To address the problem of the "Run Backtest" not working properly for the "Portfolio vs Benchmark" comparison, we need to focus on the relevant code segments that handle the backtesting process and the comparison with the benchmark. Below are the essential code segments extracted from the provided codebase:

### 1. **Function: `run_portfolio_comparison`**
   - **File/Class/Method:** `run_portfolio_comparison`
   - **Relevance:** This function is responsible for running the portfolio comparison against a benchmark or another portfolio. It handles the backtesting logic and returns the results.

```python
@handle_exceptions
def run_portfolio_comparison(comparison_type, portfolio1, portfolio2=None):
    """
    Generalized function to run portfolio comparisons.

    Parameters:
    - comparison_type (str): "vs_benchmark" or "vs_portfolio"
    - portfolio1 (dict): Primary portfolio.
    - portfolio2 (dict, optional): Secondary portfolio for "vs_portfolio".

    Returns:
    - dict: Backtest results or error.
    """
    try:
        if comparison_type == "vs_benchmark":
            available, missing, price_data = handle_portfolio_data(portfolio1)
            if not available:
                return {"error": "No valid tickers with available data."}
            allocations = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available])
            returns, cum_returns = backtest(
                weights=np.array(allocations) / 100,
                prices=price_data[available],
                rebalance_freq=portfolio1['rebalance_freq'],
                broker_fee=portfolio1['broker_fee']
            )
            if cum_returns.empty:
                return {"error": "Cumulative returns are empty."}
            # Benchmark processing...
            benchmark_data = download_data(
                [portfolio1['benchmark_symbol']],
                portfolio1['start_date'].strftime('%Y-%m-%d'),
                portfolio1['end_date'].strftime('%Y-%m-%d')
            )
            if benchmark_data.empty:
                return {"error": "Benchmark data could not be downloaded."}
            
            benchmark_returns, benchmark_cum_returns, benchmark_metrics = process_benchmark(benchmark_data, returns, portfolio1)
            portfolio_metrics = calculate_metrics(returns, cum_returns, portfolio1['rf_rate'], benchmark_returns)
            
            # Compile backtest results
            backtest_results = {
                'returns': returns,
                'cum_returns': cum_returns,
                'weights': allocations,
                'price_data': price_data[available],
                'available_selected': available,  # Added this line
                'metrics': portfolio_metrics,
                'benchmark_returns': benchmark_returns,
                'benchmark_cum_returns': benchmark_cum_returns,
                'benchmark_metrics': benchmark_metrics
            }
            
            # **Added Return Statement**
            return {"backtest_results": backtest_results}

        elif comparison_type == "vs_portfolio":
            overlap_start = max(portfolio1['start_date'], portfolio2['start_date'])
            overlap_end = min(portfolio1['end_date'], portfolio2['end_date'])
            if overlap_start >= overlap_end:
                return {"error": "No overlapping date ranges."}
            available_a, missing_a, price_data_a = handle_portfolio_data(portfolio1, (overlap_start, overlap_end))
            available_b, missing_b, price_data_b = handle_portfolio_data(portfolio2, (overlap_start, overlap_end))
            if not available_a or not available_b:
                return {"error": "One of the portfolios has no valid tickers with available data."}
            allocations_a = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available_a])
            allocations_b = adjust_allocations([portfolio2['allocations'][portfolio2['selected'].index(t)] for t in available_b])
            returns_a, cum_returns_a = backtest(
                weights=np.array(allocations_a) / 100,
                prices=price_data_a[available_a],
                rebalance_freq=portfolio1['rebalance_freq'],
                broker_fee=portfolio1['broker_fee']
            )
            returns_b, cum_returns_b = backtest(
                weights=np.array(allocations_b) / 100,
                prices=price_data_b[available_b],
                rebalance_freq=portfolio2['rebalance_freq'],
                broker_fee=portfolio2['broker_fee']
            )
            if cum_returns_a.empty or cum_returns_b.empty:
                return {"error": "One of the cumulative returns is empty."}
            common_index = cum_returns_a.index.intersection(cum_returns_b.index)
            if common_index.empty:
                return {"error": "No overlapping dates after backtesting."}
            returns_a = returns_a.loc[common_index]
            returns_b = returns_b.loc[common_index]
            cum_returns_a = cum_returns_a.loc[common_index]
            cum_returns_b = cum_returns_b.loc[common_index]
            metrics_a = calculate_metrics(returns_a, cum_returns_a, portfolio1['rf_rate'], None)
            metrics_b = calculate_metrics(returns_b, cum_returns_b, portfolio2['rf_rate'], None)
            backtest_results = {
                'returns_a': returns_a,
                'cum_returns_a': cum_returns_a,
                'weights_a': allocations_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': allocations_b,
                'price_data_b': price_data_b[available_b],
                'metrics_b': metrics_b
            }
            return {"backtest_results": backtest_results}
    except Exception as e:
        return {"error": f"An unexpected error occurred: {e}"}
```

### 2. **Function: `process_benchmark`**
   - **File/Class/Method:** `process_benchmark`
   - **Relevance:** This function processes the benchmark data and calculates the benchmark metrics. It ensures that the benchmark returns are aligned with the portfolio returns.

```python
def process_benchmark(benchmark_data, returns, portfolio):
    """Processes benchmark data and calculates benchmark metrics."""
    benchmark_returns = benchmark_data[portfolio['benchmark_symbol']].pct_change().dropna()
    benchmark_returns = benchmark_returns.reindex(returns.index, method='ffill').dropna()
    common_index = returns.index.intersection(benchmark_returns.index)
    if common_index.empty:
        st.error("No overlapping dates between portfolio returns and benchmark returns after alignment.")
        st.stop()
    returns_aligned = returns.loc[common_index]
    cum_returns_aligned = (1 + benchmark_returns.loc[common_index]).cumprod()
    benchmark_metrics = calculate_metrics(
        benchmark_returns, cum_returns_aligned, portfolio['rf_rate'], None
    )
    return benchmark_returns, cum_returns_aligned, benchmark_metrics
```

### 3. **Function: `calculate_metrics`**
   - **File/Class/Method:** `calculate_metrics`
   - **Relevance:** This function calculates various performance metrics for the portfolio and the benchmark. It is crucial for generating the backtest results.

```python
def calculate_metrics(
    returns: pd.Series, 
    cum_returns: pd.Series, 
    rf: float = 0.02, 
    benchmark_returns: pd.Series = None
) -> dict:
    """
    Calculate comprehensive portfolio metrics, optionally relative to a benchmark.

    Parameters:
    - returns (pd.Series): Portfolio returns.
    - cum_returns (pd.Series): Cumulative returns of the portfolio.
    - rf (float): Risk-free rate.
    - benchmark_returns (pd.Series, optional): Benchmark returns.

    Returns:
    - dict: Dictionary of calculated metrics.
    """
    metrics = {}
    metrics['Start Balance'] = "$10,000.00"
    if not cum_returns.empty and not returns.empty:
        end_balance = 10000 * cum_returns.iloc[-1]
        years = (cum_returns.index[-1] - cum_returns.index[0]).days / 365.25
        try:
            cagr = (end_balance / 10000) ** (1 / years) - 1 if years > 0 else np.nan
        except:
            cagr = np.nan
        metrics.update({
            'End Balance': f"${end_balance:,.2f}",
            'Annualized Return (CAGR)': f"{cagr * 100:.2f}%" if not np.isnan(cagr) else "N/A",
            'Best Year': f"{returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).max() * 100:.2f}%" if not returns.empty else "N/A",
            'Worst Year': f"{returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).min() * 100:.2f}%" if not returns.empty else "N/A",
            'Arithmetic Mean (Monthly)': f"{returns.mean() * 100:.2f}%",
            'Arithmetic Mean (Annualized)': f"{returns.mean() * 252 * 100:.2f}%",
            'Geometric Mean (Monthly)': f"{(np.exp(np.log1p(returns).mean()) - 1) * 100:.2f}%" if not returns.empty else "N/A",
            'Geometric Mean (Annualized)': f"{(np.exp(np.log1p(returns).mean() * 252) - 1) * 100:.2f}%" if not returns.empty else "N/A",
            'Standard Deviation (Monthly)': f"{returns.std() * 100:.2f}%",
            'Standard Deviation (Annualized)': f"{returns.std() * np.sqrt(252) * 100:.2f}%",
            'Downside Deviation (Monthly)': f"{returns[returns < 0].std() * 100:.2f}%" if not returns.empty else "N/A",
            'Maximum Drawdown': f"{drawdown(cum_returns) * 100:.2f}%" if not cum_returns.empty else "N/A",
            'Sharpe Ratio': f"{calculate_sharpe_ratio(returns, rf):.2f}" if not cum_returns.empty else "N/A",
            'Sortino Ratio': f"{calculate_sortino_ratio(returns, rf):.2f}" if not cum_returns.empty else "N/A",
            'Gain/Loss Ratio': f"{calculate_gain_loss_ratio(returns):.2f}" if not cum_returns.empty else "N/A",
            'Skewness': f"{skew(returns):.2f}" if not returns.empty else "N/A",
            'Excess Kurtosis': f"{kurtosis(returns):.2f}" if not returns.empty else "N/A",
            'Safe Withdrawal Rate': f"{calculate_safe_withdrawal_rate(returns):.6f}%" if not returns.empty else "N/A",
            'Perpetual Withdrawal Rate': f"{calculate_perpetual_withdrawal_rate(returns):.6f}%" if not returns.empty else "N/A",
            'Positive Periods': calculate_positive_periods(returns) if not returns.empty else "N/A"
        })
    else:
        # Assign "N/A" to all metrics if cum_returns or returns are empty
        metrics.update({
            'End Balance': "N/A",
            'Annualized Return (CAGR)': "N/A",
            'Best Year': "N/A",
            'Worst Year': "N/A",
            'Arithmetic Mean (Monthly)': "N/A",
            'Arithmetic Mean (Annualized)': "N/A",
            'Geometric Mean (Monthly)': "N/A",
            'Geometric Mean (Annualized)': "N/A",
            'Standard Deviation (Monthly)': "N/A",
            'Standard Deviation (Annualized)': "N/A",
            'Downside Deviation (Monthly)': "N/A",
            'Maximum Drawdown': "N/A",
            'Sharpe Ratio': "N/A",
            'Sortino Ratio': "N/A",
            'Gain/Loss Ratio': "N/A",
            'Skewness': "N/A",
            'Excess Kurtosis': "N/A",
            'Safe Withdrawal Rate': "N/A",
            'Perpetual Withdrawal Rate': "N/A",
            'Positive Periods': "N/A"
        })
    
    if benchmark_returns is not None and not benchmark_returns.empty:
        correlation = returns.corr(benchmark_returns)
        beta = calculate_beta(returns, benchmark_returns)
        alpha = calculate_alpha(returns, benchmark_returns, rf)
        r2 = calculate_r_squared(returns, benchmark_returns)
        treynor = calculate_treynor_ratio(returns, benchmark_returns, rf)
        calmar = calculate_calmar_ratio(returns, cum_returns)
        m2 = calculate_modigliani_miller(returns, benchmark_returns, rf)
        info_ratio = calculate_information_ratio(returns, benchmark_returns)
        tracking_error = calculate_tracking_error(returns, benchmark_returns)
        active_return = calculate_active_return(returns, benchmark_returns)
        upside_capture = calculate_capture_ratio(returns, benchmark_returns, upside=True)
        downside_capture = calculate_capture_ratio(returns, benchmark_returns, upside=False)
        
        metrics.update({
            'Benchmark Correlation': f"{correlation:.2f}" if not np.isnan(correlation) else "N/A",
            'Beta': f"{beta:.2f}" if not np.isnan(beta) else "N/A",
            'Alpha (annualized)': f"{alpha * 100:.2f}%" if not np.isnan(alpha) else "N/A",
            'R2': f"{r2 * 100:.2f}%" if not np.isnan(r2) else "N/A",
            'Treynor Ratio': f"{treynor:.2f}" if not np.isnan(treynor) else "N/A",
            'Calmar Ratio': f"{calmar:.2f}" if not np.isnan(calmar) else "N/A",
            'Modigliani–Modigliani Measure': f"{m2 * 100:.2f}%" if not np.isnan(m2) else "N/A",
            'Information Ratio': f"{info_ratio:.2f}" if not np.isnan(info_ratio) else "N/A",
            'Tracking Error': f"{tracking_error * 100:.2f}%" if not np.isnan(tracking_error) else "N/A",
            'Active Return': f"{active_return:.2f}%" if not np.isnan(active_return) else "N/A",
            'Upside Capture Ratio': f"{upside_capture:.2f}%" if not np.isnan(upside_capture) else "N/A",
            'Downside Capture Ratio': f"{downside_capture:.2f}%" if not np.isnan(downside_capture) else "N/A"
        })
    else:
        # Assign "N/A" to all benchmark metrics
        metrics.update({
            'Benchmark Correlation': "N/A",
            'Beta': "N/A",
            'Alpha (annualized)': "N/A",
            'R2': "N/A",
            'Treynor Ratio': "N/A",
            'Calmar Ratio': "N/A",
            'Modigliani–Modigliani Measure': "N/A",
            'Information Ratio': "N/A",
            'Tracking Error': "N/A",
            'Active Return': "N/A",
            'Upside Capture Ratio': "N/A",
            'Downside Capture Ratio': "N/A"
        })
    
    return metrics
```

### 4. **Function: `display_portfolio_vs_benchmark`**
   - **File/Class/Method:** `display_portfolio_vs_benchmark`
   - **Relevance:** This function displays the backtest results for the portfolio versus the benchmark. It includes visualizations and key performance indicators.

```python
def display_portfolio_vs_benchmark(results):
    """
    Enhanced display function for Portfolio vs Benchmark comparison with improved visualizations and UX.
    """
    # Extract data from results
    returns = results['returns']
    cum_returns = results['cum_returns']
    weights = results['weights']
    price_data = results['price_data']
    available_selected = results['available_selected']  # Added this line to retrieve available_selected
    metrics = results['metrics']
    benchmark_metrics = results['benchmark_metrics']
    benchmark_returns = results['benchmark_returns']
    benchmark_cum_returns = results['benchmark_cum_returns']

    # Create tabs for better organization
    main_tab, metrics_tab, risk_tab, analysis_tab = st.tabs([
        "📈 Main Charts", "📊 Performance Metrics", "🎯 Risk Analysis", "🔍 Deep Analysis"
    ])

    with main_tab:
        col1, col2 = st.columns(2)
        with col1:
            st.subheader("Portfolio Composition")
            fig_composition = px.pie(
                values=weights,
                names=price_data.columns,
                title="Current Portfolio Allocation",
                template=THEME
            )
            st.plotly_chart(fig_composition, use_container_width=True)

        with col2:
            st.subheader("Key Performance Indicators")
            metrics_display = {
                "Total Return": f"{(cum_returns.iloc[-1] - 1) * 100:.2f}%",
                "Annualized Return": metrics.get('Annualized Return (CAGR)', 'N/A'),
                "Sharpe Ratio": metrics.get('Sharpe Ratio', 'N/A'),
                "Max Drawdown": metrics.get('Maximum Drawdown', 'N/A')
            }
            for metric, value in metrics_display.items():
                st.metric(metric, value)

        # Enhanced Cumulative Returns Plot
        st.subheader("Cumulative Returns Comparison")
        fig_returns = go.Figure()
        fig_returns.add_trace(go.Scatter(
            x=cum_returns.index,
            y=cum_returns,
            name="Portfolio",
            line=dict(color='cyan', width=2)
        ))
        fig_returns.add_trace(go.Scatter(
            x=benchmark_cum_returns.index,
            y=benchmark_cum_returns,
            name="Benchmark",
            line=dict(color='orange', width=2, dash='dash')
        ))
        fig_returns.update_layout(
            template=THEME,
            hovermode='x unified',
            height=500
        )
        st.plotly_chart(fig_returns, use_container_width=True)
        # Add after the cumulative returns chart
        create_summary_recommendations('overview', metrics=metrics)

    with metrics_tab:
        st.subheader("Detailed Performance Metrics")
        
        # Create comparison metrics table
        metrics_comparison = pd.DataFrame({
            'Metric': metrics.keys(),
            "Portfolio": metrics.values(),
            "Benchmark": benchmark_metrics.values()
        })
        
        # Style the metrics table
        def highlight_better(row):
            styles = [''] * len(row)
            if isinstance(row["Portfolio"], (int, float)) and isinstance(row["Benchmark"], (int, float)):
                if 'Drawdown' in row['Metric'] or 'Risk' in row['Metric']:
                    better = row["Portfolio"] < row["Benchmark"]
                else:
                    better = row["Portfolio"] > row["Benchmark"]
                styles[1] = 'background-color: lightgreen' if better else 'background-color: lightcoral'
            return styles
        
        styled_metrics = metrics_comparison.style.apply(highlight_better, axis=1)
        st.dataframe(styled_metrics, height=400)
        # Add after the styled metrics dataframe
        create_summary_recommendations('comparison', metrics=metrics)

    with risk_tab:
        col1, col2 = st.columns(2)
        
        with col1:
            # Rolling Volatility
            st.subheader("Rolling Volatility")
            rolling_vol = returns.rolling(window=30).std() * np.sqrt(252)
            rolling_vol_bench = benchmark_returns.rolling(window=30).std() * np.sqrt(252)
            
            fig_vol = go.Figure()
            fig_vol.add_trace(go.Scatter(
                x=rolling_vol.index,
                y=rolling_vol,
                name="Portfolio",
                line=dict(color='cyan')
            ))
            fig_vol.add_trace(go.Scatter(
                x=rolling_vol_bench.index,
                y=rolling_vol_bench,
                name="Benchmark",
                line=dict(color='orange', dash='dash')
            ))
            st.plotly_chart(fig_vol, use_container_width=True)

        with col2:
            # Rolling Beta
            st.subheader("Rolling Beta")
            rolling_covar = returns.rolling(window=30).cov(benchmark_returns)
            rolling_var = benchmark_returns.rolling(window=30).var()
            rolling_beta = rolling_covar / rolling_var
            
            fig_beta = px.line(
                rolling_beta,
                title="30-Day Rolling Beta",
                template=THEME
            )
            st.plotly_chart(fig_beta, use_container_width=True)
            # Add after the rolling beta chart
            create_summary_recommendations('risk', metrics=metrics)

    with analysis_tab:
        col1, col2 = st.columns(2)
        
        with col1:
            # Monthly Returns Heatmap
            st.subheader("Monthly Returns Heatmap")
            monthly_returns = returns.resample('M').agg(lambda x: (1 + x).prod() - 1)
            monthly_returns_matrix = monthly_returns.groupby([monthly_returns.index.year, monthly_returns.index.month]).first().unstack()
            
            fig_heatmap = px.imshow(
                monthly_returns_matrix,
                labels=dict(x="Month", y="Year", color="Returns"),
                color_continuous_scale="RdYlGn",
                template=THEME
            )
            st.plotly_chart(fig_heatmap, use_container_width=True)

        with col2:
            # Rolling Correlation
            st.subheader("Rolling Correlation with Benchmark")
            rolling_corr = returns.rolling(window=30).corr(benchmark_returns)
            
            fig_corr = px.line(
                rolling_corr,
                title="30-Day Rolling Correlation",
                template=THEME
            )
            st.plotly_chart(fig_corr, use_container_width=True)

        # Risk Contribution Analysis
        st.subheader("Risk Contribution Analysis")
        risk_contrib = calculate_risk_contribution(price_data[available_selected].pct_change().dropna(), weights)
        fig_risk = px.pie(
            values=risk_contrib,
            names=price_data.columns,
            title="Risk Contribution by Asset",
            template=THEME
        )
        st.plotly_chart(fig_risk, use_container_width=True)
        # Add after the risk contribution analysis
        create_summary_recommendations('deep', metrics=metrics)
```

### Summary of Relevant Code Segments:
1. **`run_portfolio_comparison`**: This function orchestrates the backtesting process, including fetching data, calculating returns, and comparing the portfolio against the benchmark. It ensures that the benchmark data is aligned with the portfolio data and calculates the necessary metrics.
2. **`process_benchmark`**: This function processes the benchmark data, ensuring that the benchmark returns are aligned with the portfolio returns. It calculates the benchmark metrics.
3. **`calculate_metrics`**: This function calculates various performance metrics for both the portfolio and the benchmark, providing a comprehensive view of their performance.
4. **`display_portfolio_vs_benchmark`**: This function displays the backtest results, including visualizations and key performance indicators, to help users understand how the portfolio compares to the benchmark.

### Context:
These segments are crucial for ensuring that the backtest results are accurate and meaningful. They handle data processing, metric calculation, and visualization, which are essential for a proper "Portfolio vs Benchmark" comparison. If the backtest is not working properly, these functions should be reviewed and debugged to ensure that all data is correctly fetched, processed, and displayed.

fix the problem and return full code of updated part: the run backtest is not working properly for the Portfolio vs Benchmark