def proportional_prior(sigma, tau, p):
    '\n    Returns the He-Litterman simplified Omega\n    Inputs:\n    sigma: N x N Covariance Matrix as DataFrame\n    tau: a scalar\n    p: a K x N DataFrame linking Q and Assets\n    returns a P x P DataFrame, a Matrix representing Prior Uncertainties\n    '
    helit_omega = p.dot((tau * sigma)).dot(p.T)
    return pd.DataFrame(np.diag(np.diag(helit_omega.values)), index=p.index, columns=p.index)
Returns the He-Litterman simplified Omega
Inputs:
sigma: N x N Covariance Matrix as DataFrame
tau: a scalar
p: a K x N DataFrame linking Q and Assets
returns a P x P DataFrame, a Matrix representing Prior Uncertainties
def proportional_prior(sigma, tau, p):
    '\n    Returns the He-Litterman simplified Omega\n    Inputs:\n    sigma: N x N Covariance Matrix as DataFrame\n    tau: a scalar\n    p: a K x N DataFrame linking Q and Assets\n    returns a P x P DataFrame, a Matrix representing Prior Uncertainties\n    '
    helit_omega = p.dot((tau * sigma)).dot(p.T)
    return pd.DataFrame(np.diag(np.diag(helit_omega.values)), index=p.index, columns=p.index)