We set the uncertainty proportional to the variance.

omega ( np.ndarray or Pd.DataFrame, or string, optional) – KxK view uncertainty matrix (diagonal), defaults to NoneĬan instead pass “idzorek” to use Idzorek’s method (requires.

P ( np.ndarray or pd.DataFrame, optional) – KxN picking matrix, defaults to None.

Q ( np.ndarray or pd.DataFrame, optional) – Kx1 views vector, defaults to None.

If this is provided, we do not need P, Q.

absolute_views ( pd.Series or dict, optional) – a colleciton of K absolute views on a subset of assets,ĭefaults to None.

If pi=”equal”, use an equal-weighted prior. If pi=”market”, calculate a market-implied prior (requires market_caps

pi ( np.ndarray, pd.Series, optional) – Nx1 prior estimate of returns, defaults to None.

cov_matrix ( pd.DataFrame or np.ndarray) – NxN covariance matrix of returns.

_init_ ( cov_matrix, pi=None, absolute_views=None, Q=None, P=None, omega=None, view_confidences=None, tau=0.05, risk_aversion=1, **kwargs ) ¶ Parameters:

save_weights_to_file() saves the weights to csv, json, or txt.

clean_weights() rounds the weights and clips near-zeros.

set_weights() creates self.weights (np.ndarray) from a weights dict.

portfolio_performance() calculates the expected return, volatilityĪnd Sharpe ratio for the allocated portfolio.

bl_weights() - weights implied by posterior returns.

bl_cov() - posterior estimate of covariance.

bl_returns() - posterior estimate of returns.

idzorek_method() - convert views specified as percentages into BL uncertainties.

default_omega() - view uncertainty proportional to asset variance.

Black litterman excel download