Refactor large_field_preset and small_field_preset functions to enhance lineup targeting: increase slack_var range, implement team-based filtering, and consolidate results into a single DataFrame for improved accuracy in portfolio adjustments.

global_func/large_field_preset.py

@@ -2,22 +2,25 @@ import pandas as pd
 
 def large_field_preset(portfolio: pd.DataFrame, lineup_target: int):
     
-    for slack_var in range(1, 10):
-        rows_to_drop = []
-        
-        working_portfolio = portfolio.sort_values(by='Finish_percentile', ascending = True)
-        working_portfolio = working_portfolio[working_portfolio['Lineup Edge'] > 0]
-        working_portfolio = working_portfolio.reset_index(drop=True)
-        curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 10 * working_portfolio.loc[0, 'Own'])
+    for slack_var in range(1, 20):
+        concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+
+        for team in portfolio['Team'].unique():
+            rows_to_drop = []
+            working_portfolio = portfolio[portfolio['stack'] == team].sort_values(by='Finish_percentile', ascending = True)
+            working_portfolio = working_portfolio[working_portfolio['Lineup Edge'] > 0]
+            working_portfolio = working_portfolio.reset_index(drop=True)
+            curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
 
-        for i in range(1, len(working_portfolio)):
-            if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
-                rows_to_drop.append(i)
-            else:
-                curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 10 * working_portfolio.loc[i, 'Own'])
+            for i in range(1, len(working_portfolio)):
+                if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
+                    rows_to_drop.append(i)
+                else:
+                    curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
 
-        working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
-        if len(working_portfolio) >= lineup_target:
-            return working_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
+            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='Finish_percentile', ascending=True).head(lineup_target)
         
-    return working_portfolio.sort_values(by='Finish_percentile', ascending=True)
+    return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)

global_func/small_field_preset.py

@@ -1,22 +1,26 @@
 import pandas as pd
 
 def small_field_preset(portfolio: pd.DataFrame, lineup_target: int):
-    
-    for slack_var in range(1, 10):
-        rows_to_drop = []
-        working_portfolio = portfolio.sort_values(by='Own', ascending = False).reset_index(drop=True)
-        working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= .10]
-        working_portfolio = working_portfolio.reset_index(drop=True)
-        curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 10 * working_portfolio.loc[0, 'Weighted Own'])
 
-        for i in range(1, len(working_portfolio)):
-            if working_portfolio.loc[i, 'Weighted Own'] > curr_own_type_max:
-                rows_to_drop.append(i)
-            else:
-                curr_own_type_max = working_portfolio.loc[i, 'Weighted Own'] + (slack_var / 10 * working_portfolio.loc[i, 'Weighted Own'])
+    for slack_var in range(1, 20):
+        concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+        
+        for team in portfolio['Team'].unique():
+            rows_to_drop = []
+            working_portfolio = portfolio[portfolio['stack'] == team].sort_values(by='Own', ascending = False).reset_index(drop=True)
+            working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= .10]
+            working_portfolio = working_portfolio.reset_index(drop=True)
+            curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Weighted Own'])
+
+            for i in range(1, len(working_portfolio)):
+                if working_portfolio.loc[i, 'Weighted Own'] > 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'])
 
-        working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
-        if len(working_portfolio) >= lineup_target:
-            return working_portfolio.sort_values(by='Own', ascending=False).head(lineup_target)
+            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='Own', ascending=False).head(lineup_target)
         
-    return working_portfolio.sort_values(by='Own', ascending=False)
+    return concat_portfolio.sort_values(by='Own', ascending=False)