test_2.py 文件源码

def random_portfolio(returns):
    def rand_weights(n):
        # Produces n random weights that sum to 1
        k = np.random.rand(n)
        return k / sum(k)

    '''
    Returns the mean and standard deviation of returns for a random portfolio
    '''
    p = np.asmatrix(np.mean(returns, axis=1))
    w = np.asmatrix(rand_weights(returns.shape[0]))
    C = np.asmatrix(np.cov(returns))

    mu = w * p.T
    sigma = np.sqrt(w * C * w.T)

    # This recursion reduces outliers to keep plots pretty
    if sigma > 2:
        return random_portfolio(returns)
    return mu, sigma