import pandas as pd

def trim_portfolio(portfolio: pd.DataFrame, 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]

    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]

    working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
    return working_portfolio