### Code Segments Related to the Problem

#### 1. **Button Click Handling**
   - **File/Class/Method:** `portfolio_optimizer.py` / `Streamlit Layout` / `Run Backtest`
   - **Relevance:** This segment is directly related to the problem of the "Run Backtest" button not triggering any reaction. The code checks if the button is clicked and then runs the backtest.

```python
run = st.button("Run Backtest")

if run:
    with st.spinner("Running backtest..."):
        if comparison_type == "Portfolio vs Benchmark":
            portfolio = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)
            result = run_portfolio_comparison("vs_benchmark", portfolio)
        else:
            if portfolio1 == portfolio2:
                st.error("Please select two different portfolios for comparison.")
                st.stop()
            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)
            result = run_portfolio_comparison("vs_portfolio", portfolio_a, portfolio_b)

            if result.get("error"):
                st.error(result["error"])
            else:
                display_backtest_results(result["backtest_results"], comparison_type, portfolio1, portfolio2)
```

#### 2. **Backtest Execution**
   - **File/Class/Method:** `portfolio_optimizer.py` / `run_portfolio_comparison`
   - **Relevance:** This function is responsible for running the backtest. It handles the comparison between portfolios or a portfolio and a benchmark.

```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":
            # Handle primary portfolio data
            available, missing, price_data = handle_portfolio_data(portfolio1)
            if not available:
                return {"error": "No valid tickers with available data."}
            
            # Adjust allocations based on available tickers
            allocations = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available])
            
            # Perform backtest for the portfolio
            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."}
            
            # Download benchmark data
            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."}
            
            # Process benchmark data and handle potential errors
            try:
                benchmark_returns, benchmark_cum_returns, benchmark_metrics = process_benchmark(benchmark_data, returns, portfolio1)
            except ValueError as ve:
                return {"error": str(ve)}
            except Exception as e:
                return {"error": f"Benchmark processing failed: {e}"}
            
            # Calculate metrics for the portfolio without benchmark comparison
            portfolio_metrics = calculate_metrics(returns, cum_returns, portfolio1['rf_rate'])
            
            # Compile backtest results
            backtest_results = {
                'returns': returns,
                'cum_returns': cum_returns,
                'weights': allocations,
                'price_data': price_data[available],
                'available_selected': available,
                'metrics': portfolio_metrics,
                'benchmark_returns': benchmark_returns,
                'benchmark_cum_returns': benchmark_cum_returns,
                'benchmark_metrics': benchmark_metrics
            }
            
            return {"backtest_results": backtest_results}

        elif comparison_type == "vs_portfolio":
            # Determine overlapping date range between the two portfolios
            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."}
            
            # Handle data for both portfolios within the overlapping date range
            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."}
            
            # Adjust allocations based on available tickers for both portfolios
            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])
            
            # Perform backtest for both portfolios
            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."}
            
            # Align two portfolios' returns
            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]
            
            # Calculate metrics for both portfolios
            metrics_a = calculate_metrics(returns_a, cum_returns_a, portfolio1['rf_rate'])
            metrics_b = calculate_metrics(returns_b, cum_returns_b, portfolio2['rf_rate'])
            
            # Compile backtest results
            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}"}
```

#### 3. **Display Backtest Results**
   - **File/Class/Method:** `portfolio_optimizer.py` / `display_backtest_results`
   - **Relevance:** This function is responsible for displaying the results of the backtest. It checks the comparison type and calls the appropriate display function.

```python
def display_backtest_results(results, comparison_type, portfolio1, portfolio2=None):
    if comparison_type == "Portfolio vs Benchmark":
        display_portfolio_vs_benchmark(results)
    else:
        # For portfolio vs portfolio comparison
        display_portfolio_vs_portfolio(results, portfolio1, portfolio2)
```

### Summary
The provided code segments are essential for handling the "Run Backtest" button click, executing the backtest, and displaying the results. If the button is not triggering any reaction, the issue might be related to the button click handling, the backtest execution, or the display of results. Ensure that all necessary conditions are met before the backtest is executed, and that the results are correctly passed to the display functions.

problem: clicked button Run Backtest, no reaction of the system
fix it and return full updated code