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 returns_pre_processing(self, codes_to_use: list, trading_rule_list: list) -> returnsPreProcessor:
pandl_forecasts = self.get_pandl_forecasts(codes_to_use)
turnovers = self.get_turnover_for_list_of_rules(codes_to_use, trading_rule_list)
config = self.config
weighting_params = copy(config.forecast_weight_estimate)
cost_params = copy(config.forecast_cost_estimates)
weighting_params = {**weighting_params, **cost_params}
returns_pre_processor = returnsPreProcessor(pandl_forecasts,
turnovers = turnovers,
log=self.log,
**weighting_params)
return returns_pre_processor