Refactor hedging_preset.py to use a configurable list size for top owned players. This change enhances flexibility in player selection and adjusts lineup target calculations accordingly.

global_func/hedging_preset.py

@@ -6,10 +6,11 @@ from global_func.large_field_preset import large_field_preset
 def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file: pd.DataFrame):
     
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Similarity Score']
+    list_size = 3
 
     check_own_df = projections_file.copy()
     check_own_df = check_own_df.sort_values(by='ownership', ascending=False)
-    top_owned = check_own_df['player_names'].head(3).tolist()
+    top_owned = check_own_df['player_names'].head(list_size).tolist()
 
     concat_portfolio = pd.DataFrame(columns=portfolio.columns)
 
@@ -29,8 +30,8 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
         removed_df = working_df[remove_mask]
         locked_df = working_df[lock_mask]
 
-        removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / 2), excluded_cols)
-        locked_lineups = large_field_preset(locked_df, math.ceil(lineup_target / 2), excluded_cols)
+        removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * 2)), excluded_cols)
+        locked_lineups = large_field_preset(locked_df, math.ceil(lineup_target / (list_size * 2)), excluded_cols)
 
         concat_portfolio = pd.concat([concat_portfolio, removed_lineups, locked_lineups])