Update portfolio distribution logic in distribute_preset.py to utilize 'Similarity Score' for maximum ownership type calculations. This change improves the accuracy of portfolio filtering by ensuring the correct comparison metric is applied, enhancing the overall trimming process.

global_func/distribute_preset.py

@@ -22,13 +22,13 @@ def distribute_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
             if len(working_portfolio) == 0:
                 continue
             elif len(working_portfolio) >= 1:
-                curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Weighted Own'])
+                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, 'Weighted Own'] > 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, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Weighted Own'])
+                        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.head(10)])