medium

1.py

np.random.seed(42)
num_ports = 6000
all_weights = np.zeros((num_ports, len(stocks.columns)))
ret_arr = np.zeros(num_ports)
vol_arr = np.zeros(num_ports)
sharpe_arr = np.zeros(num_ports)

for x in range(num_ports):
    # Weights
    weights = np.array(np.random.random(4))
    weights = weights/np.sum(weights)
    
    # Save weights
    all_weights[x,:] = weights
    
    # Expected return
    ret_arr[x] = np.sum( (log_ret.mean() * weights * 252))
    
    # Expected volatility
    vol_arr[x] = np.sqrt(np.dot(weights.T, np.dot(log_ret.cov()*252, weights)))
    
    # Sharpe Ratio
    sharpe_arr[x] = ret_arr[x]/vol_arr[x]