cryptocurrencyportfolio_5

port_return = []

# Initialize an empty list for storing the portfolio volatility
port_volatility = []

# Initialize an empty list for storing the portfolio weights
port_weights = []

num_assets = len(data.columns)
num_portfolio = 1000000

individual_rets = data.resample('Y').last().pct_change().mean()

for port in range(num_portfolio):
  # Randomly generate weigh combination
  weights = np.random.random(num_assets)
  # Normalize weight so that they sum to 1
  weights = weights/np.sum(weights)
  port_weights.append(weights)
  # Return are the dot product of individual expected returns of asset and its weights
  returns = np.dot(weights, individual_rets)
  port_return.append(returns)
  # Computing Portfolio Volatility
  portfolio_volatility = np.sqrt(np.dot(weights.T,np.dot(var_matrix*252,weights)))  
  port_volatility.append(portfolio_volatility)
  
portfolio = {'Returns': port_return,'Volatility': port_volatility}

for counter, symbol in enumerate(data.columns.tolist()):
  portfolio[symbol] = [w[counter] for w in port_weights]
  
portfolios_V1 = pd.DataFrame(portfolio)
portfolios_V1.head()