def weights_and_estimates_with_no_valid_data(
estimates: Estimates) -> estimatesWithPortfolioWeights:
weights_with_no_valid_data = portfolioWeights.allnan([])
weights_with_estimates_for_valid_data = estimatesWithPortfolioWeights(
weights=weights_with_no_valid_data, estimates=estimates)
return weights_with_estimates_for_valid_data
def equal_weights_optimisation(estimates: Estimates,
**_ignore_weighting_args) -> estimatesWithPortfolioWeights:
portfolio_weights = one_over_n_portfolio_weights_from_estimates(estimates)
estimates_with_weights = estimatesWithPortfolioWeights(weights=portfolio_weights,
estimates=estimates)
return estimates_with_weights
def handcraft_optimisation(
estimates: Estimates,
equalise_SR: bool = False,
equalise_vols: bool = True,
**_ignored_weighting_kwargs) -> estimatesWithPortfolioWeights:
weights = get_handcrafted_portfolio_weights_for_valid_data(
estimates, equalise_vols=equalise_vols, equalise_SR=equalise_SR)
estimates_and_portfolio_weights = estimatesWithPortfolioWeights(
weights=weights, estimates=estimates)
return estimates_and_portfolio_weights
def optimise_given_estimates(
estimates: Estimates,
equalise_SR: bool = True,
ann_target_SR: float = 0.5,
equalise_vols: bool = True,
**_ignored_kwargs) -> estimatesWithPortfolioWeights:
estimates = estimates.equalise_estimates(equalise_vols=equalise_vols,
equalise_SR=equalise_SR,
ann_target_SR=ann_target_SR)
portfolio_weights = optimise_from_processed_estimates(estimates)
estimates_with_portfolio_weights = estimatesWithPortfolioWeights(
weights=portfolio_weights, estimates=estimates)
return estimates_with_portfolio_weights