Add trim slack parameter to portfolio trimming functionality in app.py and trim_portfolio.py, allowing for more flexible threshold adjustments during portfolio management.

app.py

@@ -912,6 +912,7 @@ with tab2:
                 with st.form(key='trim_form'):
                     performance_type = st.selectbox("Select Sorting variable", ['median', 'Finish_percentile'])
                     own_type = st.selectbox("Select trimming variable", ['Own', 'Geomean', 'Weighted Own'])
+                    trim_slack_var = st.number_input("Select trim slack (percentile addition to trimming variable ceiling)", value=0.0, min_value=0.0, max_value=1.0, step=0.1)
                     performance_threshold_high = st.number_input("Select performance threshold (will only trim if sorting variable is lower than this)", value=st.session_state['portfolio'][performance_type].max(), min_value=0.0, step=1.0)
                     performance_threshold_low = st.number_input("Select performance threshold (will only trim if sorting variable is higher than this)", value=0.0, min_value=0.0, step=1.0)
                     own_threshold_high = st.number_input("Select own threshold (will only trim if trimming variable is lower than this)", value=st.session_state['portfolio'][own_type].max(), min_value=0.0, step=1.0)
@@ -949,7 +950,7 @@ with tab2:
                             )
                             st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
                         
-                        st.session_state['portfolio'] = trim_portfolio(st.session_state['portfolio'], performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
+                        st.session_state['portfolio'] = trim_portfolio(st.session_state['portfolio'], trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
                         st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
 
         with col2:

global_func/trim_portfolio.py

@@ -1,12 +1,12 @@
 import pandas as pd
 
-def trim_portfolio(portfolio: pd.DataFrame, performance_type: str, own_type: str, performance_threshold_high: float, performance_threshold_low: float, own_threshold_high: float, own_threshold_low: float):
+def trim_portfolio(portfolio: pd.DataFrame, trim_slack: float, performance_type: str, own_type: str, performance_threshold_high: float, performance_threshold_low: float, own_threshold_high: float, own_threshold_low: float):
     if performance_type == 'Finish_percentile':
         working_portfolio = portfolio.sort_values(by=performance_type, ascending = True).reset_index(drop=True)
     else:
         working_portfolio = portfolio.sort_values(by=performance_type, ascending = False).reset_index(drop=True)
     rows_to_drop = []
-    curr_own_type_max = working_portfolio.loc[0, own_type]
+    curr_own_type_max = working_portfolio.loc[0, own_type] + (trim_slack * working_portfolio.loc[0, own_type])
 
     for i in range(1, len(working_portfolio)):
         if working_portfolio.loc[i, own_type] > curr_own_type_max and \
@@ -16,7 +16,7 @@ def trim_portfolio(portfolio: pd.DataFrame, performance_type: str, own_type: str
         working_portfolio.loc[i, own_type] <= own_threshold_high:
             rows_to_drop.append(i)
         else:
-            curr_own_type_max = working_portfolio.loc[i, own_type]
+            curr_own_type_max = working_portfolio.loc[i, own_type] + (trim_slack * working_portfolio.loc[0, own_type])
 
     working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
     return working_portfolio