def equal_risk_contributon(weights, cov_matrix):
    marginal_risk = weights.T*np.dot(cov_matrix, weights)
    marginal_risk = np.abs(np.sum(marginal_risk)/4-marginal_risk)

    return np.sum(marginal_risk)*1000

def min_risk_diff(mean_returns, cov_matrix):
    num_assets = mean_returns.shape[0]
    args = (cov_matrix)
    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    bound = (0.0,1.0)
    bounds = tuple(bound for asset in range(num_assets))
    result = minimize(equal_risk_contributon, num_assets*[1./num_assets,], args=args,
                        method='SLSQP', bounds=bounds, constraints=constraints)
    return result.x