Enhance volatility_preset function to include sport-specific logic for MLB, allowing dynamic adjustments in player selection based on team diversity. This update improves lineup generation accuracy by refining the handling of team constraints and optimizing the portfolio based on lineup edge.

global_func/small_field_preset.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import numpy as np
 
 def small_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']

global_func/volatility_preset.py

@@ -7,11 +7,29 @@ def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
 
     for slack_var in range(1, 20):
         concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+        if sport == 'MLB':
+            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 team in portfolio['Stack'].unique():
+                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)
+        else:
             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.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'])
 
@@ -23,7 +41,7 @@ def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
 
             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)