| import pandas as pd | |
| import numpy as np | |
| def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list): | |
| excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity'] | |
| player_columns = [col for col in portfolio.columns if col not in excluded_cols] | |
| for slack_var in range(1, 20): | |
| concat_portfolio = pd.DataFrame(columns=portfolio.columns) | |
| for team in portfolio['Stack'].unique(): | |
| rows_to_drop = [] | |
| working_portfolio = portfolio.copy() | |
| working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Lineup Edge', ascending = False) | |
| working_portfolio = working_portfolio.reset_index(drop=True) | |
| curr_own_type_max = working_portfolio.loc[0, 'Diversity'] + (slack_var / 20 * working_portfolio.loc[0, 'Diversity']) | |
| for i in range(1, len(working_portfolio)): | |
| if working_portfolio.loc[i, 'Diversity'] < curr_own_type_max: | |
| rows_to_drop.append(i) | |
| else: | |
| curr_own_type_max = working_portfolio.loc[i, 'Diversity'] + (slack_var / 20 * working_portfolio.loc[i, 'Diversity']) | |
| 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='Lineup Edge', ascending=False).head(lineup_target) | |
| return concat_portfolio.sort_values(by='Lineup Edge', ascending=False) | |