如何 plot 在 Python 中的同一投资组合内分配多个资产

Question

我的投资组合由以下三种资产组成

# import some libraries
from pandas_datareader import data as wb
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from scipy.stats import norm
import math
# get the stock symbols in our portfolio
assets = ['AAP','NFLX','BA']

# assign weights to our portfolio 
weights = np.array([0.2, 0.3, 0.5])
# get the portfolio start date
stockStartDate = '2020-01-01'

# get the portfolio ending date 
today = datetime.today().strftime('%Y-%m-%d')

# create a dataframe to store the adjusted close price of the price
df = pd.DataFrame()

# store the adjusted close price pf the stock into the df
for stock in assets : 
  df[stock] = wb.DataReader(stock, data_source = 'yahoo', start = stockStartDate, end = today)['Adj Close']

# visually show the portfolio

title = 'Portfolio Adjusted Close Price History'

#get the stocks
my_stocks = df

# create & plot the graph
for c in my_stocks.columns.values:
  plt.plot(my_stocks[c], label = c)

plt.title(title)
plt.xlabel('Date', fontsize = 18)
plt.ylabel('Adj. Price USD($)')
plt.legend(my_stocks.columns.values, loc = 'upper left')
plt.show()

这导致了这个结果：投资组合调整价格历史

我已经能够使用下面的代码为整个投资组合绘制一个图表

returns = df.pct_change()
df1 = returns

mean = sum(weights)/len(weights)
var = sum(pow(x-mean,2) for x in weights) / len(weights)
std = math.sqrt(var)
x_min = df1.min()*100
x_max = df1.max()*100

mean = mean
std = std
#x_axis = np.arange(x_min, x_max,0.01)
x_axis = np.arange(-20,20,0.01)
plt.plot(x_axis, norm.pdf(x_axis, mean, std))
plt.xlabel('returns')
plt.ylabel('Frequency density')
plt.title('Returns Distributions for N = 1')
plt.show()

以及以下代码：

for d in returns.columns:
  plt.plot(returns[d], label = d)

plt.title(title)
plt.xlabel('Date', fontsize = 18)
plt.ylabel('Adj. Price USD($)')
plt.legend(my_stocks.columns.values, loc = 'upper left')
plt.show()

但是我想要的结果如下：资产价格分布和资产总回报

你能帮忙计算这两个图吗？

提前谢谢了