Add volatility preset option in app.py and implement volatility_preset function. Updated lineup generation logic to include new preset for enhanced lineup edge management.

app.py

@@ -23,6 +23,7 @@ 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
 from global_func.hedging_preset import hedging_preset
+from global_func.volatility_preset import volatility_preset
 
 freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
 stacking_sports = ['MLB', 'NHL', 'NFL']
@@ -1111,7 +1112,7 @@ with tab2:
             with st.expander('Presets'):
                 st.info("Still heavily in testing here, I'll announce when they are ready for use.")
                 with st.form(key='Small Field Preset'):
-                    preset_choice = st.selectbox("Preset", options=['Small Field (Heavy Own)', 'Large Field (Manage Similarity)', 'Hedge Chalk (Manage Leverage)'], index=0)
+                    preset_choice = st.selectbox("Preset", options=['Small Field (Heavy Own)', 'Large Field (Manage Similarity)', 'Hedge Chalk (Manage Leverage)', 'Volatility (Heavy Lineup Edge)'], index=0)
                     lineup_target = st.number_input("Lineups to produce", value=150, min_value=1, step=1)
                     submitted = st.form_submit_button("Submit")
                     if submitted:
@@ -1119,6 +1120,8 @@ with tab2:
                             parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
                         elif preset_choice == 'Large Field (Manage Similarity)':
                             parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
+                        elif preset_choice == 'Volatility (Heavy Lineup Edge)':
+                            parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
                         elif preset_choice == 'Hedge Chalk (Manage Leverage)':
                             parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'])
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)

global_func/large_field_preset.py

@@ -7,7 +7,6 @@ def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols
     
     concat_portfolio = portfolio.copy()
     concat_portfolio = concat_portfolio.sort_values(by='Similarity Score', ascending=True).reset_index(drop=True)
-    concat_portfolio = concat_portfolio[concat_portfolio['Lineup Edge'] > 0]
 
     # Calculate target similarity scores for linear progression
     similarity_floor = concat_portfolio['Similarity Score'].min()

global_func/small_field_preset.py

@@ -1,7 +1,6 @@
 import pandas as pd
 
 def small_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
-
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Similarity Score']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]
 

global_func/volatility_preset.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+
+def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
+    excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Similarity Score']
+    player_columns = [col for col in portfolio.columns if col not in excluded_cols]
+
+    for slack_var in range(1, 20):
+        concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+
+        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, 'median'] + (slack_var / 20 * working_portfolio.loc[0, 'median'])
+
+            for i in range(1, len(working_portfolio)):
+                if working_portfolio.loc[i, 'median'] < curr_own_type_max:
+                    rows_to_drop.append(i)
+                else:
+                    curr_own_type_max = working_portfolio.loc[i, 'median'] + (slack_var / 20 * working_portfolio.loc[i, 'median'])
+
+            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)
+        
+    return concat_portfolio.sort_values(by='Lineup Edge', ascending=False)