| 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 own_type == 'Diversity': | |
| 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]) | |
| else: | |
| 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 |