fixing exposure management

app.py

@@ -1342,6 +1342,7 @@ if selected_tab == 'Manage Portfolio':
                         min_dupes = st.number_input("Min acceptable dupes?", value=0, min_value=0, max_value=1000, step=1)
                         min_finish_percentile = st.number_input("Min acceptable finish percentile?", value=0.00, min_value=0.00, max_value=1.00, step=.001)
                         min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-1.00, min_value=-1.00, max_value=1.00, step=.001)
+                        min_weighted_own = st.number_input("Min acceptable Weighted Own?", value=0.0, min_value=0.0, max_value=500.0, step=1.0)
                     with macro_max_col:
                         max_salary = st.number_input("Max acceptable salary?", value=salary_max, min_value=0, max_value=salary_max, step=100)
                         max_proj = st.number_input("Max acceptable projection?", value=1500.0, min_value=0.0, max_value=1500.0, step=1.0)
@@ -1349,6 +1350,7 @@ if selected_tab == 'Manage Portfolio':
                         max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, max_value=1000, step=1)
                         max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=1.00, min_value=0.00, max_value=1.00, step=.001)
                         max_lineup_edge = st.number_input("Max acceptable Lineup Edge?", value=1.00, min_value=-1.00, max_value=1.00, step=.001)
+                        max_weighted_own = st.number_input("Max acceptable Weighted Own?", value=500.0, min_value=0.0, max_value=500.0, step=1.0)
 
                     if sport_var in stacking_sports:
                         stack_include_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
@@ -1380,7 +1382,9 @@ if selected_tab == 'Manage Portfolio':
                             (st.session_state['working_frame']['Finish_percentile'] >= min_finish_percentile) &
                             (st.session_state['working_frame']['Finish_percentile'] <= max_finish_percentile) &
                             (st.session_state['working_frame']['Lineup Edge'] >= min_lineup_edge) &
-                            (st.session_state['working_frame']['Lineup Edge'] <= max_lineup_edge)
+                            (st.session_state['working_frame']['Lineup Edge'] <= max_lineup_edge) &
+                            (st.session_state['working_frame']['Weighted Own'] >= min_weighted_own) &
+                            (st.session_state['working_frame']['Weighted Own'] <= max_weighted_own)
                         )
                         
                         
@@ -1410,7 +1414,9 @@ if selected_tab == 'Manage Portfolio':
                             (st.session_state['export_base']['Finish_percentile'] >= min_finish_percentile) &
                             (st.session_state['export_base']['Finish_percentile'] <= max_finish_percentile) &
                             (st.session_state['export_base']['Lineup Edge'] >= min_lineup_edge) &
-                            (st.session_state['export_base']['Lineup Edge'] <= max_lineup_edge)
+                            (st.session_state['export_base']['Lineup Edge'] <= max_lineup_edge) &
+                            (st.session_state['export_base']['Weighted Own'] >= min_weighted_own) &
+                            (st.session_state['export_base']['Weighted Own'] <= max_weighted_own)
                         )
 
                         if 'Stack' in st.session_state['export_base'].columns:
@@ -2027,6 +2033,8 @@ if selected_tab == 'Manage Portfolio':
                 with st.form(key='Exposures'):
                     exposure_player = st.selectbox("Player", options=sorted(list(set(st.session_state['projections_df']['player_names'].unique()))), key='exposure_player')
                     exposure_target = st.number_input("Target Exposure", value=.50, min_value=0.0, max_value=1.0, step=0.01)
+                    comp_salary_below = st.number_input("Comp Salary Below", value=0, min_value=-1000, max_value=0, step=100)
+                    comp_salary_above = st.number_input("Comp Salary Above", value=0, min_value=0, max_value=1000, step=100)
                     if 'Stack' in st.session_state['working_frame'].columns:
                         ignore_stacks = st.multiselect("Ignore Specific Stacks?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
                     else:
@@ -2045,7 +2053,7 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['settings_base'] = False
                         # Prepare DataFrame for exposure_spread to avoid categorical issues
                         working_frame_prepared = prepare_dataframe_for_exposure_spread(st.session_state['working_frame'], st.session_state['player_columns'])
-                        parsed_frame = exposure_spread(working_frame_prepared, st.session_state['exposure_player'], exposure_target, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(working_frame_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         # Use consolidated calculation function
                         parsed_frame = calculate_lineup_metrics(
@@ -2082,7 +2090,7 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['settings_base'] = False
                         # Prepare DataFrame for exposure_spread to avoid categorical issues
                         export_base_prepared = prepare_dataframe_for_exposure_spread(st.session_state['export_base'], st.session_state['player_columns'])
-                        parsed_frame = exposure_spread(export_base_prepared, st.session_state['exposure_player'], exposure_target, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(export_base_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         # Use consolidated calculation function for export
                         parsed_frame = calculate_lineup_metrics(

global_func/exposure_spread.py

@@ -148,20 +148,22 @@ def check_position_eligibility(sport, column_name, player_positions):
         # Default fallback - assume exact position match
         return column_name in player_positions
 
-def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stacks, remove_teams, specific_replacements, specific_columns, projections_df, sport_var, type_var, salary_max, stacking_sports):
+def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams, specific_replacements, specific_columns, projections_df, sport_var, type_var, salary_max, stacking_sports):
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
     comparable_players = comparable_players.reset_index(drop=True)
     comp_salary_high = comparable_players['salary'][0]
     if type_var == 'Showdown':
-        comp_salary_low = comparable_players['salary'][0] - 1000
+        comp_salary_low = comparable_players['salary'][0] + comp_salary_below
+        comp_salary_high = comparable_players['salary'][0] + comp_salary_above
     else:
-        comp_salary_low = comparable_players['salary'][0] - 500
+        comp_salary_low = comparable_players['salary'][0] + comp_salary_below
+        comp_salary_high = comparable_players['salary'][0] + comp_salary_above
     comp_projection_high = comparable_players['median'][0]
     if type_var == 'Showdown':
         comp_projection_low = comparable_players['median'][0] - (comparable_players['median'][0] * .5)
     else:
-        comp_projection_low = comparable_players['median'][0] - (comparable_players['median'][0] * .75)
+        comp_projection_low = comparable_players['median'][0] - (comparable_players['median'][0] * .5)
     # players can be eligible at multiple positions, so we need to find all the positions the player is eligible at
     # the position column can have positions designated as 1B/OF which means they are eligible at 1B and OF
     comp_player_position = comparable_players['position'].tolist()
@@ -226,6 +228,8 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
     # key concept here is if they have a lineups to remove above 0 it means that we are trying to replace them with comparable players
     # if the lineups to remove is below zero it means we want to find comparable players and replace them with the exposure player
     if lineups_to_remove > 0:
+        print(f"Lineups to remove: {lineups_to_remove}")
+        print(f"Current Counter: {change_counter}")
         for row in random_row_indices_insert:
             if change_counter < math.ceil(lineups_to_remove):
                 if specific_replacements != []: