import yfinance

stocks = ['ARM', 'META', 'SPY', 'TSLA']

data = yfinance.download(stocks,
'2024-04-01',
'2024-05-09')['Close']

returns = data.pct_change()
returns.head()

returns = returns.dropna()
returns.head()

average_daily_returns = returns.mean()
print(average_daily_returns)

standard_deviation_daily_returns = returns.std()
print(standard_deviation_daily_returns)

import numpy
weights = numpy.array([0.25, 0.25, 0.25, 0.25])

covariance_matrix = (returns.cov())*250

expected_portfolio_performace = numpy.sum(average_daily_returns * weights)
print(expected_portfolio_performance)

returns['Portfolio Returns'] = returns.dot(weights)
returns.head()

daily_cumulative_returns = (1+returns).cumprod()
print(daily_cumulative_returns)

daily_cumulative_returns.tail()