Update volatility_preset function to use 'Similarity Score' for row filtering instead of 'median'. This change improves the accuracy of lineup adjustments by ensuring the correct comparison for ownership type maximums.

global_func/volatility_preset.py

@@ -13,13 +13,13 @@ def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
             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, 'median'] + (slack_var / 20 * working_portfolio.loc[0, 'median'])
+            curr_own_type_max = working_portfolio.loc[0, 'Similarity Score'] + (slack_var / 20 * working_portfolio.loc[0, 'Similarity Score'])
 
             for i in range(1, len(working_portfolio)):
-                if working_portfolio.loc[i, 'median'] > curr_own_type_max:
+                if working_portfolio.loc[i, 'Similarity Score'] < curr_own_type_max:
                     rows_to_drop.append(i)
                 else:
-                    curr_own_type_max = working_portfolio.loc[i, 'median'] + (slack_var / 20 * working_portfolio.loc[i, 'median'])
+                    curr_own_type_max = working_portfolio.loc[i, 'Similarity Score'] + (slack_var / 20 * working_portfolio.loc[i, 'Similarity Score'])
 
             working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
             concat_portfolio = pd.concat([concat_portfolio, working_portfolio])