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import pandas as pd
def trim_portfolio(portfolio: pd.DataFrame, trim_slack: float, performance_type: str, own_type: str, performance_threshold_high: float, performance_threshold_low: float, own_threshold_high: float, own_threshold_low: float):
if performance_type == 'Finish_percentile':
working_portfolio = portfolio.sort_values(by=performance_type, ascending = True).reset_index(drop=True)
else:
working_portfolio = portfolio.sort_values(by=performance_type, ascending = False).reset_index(drop=True)
rows_to_drop = []
curr_own_type_max = working_portfolio.loc[0, own_type] + (trim_slack * working_portfolio.loc[0, own_type])
for i in range(1, len(working_portfolio)):
if working_portfolio.loc[i, own_type] > curr_own_type_max and \
working_portfolio.loc[i, performance_type] > performance_threshold_low and \
working_portfolio.loc[i, performance_type] <= performance_threshold_high and \
working_portfolio.loc[i, own_type] > own_threshold_low and \
working_portfolio.loc[i, own_type] <= own_threshold_high:
rows_to_drop.append(i)
else:
curr_own_type_max = working_portfolio.loc[i, own_type] + (trim_slack * working_portfolio.loc[i, own_type])
working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
return working_portfolio |