#create scatter plot coloured by Sharpe Ratio
plt.scatter(results_frame.stdev,results_frame.ret,c=results_frame.sharpe,cmap='RdYlBu')
plt.xlabel('Volatility')
plt.ylabel('Returns')
plt.colorbar()
#plot red star to highlight position of portfolio with highest Sharpe Ratio
plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker=(5,1,0),color='r',s=1000)
#plot green star to highlight position of minimum variance portfolio
plt.scatter(min_vol_port[1],min_vol_port[0],marker=(5,1,0),color='g',s=1000)
【问题讨论】：
标签： python