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
def get_pandl_forecasts(self, codes_to_use: list) -> dictOfReturnsForOptimisationWithCosts:
# returns a dict of accountCurveGroups
pandl_forecasts = dict(
[(code, self.get_returns_for_optimisation(code)) for code in codes_to_use]
)
pandl_forecasts = dictOfReturnsForOptimisationWithCosts(pandl_forecasts)
return pandl_forecasts
