我想创建一个脚本，根据一篮子股票的整体回报，寻找一个月内买卖一篮子股票的最佳时间。
例如，假设我在第 2 天买入并在第 12 天卖出，股票 1 的最高回报率为 8%，如果我在第 9 天买入并在第 15 天卖出，股票 2 的最高回报率为 4%如果我在第 1 天买入并在第 20 天卖出，股票 3 的最高回报率为 -1%。我的问题是你如何编写一个脚本来查看所有三只股票的所有回报可能性并得出一个输出如果我必须在同一天购买所有三只股票并在同一天卖出，那么整个一揽子股票的回报率最高。
例如，所有三只股票的最高回报可能是在第 4 天买入并在第 17 天卖出，其中股票 1 的整体投资回报率为 7%、股票 2 为 3.5% 和 -1.25股票 3 的百分比。这是基于这样一个事实，即如果必须在同一天买卖所有三只股票，我们将无法获得每只股票的最佳回报。
【问题讨论】：
按日期对 df 进行排序。
df = df.sort_values(by ='Date', ascending=True)
然后每天汇总所有股票
df['total'] = df[['TSLA Price', 'NVDA Price', 'AAPL Price']].sum(axis=1)
然后使用下面的函数
def max_profit(li):
max_profit, purchase_on, sell_on = 0, 0, 0
for i, buy in enumerate(li):
for j, sell in enumerate(li[i+1:]):
if sell-buy > max_profit:
max_profit, purchase_on, sell_on = sell-buy, i, i+j+1
return max_profit, purchase_on, sell_on
max_profit, purchase_on, sell_on = max_profit(df['total'].tolist())
买入/卖出日期将是...
buy_date, sell_date = df['Date'].iloc[purchase_on], df['Date'].iloc[sell_on ]
要不同的权重，只需将股票乘以权重，然后求和。
【讨论】：
这看起来像是一篇很老的帖子，但我会用我为你准备的内容加入。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.optimize as sco
import datetime as dt
import math
from datetime import datetime, timedelta
from pandas_datareader import data as wb
from sklearn.cluster import KMeans
np.random.seed(777)
start = '2018-06-30'
end = '2020-06-30'
# N = 90
# start = datetime.now() - timedelta(days=N)
# end = dt.datetime.today()
tickers = ['AXP','AAPL','BA','CAT','CSCO','CVX','XOM','GS','HD','IBM','INTC','JNJ','KO','JPM','MCD','MMM','MRK','MSFT','NKE','PFE','PG','TRV','UNH','RTX','VZ','V','WBA','WMT','DIS','DOW']
thelen = len(tickers)
price_data = []
for ticker in tickers:
prices = wb.DataReader(ticker, start = start, end = end, data_source='yahoo')[['Adj Close']]
price_data.append(prices.assign(ticker=ticker)[['ticker', 'Adj Close']])
df = pd.concat(price_data)
df.dtypes
df.head()
df.shape
pd.set_option('display.max_columns', 500)
df = df.reset_index()
df = df.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()
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(thelen)
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 = 10000
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)
结果：
Maximum Sharpe Ratio Portfolio Allocation
Annualised Return: 0.16
Annualised Volatility: 0.25
AAPL AXP BA CAT CSCO CVX DIS DOW GS HD IBM \
allocation 4.76 1.63 1.03 0.26 1.22 0.05 6.86 0.43 1.02 6.42 0.88
INTC JNJ JPM KO MCD MMM MRK MSFT NKE PFE PG \
allocation 2.56 0.64 4.92 0.27 6.64 4.13 6.99 5.49 4.52 2.35 6.36
RTX TRV UNH V VZ WBA WMT XOM
allocation 5.3 1.79 0.39 6.8 7.06 0.67 5.98 2.6
-
Minimum Volatility Portfolio Allocation
Annualised Return: 0.13
Annualised Volatility: 0.24
AAPL AXP BA CAT CSCO CVX DIS DOW GS HD IBM \
allocation 4.43 0.59 0.59 1.01 1.69 0.56 1.04 0.81 0.64 1.44 1.72
INTC JNJ JPM KO MCD MMM MRK MSFT NKE PFE PG \
allocation 3.43 3.47 2.9 5.43 7.61 4.99 7.78 4.72 2.2 7.13 6.72
RTX TRV UNH V VZ WBA WMT XOM
allocation 0.47 4.56 2.95 0.47 6.84 7.81 4.76 1.23
最后：
您还可以尝试其他事情。查看下面的链接了解所有详细信息。
https://github.com/ASH-WICUS/Notebooks/blob/master/Portfolio%20Optimization.ipynb
【讨论】：