Add team ownership calculation in hedging_preset.py to enhance lineup generation. Introduced a new function to sum hitter ownership by team and adjusted lineup target calculations for improved accuracy. This change allows for better team-based lineup strategies.

global_func/hedging_preset.py

@@ -2,6 +2,7 @@ import pandas as pd
 import math
 from global_func.small_field_preset import small_field_preset
 from global_func.large_field_preset import large_field_preset
+from global_func.get_portfolio_names import trim_portfolio
 
 def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file: pd.DataFrame):
     
@@ -12,6 +13,28 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
     check_own_df = check_own_df.sort_values(by='ownership', ascending=False)
     top_owned = check_own_df['player_names'].head(list_size).tolist()
 
+    def get_team_hitter_ownership(projections_file: pd.DataFrame):
+        """
+        Calculate the sum ownership of hitters on each team.
+        Excludes SP and P positions and sums ownership by team.
+        
+        Args:
+            projections_file (pd.DataFrame): DataFrame with 'position', 'team', and 'ownership' columns
+            
+        Returns:
+            pd.Series: Series with team names as index and total hitter ownership as values, sorted descending
+        """
+        # Filter out pitchers (SP and P positions)
+        hitters_df = projections_file[~projections_file['position'].isin(['P', 'SP'])]
+        
+        # Group by team and sum ownership
+        team_ownership = hitters_df.groupby('team')['ownership'].sum().sort_values(ascending=False)
+    
+        return team_ownership
+
+    team_ownership = get_team_hitter_ownership(projections_file)
+    top_owned_teams = team_ownership.head(list_size).index.tolist()
+
     concat_portfolio = pd.DataFrame(columns=portfolio.columns)
 
     for player in top_owned:
@@ -33,9 +56,16 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
         locked_df = working_df[lock_mask]
         print(locked_df.head(10))
 
-        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)
+        removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * 3)), excluded_cols)
+        locked_lineups = large_field_preset(locked_df, math.ceil(lineup_target / (list_size * 3)), excluded_cols)
 
         concat_portfolio = pd.concat([concat_portfolio, removed_lineups, locked_lineups])
+    
+    for team in top_owned_teams:
+        working_df = portfolio.copy()
+        teams_df = working_df[working_df['Stack'] == team]
+
+        team_lineups = large_field_preset(teams_df, math.ceil(lineup_target / (list_size * 3)), excluded_cols)
+        concat_portfolio = pd.concat([concat_portfolio, team_lineups])
 
     return concat_portfolio.head(lineup_target)