这个怎么样？你能用这个吗？
# https://towardsdatascience.com/efficient-frontier-portfolio-optimisation-in-python-e7844051e7f
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import quandl
import scipy.optimize
np.random.seed(777)
quandl.ApiConfig.api_key = 'you_resy_goes_here'
stocks = ['AAPL', 'AMZN', 'MSFT', 'SBUX']
data = quandl.get_table('WIKI/PRICES', ticker = stocks,
qopts = { 'columns': ['date', 'ticker', 'adj_close'] },
date = { 'gte': '2018-1-1', 'lte': '2019-12-31' }, paginate=True)
data.head()
data.info()
df = data.set_index('date')
table = df.pivot(columns='ticker')
# By specifying col[1] in below list comprehension
# You can select the stock names under multi-level column
table.columns = [col[1] for col in table.columns]
table.head()
plt.figure(figsize=(14, 7))
for c in table.columns.values:
plt.plot(table.index, table[c], lw=3, alpha=0.8,label=c)
plt.legend(loc='upper left', fontsize=12)
plt.ylabel('price in $')
returns = table.pct_change()
plt.figure(figsize=(14, 7))
for c in returns.columns.values:
plt.plot(returns.index, returns[c], lw=3, alpha=0.8,label=c)
plt.legend(loc='upper right', fontsize=12)
plt.ylabel('daily returns')
###################################################
def portfolio_annualised_performance(weights, mean_returns, cov_matrix):
returns = np.sum(mean_returns*weights ) *252
std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)
return std, returns
def random_portfolios(num_portfolios, mean_returns, cov_matrix, risk_free_rate):
results = np.zeros((3,num_portfolios))
weights_record = []
for i in range(num_portfolios):
weights = np.random.random(4)
weights /= np.sum(weights)
weights_record.append(weights)
portfolio_std_dev, portfolio_return = portfolio_annualised_performance(weights, mean_returns, cov_matrix)
results[0,i] = portfolio_std_dev
results[1,i] = portfolio_return
results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev
return results, weights_record
returns = table.pct_change()
mean_returns = returns.mean()
cov_matrix = returns.cov()
num_portfolios = 25000
risk_free_rate = 0.0178
###################################################
def display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate):
results, weights = random_portfolios(num_portfolios,mean_returns, cov_matrix, risk_free_rate)
max_sharpe_idx = np.argmax(results[2])
sdp, rp = results[0,max_sharpe_idx], results[1,max_sharpe_idx]
max_sharpe_allocation = pd.DataFrame(weights[max_sharpe_idx],index=table.columns,columns=['allocation'])
max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]
max_sharpe_allocation = max_sharpe_allocation.T
min_vol_idx = np.argmin(results[0])
sdp_min, rp_min = results[0,min_vol_idx], results[1,min_vol_idx]
min_vol_allocation = pd.DataFrame(weights[min_vol_idx],index=table.columns,columns=['allocation'])
min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]
min_vol_allocation = min_vol_allocation.T
print("-")
print("Maximum Sharpe Ratio Portfolio Allocation\n")
print("Annualised Return:", round(rp,2))
print("Annualised Volatility:", round(sdp,2))
print("\n")
print(max_sharpe_allocation)
print("-")
print("Minimum Volatility Portfolio Allocation\n")
print("Annualised Return:", round(rp_min,2))
print("Annualised Volatility:", round(sdp_min,2))
print("\n")
print(min_vol_allocation)
plt.figure(figsize=(10, 7))
plt.scatter(results[0,:],results[1,:],c=results[2,:],cmap='YlGnBu', marker='o', s=10, alpha=0.3)
plt.colorbar()
plt.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')
plt.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')
plt.title('Simulated Portfolio Optimization based on Efficient Frontier')
plt.xlabel('annualised volatility')
plt.ylabel('annualised returns')
plt.legend(labelspacing=0.8)
display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate)