import pandas as pd

def distribute_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
    
    for slack_var in range(1, 20):
        concat_portfolio = pd.DataFrame(columns=portfolio.columns)

        for finishing_range in range(1, 20):
            rows_to_drop = []
            working_portfolio = portfolio.copy()
            working_portfolio = working_portfolio[(working_portfolio['Finish_percentile'] <= (finishing_range / 100)) & (working_portfolio['Finish_percentile'] >= ((finishing_range - 1) / 100))].sort_values(by='median', ascending = False)
            working_portfolio = working_portfolio.reset_index(drop=True)
            curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Weighted Own'])

            for i in range(1, len(working_portfolio)):
                if working_portfolio.loc[i, 'Weighted'] > curr_own_type_max:
                    rows_to_drop.append(i)
                else:
                    curr_own_type_max = working_portfolio.loc[i, 'Weighted'] + (slack_var / 20 * working_portfolio.loc[i, 'Weighted'])
            
            working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
            concat_portfolio = pd.concat([concat_portfolio, working_portfolio])

        if len(concat_portfolio) >= lineup_target:
            return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
        
    return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)