I have a Python script for a portfolio optimizer using Streamlit. The script includes functionality to compare two portfolios. However, there are some bugs in the "Portfolio vs Portfolio" comparison section. I need help identifying and fixing these bugs. Below is the relevant code snippet for the "Portfolio vs Portfolio" comparison.

```python
elif step == "Run Backtest":
    st.title("📊 Run Backtest")
    if st.session_state.portfolios:
        with st.form("compare_form"):
            st.subheader("🔍 Select Portfolios to Compare")
            comparison_type = st.selectbox(
                "Comparison Type",
                ["Portfolio vs Benchmark", "Portfolio vs Portfolio"]
            )
            portfolio_names = [p['name'] for p in st.session_state.portfolios]
            if comparison_type == "Portfolio vs Benchmark":
                portfolio1 = st.selectbox("Select Portfolio", portfolio_names)
            else:
                portfolio1 = st.selectbox("Select First Portfolio", portfolio_names, key="p1")
                portfolio2 = st.selectbox("Select Second Portfolio", portfolio_names, key="p2")
            run = st.form_submit_button("Run Backtest")

        if run:
            with st.spinner("Running backtest..."):
                if comparison_type == "Portfolio vs Benchmark":
                    # Code for Portfolio vs Benchmark comparison
                    pass
                elif comparison_type == "Portfolio vs Portfolio":
                    if portfolio1 == portfolio2:
                        st.error("Please select two different portfolios for comparison.")
                    else:
                        portfolio_a = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)
                        portfolio_b = next(p for p in st.session_state.portfolios if p['name'] == portfolio2)

                        overlap_start = max(portfolio_a['start_date'], portfolio_b['start_date'])
                        overlap_end = min(portfolio_a['end_date'], portfolio_b['end_date'])

                        if overlap_start >= overlap_end:
                            st.error("The selected portfolios do not have overlapping date ranges.")
                            st.stop()

                        price_data_a = download_data(
                            portfolio_a['selected'],
                            overlap_start,
                            overlap_end
                        )
                        price_data_b = download_data(
                            portfolio_b['selected'],
                            overlap_start,
                            overlap_end
                        )

                        if price_data_a.empty:
                            st.error(f"No price data available for portfolio '{portfolio_a['name']}' in the overlapping period.")
                            st.stop()
                        if price_data_b.empty:
                            st.error(f"No price data available for portfolio '{portfolio_b['name']}' in the overlapping period.")
                            st.stop()

                        # Backtest Portfolio A
                        available_a = [ticker for ticker in portfolio_a['selected'] if ticker in price_data_a.columns]
                        missing_a = list(set(portfolio_a['selected']) - set(available_a))
                        if missing_a:
                            st.warning(f"Excluded tickers from '{portfolio_a['name']}': {', '.join(missing_a)}")
                        if not available_a:
                            st.error(f"No valid tickers for portfolio '{portfolio_a['name']}' in the overlapping period.")
                            st.stop()
                        allocs_a = [portfolio_a['allocations'][i] for i, ticker in enumerate(portfolio_a['selected']) if ticker in available_a]
                        if not np.isclose(sum(allocs_a), 100.0, atol=1e-4):
                            st.warning(f"Allocations for '{portfolio_a['name']}' do not sum to 100%. Adjusting allocations proportionally.")
                            allocs_a = [a / sum(allocs_a) * 100 for a in allocs_a]
                        weights_a = np.array(allocs_a) / 100
                        returns_a, cum_returns_a = backtest(weights_a, price_data_a[available_a],
                                                           portfolio_a['rebalance_freq'], portfolio_a['broker_fee'], debug=False)

                        # Backtest Portfolio B
                        available_b = [ticker for ticker in portfolio_b['selected'] if ticker in price_data_b.columns]
                        missing_b = list(set(portfolio_b['selected']) - set(available_b))
                        if missing_b:
                            st.warning(f"Excluded tickers from '{portfolio_b['name']}': {', '.join(missing_b)}")
                        if not available_b:
                            st.error(f"No valid tickers for portfolio '{portfolio_b['name']}' in the overlapping period.")
                            st.stop()
                        allocs_b = [portfolio_b['allocations'][i] for i, ticker in enumerate(portfolio_b['selected']) if ticker in available_b]
                        if not np.isclose(sum(allocs_b), 100.0, atol=1e-4):
                            st.warning(f"Allocations for '{portfolio_b['name']}' do not sum to 100%. Adjusting allocations proportionally.")
                            allocs_b = [a / sum(allocs_b) * 100 for a in allocs_b]
                        weights_b = np.array(allocs_b) / 100
                        returns_b, cum_returns_b = backtest(weights_b, price_data_b[available_b],
                                                           portfolio_b['rebalance_freq'], portfolio_b['broker_fee'], debug=False)

                        if cum_returns_a.empty or cum_returns_b.empty:
                            st.error("One of the portfolios has empty cumulative returns. Check the data and allocations.")
                            st.stop()

                        # Align date ranges between the two portfolios
                        common_index = cum_returns_a.index.intersection(cum_returns_b.index)
                        if common_index.empty:
                            st.error("No overlapping dates between the two portfolios after backtesting.")
                            st.stop()
                        returns_a = returns_a.loc[common_index]
                        cum_returns_a = cum_returns_a.loc[common_index]
                        returns_b = returns_b.loc[common_index]
                        cum_returns_b = cum_returns_b.loc[common_index]

                        # Calculate metrics without using one portfolio as the benchmark for the other
                        metrics_a = calculate_portfolio_metrics(
                            returns_a, cum_returns_a, portfolio_a['rf_rate'], benchmark_returns=None
                        )
                        metrics_b = calculate_portfolio_metrics(
                            returns_b, cum_returns_b, portfolio_b['rf_rate'], benchmark_returns=None
                        )

                        # Dashboard Header with Key Stats
                        with st.container():
                            st.markdown("### 🔑 Key Metrics")
                            key_metrics_a = {
                                'Annualized Return (CAGR)': metrics_a.get('Annualized Return (CAGR)', "N/A"),
                                'Sharpe Ratio': metrics_a.get('Sharpe Ratio', "N/A"),
                                'Maximum Drawdown': metrics_a.get('Maximum Drawdown', "N/A")
                            }
                            key_metrics_b = {
                                'Annualized Return (CAGR)': metrics_b.get('Annualized Return (CAGR)', "N/A"),
                                'Sharpe Ratio': metrics_b.get('Sharpe Ratio', "N/A"),
                                'Maximum Drawdown': metrics_b.get('Maximum Drawdown', "N/A")
                            }
                            cols = st.columns(len(key_metrics_a))
                            for col, (metric, value) in zip(cols, key_metrics_a.items()):
                                with col:
                                    st.metric(label=f"{portfolio_a['name']} {metric}", value=value)
                            for col, (metric, value) in zip(cols, key_metrics_b.items()):
                                with col:
                                    st.metric(label=f"{portfolio_b['name']} {metric}", value=value)

                        # Create Tabs for Organized Sections
                        tabs = st.tabs(["Overview", "Performance Statistics", "Advanced Metrics", "Drawdowns", "Visualizations"])

                        with tabs[0]:
                            st.header("📈 Portfolio Performance Overview")
                            st.write(f"**Portfolio A Name:** {portfolio_a['name']}")
                            st.write(f"**Portfolio B Name:** {portfolio_b['name']}")
                            st.write(f"**Start Date:** {portfolio_a['start_date']}")
                            st.write(f"**End Date:** {portfolio_a['end_date']}")

                        with tabs[1]:
                            st.header("📊 Performance Statistics")
                            with st.expander("🛠️ Basic Metrics", expanded=True):
                                performance_data_stats = {
                                    'Metric': [
                                        'Start Balance',
                                        'End Balance',
                                        'Annualized Return (CAGR)',
                                        'Standard Deviation (Annualized)',
                                        'Best Year',
                                        'Worst Year',
                                        'Maximum Drawdown',
                                        'Sharpe Ratio',
                                        'Sortino Ratio',
                                        'Benchmark Correlation'
                                    ],
                                    portfolio_a['name']: [
                                        metrics_a['Start Balance'],
                                        metrics_a['End Balance'],
                                        metrics_a['Annualized Return (CAGR)'],
                                        metrics_a['Standard Deviation (Annualized)'],
                                        metrics_a['Best Year'],
                                        metrics_a['Worst Year'],
                                        metrics_a['Maximum Drawdown'],
                                        metrics_a['Sharpe Ratio'],
                                        metrics_a['Sortino Ratio'],
                                        metrics_a['Benchmark Correlation']
                                    ],
                                    portfolio_b['name']: [
                                        metrics_b['Start Balance'],
                                        metrics_b['End Balance'],
                                        metrics_b['Annualized Return (CAGR)'],
                                        metrics_b['Standard Deviation (Annualized)'],
                                        metrics_b['Best Year'],
                                        metrics_b['Worst Year'],
                                        metrics_b['Maximum Drawdown'],
                                        metrics_b['Sharpe Ratio'],
                                        metrics_b['Sortino Ratio'],
                                        metrics_b['Benchmark Correlation']
                                    ]
                                }
                                performance_df_stats = pd.DataFrame(performance_data_stats).set_index('Metric')
                                st.table(performance_df_stats)

                        with tabs[2]:
                            st.header("📉 Advanced Metrics")
                            with st.expander("📈 Advanced Metrics", expanded=False):
                                performance_data_advanced = {
                                    'Metric': list(metrics_a.keys()),
                                    portfolio_a['name']: list(metrics_a.values()),
                                    portfolio_b['name']: list(metrics_b.values())
                                }
                                performance_df_advanced = pd.DataFrame(performance_data_advanced).set_index('Metric')
                                st.table(performance_df_advanced)

                        with tabs[3]:
                            st.header("📉 Drawdowns")
                            st.markdown("### 📉 Detailed Drawdowns")
                            st.markdown(f"#### 📉 Drawdowns for {portfolio_a['name']}")
                            drawdowns_a = get_drawdown_details(cum_returns_a)
                            if drawdowns_a:
                                drawdowns_a_df = pd.DataFrame(drawdowns_a)
                                st.table(drawdowns_a_df)
                            else:
                                st.write("No drawdowns detected.")

                            st.markdown(f"#### 📉 Drawdowns for {portfolio_b['name']}")
                            drawdowns_b = get_drawdown_details(cum_returns_b)
                            if drawdowns_b:
                                drawdowns_b_df = pd.DataFrame(drawdowns_b)
                                st.table(drawdowns_b_df)
                            else:
                                st.write("No drawdowns detected.")

                        with tabs[4]:
                            st.header("📊 Visualizations")
                            # Growth Comparison
                            fig_growth = px.line(title='Growth Comparison')
                            fig_growth.add_scatter(x=cum_returns_a.index, y=cum_returns_a, mode='lines', name=portfolio_a['name'])
                            fig_growth.add_scatter(x=cum_returns_b.index, y=cum_returns_b, mode='lines', name=portfolio_b['name'])
                            st.plotly_chart(fig_growth, use_container_width=True)

                            # Drawdown Comparison
                            portfolio_drawdown_series_a = (cum_returns_a / cum_returns_a.expanding().max() - 1) * 100
                            portfolio_drawdown_series_b = (cum_returns_b / cum_returns_b.expanding().max() - 1) * 100
                            fig_drawdown = px.line(title='Drawdown Comparison')
                            fig_drawdown.add_scatter(x=portfolio_drawdown_series_a.index, y=portfolio_drawdown_series_a, mode='lines', name=portfolio_a['name'])
                            fig_drawdown.add_scatter(x=portfolio_drawdown_series_b.index, y=portfolio_drawdown_series_b, mode='lines', name=portfolio_b['name'])
                            st.plotly_chart(fig_drawdown, use_container_width=True)

                            # Box Plot for Returns Distribution
                            st.subheader("📦 Returns Distribution Box Plot")
                            fig_box_plot_a = px.box(returns_a, title=f'Returns Distribution Box Plot for {portfolio_a["name"]}', labels={'value': 'Returns', 'variable': 'Asset'})
                            fig_box_plot_b = px.box(returns_b, title=f'Returns Distribution Box Plot for {portfolio_b["name"]}', labels={'value': 'Returns', 'variable': 'Asset'})
                            st.plotly_chart(fig_box_plot_a, use_container_width=True)
                            st.plotly_chart(fig_box_plot_b, use_container_width=True)

                            # Heatmap of Correlations Between Assets
                            st.subheader("🔥 Correlation Heatmap of Assets")
                            plot_correlation_heatmap(price_data_a[available_a].pct_change().dropna())
                            plot_correlation_heatmap(price_data_b[available_b].pct_change().dropna())

                            # Portfolio Allocation Pie Chart
                            st.subheader("🥧 Portfolio Allocation Pie Chart")
                            plot_allocation_pie(weights_a, available_a, title=f"{portfolio_a['name']} Allocation")
                            plot_allocation_pie(weights_b, available_b, title=f"{portfolio_b['name']} Allocation")

                            # Final Score
                            st.markdown("### 📝 Final Score")
                            score_a = calculate_final_score(metrics_a, metrics_b)
                            score_b = calculate_final_score(metrics_b, metrics_a)
                            st.write(f"**{portfolio_a['name']}:** {score_a:.2f} / 100")
                            st.write(f"**{portfolio_b['name']}:** {score_b:.2f} / 100")

                            # Store results
                            st.session_state.backtest_results = {
                                'returns_a': returns_a,
                                'cum_returns_a': cum_returns_a,
                                'weights_a': weights_a,
                                'price_data_a': price_data_a[available_a],
                                'metrics_a': metrics_a,
                                'returns_b': returns_b,
                                'cum_returns_b': cum_returns_b,
                                'weights_b': weights_b,
                                'price_data_b': price_data_b[available_b],
                                'metrics_b': metrics_b,
                                'performance_df_stats': performance_df_stats,
                                'performance_df_advanced': performance_df_advanced,
                                'benchmark_cum_returns': pd.Series(dtype=float)
                            }
                            st.success("Backtest completed!")
```

**Issues:**
1. The "Portfolio vs Portfolio" comparison section seems to have some bugs, especially in the alignment of date ranges and the calculation of metrics.
2. The visualizations for the "Portfolio vs Portfolio" comparison are not displaying correctly.

**Tasks:**
1. Identify and fix the bugs in the "Portfolio vs Portfolio" comparison section.
2. Ensure that the date ranges are correctly aligned between the two portfolios.
3. Verify that the metrics and visualizations are correctly calculated and displayed.

Please help me fix these issues.

---

**Note:** The code provided is a snippet from a larger script. If additional context or code is needed to resolve the issues, please let me know.