Allocation (%) can be updated

pages/ETF_Portfolio_Builder.py

@@ -1135,60 +1135,20 @@ def portfolio_summary(final_alloc: pd.DataFrame) -> None:
         return
 
     try:
-        # Calculate key metrics
+        # Use the allocation from session state for all calculations and display
-        total_capital = final_alloc["Capital Allocated ($)"].sum()
-        total_income = final_alloc["Income Contributed ($)"].sum()
-        
-        # Calculate weighted average yield
-        weighted_yield = (final_alloc["Allocation (%)"] * final_alloc["Yield (%)"]).sum() / 100
-        
-        # Display metrics in columns
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            st.metric("Total Capital", format_large_number(total_capital))
-            
-        with col2:
-            st.metric("Annual Income", format_large_number(total_income))
-            st.metric("Monthly Income", format_large_number(total_income/12))
-            
-        with col3:
-            st.metric("Average Yield", f"{weighted_yield:.2f}%")
-            st.metric("Effective Yield", f"{(total_income/total_capital*100):.2f}%")
-        
-        # Display allocation chart
-        fig = px.pie(
-            final_alloc,
-            values="Allocation (%)",
-            names="Ticker",
-            title="Portfolio Allocation by ETF",
-            hover_data={
-                "Ticker": True,
-                "Allocation (%)": ":.2f",
-                "Yield (%)": ":.2f",
-                "Capital Allocated ($)": ":,.2f",
-                "Income Contributed ($)": ":,.2f"
-            }
-        )
-        st.plotly_chart(fig, use_container_width=True)
-        
-        # Display detailed allocation table
-        st.subheader("Detailed Allocation")
         display_df = final_alloc.copy()
+        numeric_columns = ["Allocation (%)", "Yield (%)", "Price", "Shares", "Capital Allocated ($)", "Income Contributed ($)"]
+        for col in numeric_columns:
+            if col in display_df.columns:
+                display_df[col] = pd.to_numeric(display_df[col], errors='coerce')
         display_df["Monthly Income"] = display_df["Income Contributed ($)"] / 12
-        
+        display_df["Capital Allocated ($)"] = display_df["Capital Allocated ($)"].apply(lambda x: f"${float(x):,.2f}")
-        # Format large numbers in the display DataFrame
+        display_df["Income Contributed ($)"] = display_df["Income Contributed ($)"].apply(lambda x: f"${float(x):,.2f}")
-        display_df["Capital Allocated ($)"] = display_df["Capital Allocated ($)"].apply(format_large_number)
+        display_df["Monthly Income"] = display_df["Monthly Income"].apply(lambda x: f"${float(x):,.2f}")
-        display_df["Income Contributed ($)"] = display_df["Income Contributed ($)"].apply(format_large_number)
-        display_df["Monthly Income"] = display_df["Monthly Income"].apply(format_large_number)
-        
-        # Ensure data_source column exists and rename it for display
         if "data_source" in display_df.columns:
             display_df = display_df.rename(columns={"data_source": "Data Source"})
         else:
             display_df["Data Source"] = "Unknown"
-        
-        # Select and order columns for display
         display_columns = [
             "Ticker",
             "Allocation (%)",
@@ -1201,15 +1161,43 @@ def portfolio_summary(final_alloc: pd.DataFrame) -> None:
             "Risk Level",
             "Data Source"
         ]
-        
+        editor_key = "allocation_editor"
-        # Format the display
+        def parse_currency(val):
-        st.dataframe(
+            if isinstance(val, str):
-            display_df[display_columns].style.format({
+                return float(val.replace('$', '').replace(',', ''))
-                "Allocation (%)": "{:.2f}%",
+            return float(val)
-                "Yield (%)": "{:.2f}%",
+        # --- Portfolio Summary Metrics (use session state allocation) ---
-                "Price": "${:,.2f}",
+        total_capital = display_df["Capital Allocated ($)"].apply(parse_currency).sum()
-                "Shares": "{:,.4f}"
+        total_income = display_df["Income Contributed ($)"].apply(parse_currency).sum()
-            }),
+        weighted_yield = (display_df["Allocation (%)"] * display_df["Yield (%)"]).sum() / 100
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("Total Capital", f"${total_capital:,.2f}")
+        with col2:
+            st.metric("Annual Income", f"${total_income:,.2f}")
+            st.metric("Monthly Income", f"${total_income/12:,.2f}")
+        with col3:
+            st.metric("Average Yield", f"{weighted_yield:.2f}%")
+            st.metric("Effective Yield", f"{(total_income/total_capital*100):.2f}%")
+        # --- Pie chart (use session state allocation) ---
+        fig = px.pie(
+            display_df,
+            values="Allocation (%)",
+            names="Ticker",
+            title="Portfolio Allocation by ETF",
+            hover_data={
+                "Ticker": True,
+                "Allocation (%)": ":.2f",
+                "Yield (%)": ":.2f",
+                "Capital Allocated ($)": ":,.2f",
+                "Income Contributed ($)": ":,.2f"
+            }
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        # --- Editable allocation table below the pie chart ---
+        st.subheader("Detailed Allocation")
+        edited_df = st.data_editor(
+            display_df[display_columns],
             column_config={
                 "Ticker": st.column_config.TextColumn("Ticker", disabled=True),
                 "Allocation (%)": st.column_config.NumberColumn(
@@ -1220,9 +1208,21 @@ def portfolio_summary(final_alloc: pd.DataFrame) -> None:
                     format="%.1f",
                     required=True
                 ),
-                "Yield (%)": st.column_config.TextColumn("Yield (%)", disabled=True),
+                "Yield (%)": st.column_config.NumberColumn(
-                "Price": st.column_config.TextColumn("Price", disabled=True),
+                    "Yield (%)",
-                "Shares": st.column_config.TextColumn("Shares", disabled=True),
+                    format="%.2f",
+                    disabled=True
+                ),
+                "Price": st.column_config.NumberColumn(
+                    "Price",
+                    format="%.2f",
+                    disabled=True
+                ),
+                "Shares": st.column_config.NumberColumn(
+                    "Shares",
+                    format="%.2f",
+                    disabled=True
+                ),
                 "Capital Allocated ($)": st.column_config.TextColumn("Capital Allocated ($)", disabled=True),
                 "Monthly Income": st.column_config.TextColumn("Monthly Income", disabled=True),
                 "Income Contributed ($)": st.column_config.TextColumn("Income Contributed ($)", disabled=True),
@@ -1230,9 +1230,35 @@ def portfolio_summary(final_alloc: pd.DataFrame) -> None:
                 "Data Source": st.column_config.TextColumn("Data Source", disabled=True)
             },
             hide_index=True,
-            use_container_width=True
+            use_container_width=True,
+            key=editor_key
         )
-        
+        edited_df = edited_df.reset_index(drop=True)
+        # --- Update Allocations Button ---
+        if st.button("Update Allocations", type="primary"):
+            try:
+                edited_df["Allocation (%)"] = pd.to_numeric(edited_df["Allocation (%)"], errors='coerce')
+                total_allocation = edited_df["Allocation (%)"].sum()
+                if abs(total_allocation - 100.0) > 0.01:
+                    st.error(f"Total allocation must be 100%. Current total: {total_allocation:.2f}%")
+                else:
+                    edited_df["Capital Allocated ($)"] = edited_df["Capital Allocated ($)"].apply(parse_currency)
+                    edited_df["Income Contributed ($)"] = edited_df["Income Contributed ($)"].apply(parse_currency)
+                    edited_df["Monthly Income"] = edited_df["Monthly Income"].apply(parse_currency)
+                    total_capital = edited_df["Capital Allocated ($)"].sum()
+                    for idx, row in edited_df.iterrows():
+                        edited_df.at[idx, "Capital Allocated ($)"] = total_capital * (row["Allocation (%)"] / 100)
+                        edited_df.at[idx, "Income Contributed ($)"] = edited_df.at[idx, "Capital Allocated ($)"] * (row["Yield (%)"] / 100)
+                        edited_df.at[idx, "Monthly Income"] = edited_df.at[idx, "Income Contributed ($)"] / 12
+                        edited_df.at[idx, "Shares"] = edited_df.at[idx, "Capital Allocated ($)"] / row["Price"]
+                    # Update the session state with new allocations and trigger rerun
+                    st.session_state.final_alloc = edited_df
+                    st.success("Allocations updated successfully! Click 'Run Portfolio Simulation' to recalculate.")
+                    st.rerun()
+            except Exception as e:
+                st.error(f"Error updating allocations: {str(e)}")
+                logger.error(f"Error in allocation update: {str(e)}")
+                logger.error(traceback.format_exc())
     except Exception as e:
         st.error(f"Error calculating portfolio summary: {str(e)}")
         logger.error(f"Error in portfolio_summary: {str(e)}")