def get_optimised_positions_data_dict_given_optimisation(
data_for_objective: dataForObjectiveInstance,
objective_function: objectiveFunctionForGreedy,
) -> dict:
optimised_positions = objective_function.optimise_positions()
optimised_positions = optimised_positions.replace_weights_with_ints()
optimised_position_weights = get_weights_given_positions(
optimised_positions,
per_contract_value=data_for_objective.per_contract_value)
instrument_list = list(optimised_position_weights.keys())
minima_weights = portfolioWeights.from_weights_and_keys(
list_of_keys=instrument_list,
list_of_weights=list(objective_function.minima_as_np),
)
maxima_weights = portfolioWeights.from_weights_and_keys(
list_of_keys=instrument_list,
list_of_weights=list(objective_function.maxima_as_np),
)
starting_weights = portfolioWeights.from_weights_and_keys(
list_of_keys=instrument_list,
list_of_weights=list(objective_function.starting_weights_as_np),
)
data_dict = dict([(
instrument_code,
get_optimal_position_entry_with_calcs_for_code(
instrument_code=instrument_code,
data_for_objective=data_for_objective,
optimised_position_weights=optimised_position_weights,
optimised_positions=optimised_positions,
maxima_weights=maxima_weights,
starting_weights=starting_weights,
minima_weights=minima_weights,
),
) for instrument_code in instrument_list])
return data_dict
def optimise_from_processed_estimates(
estimates: Estimates) -> portfolioWeights:
stdev_list = estimates.stdev_list
corrmatrix = estimates.correlation_matrix
mean_list = estimates.mean_list
list_of_asset_names = estimates.asset_names
sigma = sigma_from_corr_and_std(stdev_list=stdev_list,
corrmatrix=corrmatrix)
weights = optimise_from_sigma_and_mean_list(sigma, mean_list=mean_list)
portfolio_weights = portfolioWeights.from_weights_and_keys(
list_of_weights=weights, list_of_keys=list_of_asset_names)
return portfolio_weights
def clean_weights(weights: portfolioWeights,
must_haves: dict,
fraction: float = 0.5) -> portfolioWeights:
asset_names, list_of_weights, list_of_must_haves = \
get_lists_from_dicts_of_weights_and_must_haves(weights = weights, must_haves = must_haves)
cleaned_list_of_weights = clean_list_of_weights(
weights=list_of_weights,
must_haves=list_of_must_haves,
fraction=fraction)
cleaned_weights = portfolioWeights.from_weights_and_keys(
list_of_weights=cleaned_list_of_weights, list_of_keys=asset_names)
return cleaned_weights
def calculate_contract_positions_from_weights_and_values(
optimal_weights: portfolioWeights,
per_contract_value: portfolioWeights) -> portfolioWeights:
instrument_list = list(optimal_weights.keys())
optimal_weights_as_np = np.array(
optimal_weights.as_list_given_keys(instrument_list))
per_contract_value_as_np = np.array(
per_contract_value.as_list_given_keys(instrument_list))
positions_as_np = optimal_weights_as_np / per_contract_value_as_np
positions = portfolioWeights.from_weights_and_keys(positions_as_np,
instrument_list)
return positions
def optimise_weights(self) -> portfolioWeights:
optimal_weights_without_missing_items_as_np = self.optimise_np_for_valid_keys(
)
optimal_weights_without_missing_items_as_list = list(
optimal_weights_without_missing_items_as_np)
optimal_weights = portfolioWeights.from_weights_and_keys(
list_of_keys=self.keys_with_valid_data,
list_of_weights=optimal_weights_without_missing_items_as_list,
)
optimal_weights_for_all_keys = (
optimal_weights.with_zero_weights_for_missing_keys(
list(self.weights_optimal.keys())))
return optimal_weights_for_all_keys
def adjust_weights_for_SR_on_handcrafted_portfolio(
raw_weights: portfolioWeights,
handcraft_portfolio: handcraftPortfolio) -> portfolioWeights:
SR_list = handcraft_portfolio.sharpe_ratio
avg_correlation = handcraft_portfolio.avg_correlation
years_of_data = handcraft_portfolio.data_length_years
asset_names = handcraft_portfolio.asset_names
weights_as_list = raw_weights.as_list_given_keys(asset_names)
weights = adjust_weights_for_SR(SR_list=SR_list,
avg_correlation=avg_correlation,
years_of_data=years_of_data,
weights_as_list=weights_as_list)
weights = portfolioWeights.from_weights_and_keys(list_of_weights=weights,
list_of_keys=asset_names)
return weights