def get_returns_for_optimisation(
self, instrument_code: str) -> returnsForOptimisationWithCosts:
"""
Get pandl for forecasts for a given rule
THese will include both gross and net returns, in case we do any pooling
KEY INPUT
:param instrument_code:
:type str:
:returns: accountCurveGroup object
"""
accounts = self.accounts_stage
if accounts is missing_data:
error_msg = (
"You need an accounts stage in the system to estimate forecast weights"
)
self.log.critical(error_msg)
raise Exception(error_msg)
cheap_rule_list = self.cheap_trading_rules(instrument_code)
pandl = accounts.pandl_for_instrument_rules_unweighted(
instrument_code, cheap_rule_list)
pandl = returnsForOptimisationWithCosts(pandl)
return pandl
def returns_pre_processor(self) -> returnsPreProcessor:
instrument_list = self.get_instrument_list(for_instrument_weights=True)
pandl_across_subsystems_raw = self.pandl_across_subsystems(
instrument_list=instrument_list
)
pandl_across_subsystems_as_returns_object = returnsForOptimisationWithCosts(
pandl_across_subsystems_raw
)
pandl_across_subsystems = dictOfReturnsForOptimisationWithCosts(
pandl_across_subsystems_as_returns_object
)
turnovers = self.turnover_across_subsystems()
config = self.config
weighting_params = copy(config.instrument_weight_estimate)
returns_pre_processor = returnsPreProcessor(
pandl_across_subsystems,
turnovers=turnovers,
log=self.log,
**weighting_params
)
return returns_pre_processor