Enhance portfolio trimming functionality in app.py and trim_portfolio.py: add performance and own thresholds for trimming criteria, improving data filtering capabilities and allowing for more precise portfolio management.

app.py

@@ -912,7 +912,10 @@ 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'])
-                    
+                    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, step=1)
+                    performance_threshold_low = st.number_input("Select performance threshold (will only trim if sorting variable is higher than this)", value=0, min_value=0, step=1)
+                    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, step=1)
+                    own_threshold_low = st.number_input("Select own threshold (will only trim if trimming variable is higher than this)", value=0, min_value=0, step=1)
                     submitted = st.form_submit_button("Trim")
                     if submitted:
                         st.write('initiated')
@@ -946,7 +949,7 @@ 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)
+                        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'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
 
         with col2:

global_func/trim_portfolio.py

@@ -1,4 +1,6 @@
-def trim_portfolio(portfolio, performance_type, own_type):
+import pandas as pd
+
+def trim_portfolio(portfolio: pd.DataFrame, performance_type: str, own_type: str, performance_threshold_high: int, performance_threshold_low: int, own_threshold_high: int, own_threshold_low: int):
     if performance_type == 'Finish_percentile':
         working_portfolio = portfolio.sort_values(by=performance_type, ascending = True).reset_index(drop=True)
     else:
@@ -7,7 +9,11 @@ def trim_portfolio(portfolio, performance_type, own_type):
     curr_own_type_max = 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:
+        if working_portfolio.loc[i, own_type] > curr_own_type_max and \
+        working_portfolio.loc[i, performance_type] > performance_threshold_low and \
+        working_portfolio.loc[i, performance_type] <= performance_threshold_high and \
+        working_portfolio.loc[i, own_type] > own_threshold_low and \
+        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]