Add large field preset functionality in app.py and implement large_field_preset function for improved lineup targeting. Update small_field_preset function to sort by 'Own' for consistency in portfolio adjustments.

app.py

@@ -21,6 +21,7 @@ from global_func.find_csv_mismatches import find_csv_mismatches
 from global_func.trim_portfolio import trim_portfolio
 from global_func.get_portfolio_names import get_portfolio_names
 from global_func.small_field_preset import small_field_preset
+from global_func.large_field_preset import large_field_preset
 
 freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
 stacking_sports = ['MLB', 'NHL', 'NFL']
@@ -1109,10 +1110,10 @@ with tab2:
                     lineup_target = st.number_input("Lineups to produce", value=150, min_value=1, step=1)
                     submitted = st.form_submit_button("Submit")
                     if submitted:
-                        if preset_choice == 'Small Field':
+                        if preset_choice == 'Small Field (Heavy Own)':
                             parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target)
-                        # elif preset_choice == 'Large Field':
-                        #     parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target)
+                        elif preset_choice == 'Large Field (Finish Percentile / Edge)':
+                            parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target)
                         # elif preset_choice == 'Volatile':
                         #     parsed_frame = volatile_preset(st.session_state['working_frame'], lineup_target)
                         # elif preset_choice == 'Distributed':

global_func/large_field_preset.py

@@ -0,0 +1,23 @@
+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).reset_index(drop=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 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'])
+
+        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)
+        
+    return working_portfolio.sort_values(by='Own', ascending=False)

global_func/small_field_preset.py

@@ -17,6 +17,6 @@ def small_field_preset(portfolio: pd.DataFrame, lineup_target: int):
 
         working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
         if len(working_portfolio) >= lineup_target:
-            return working_portfolio.sort_values(by='median', ascending=False).head(lineup_target)
+            return working_portfolio.sort_values(by='Own', ascending=False).head(lineup_target)
         
-    return working_portfolio.sort_values(by='median', ascending=False)
+    return working_portfolio.sort_values(by='Own', ascending=False)