if current_step == "Optimize Portfolio":
    st.title("🔧 Optimize Portfolio")
    
    if not st.session_state.portfolios:
        st.warning("Please add at least one portfolio to optimize.")
    else:
        portfolio_names = [p['name'] for p in st.session_state.portfolios]
        selected_portfolio_name = st.selectbox("📁 Select Portfolio to Optimize", portfolio_names)
        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio_name)
        
        # Download and prepare data
        price_data = download_data(
            portfolio['selected'],
            portfolio['start_date'],
            portfolio['end_date']
        )
        
        if price_data.empty:
            st.error("No price data available for the selected portfolio.")
        else:
            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
            missing_selected = list(set(portfolio['selected']) - set(available_selected))
            if missing_selected:
                st.warning(f"Excluded tickers with no data: {', '.join(missing_selected)}")
            
            if not available_selected:
                st.error("No selected tickers have available data for the chosen date range.")
            else:
                returns = price_data[available_selected].pct_change().dropna()
                
                st.header("🚀 Portfolio Optimization Settings")
                
                # Constraints Section
                with st.expander("⚙️ Constraints", expanded=True):
                    constraints_col1, constraints_col2 = st.columns(2)
                    
                    with constraints_col1:
                        max_weight = st.slider(
                            "📈 Maximum Weight per Asset (%)",
                            min_value=0.0,
                            max_value=100.0,
                            value=100.0,
                            step=0.1,
                            help="Set the maximum allocation percentage for any single asset."
                        ) / 100  # Convert to decimal
                
                    with constraints_col2:
                        min_weight = st.slider(
                            "📉 Minimum Weight per Asset (%)",
                            min_value=0.0,
                            max_value=100.0,
                            value=0.0,
                            step=0.1,
                            help="Set the minimum allocation percentage for any single asset."
                        ) / 100  # Convert to decimal
                
                    # Validation: Ensure min_weight does not exceed max_weight
                    if min_weight > max_weight:
                        st.error("⚠️ **Minimum weight cannot exceed Maximum weight.** Please adjust your settings.")
                
                # Objectives Section
                with st.expander("🎯 Objectives", expanded=True):
                    objective_options = {
                        "Maximize Sharpe Ratio": "sharpe",
                        "Minimize Variance": "min_variance",
                        "Maximize Return": "max_return",
                        "Minimize Drawdown": "min_drawdown",
                        "Maximize Alpha": "maximize_alpha",
                        "Minimize Beta": "minimize_beta"
                    }
                    selected_objectives = st.multiselect(
                        "Select Optimization Objectives",
                        options=list(objective_options.keys()),
                        default=["Maximize Sharpe Ratio"],
                        help="Choose one or more objectives to optimize your portfolio."
                    )
                    
                    if not selected_objectives:
                        st.warning("⚠️ Please select at least one optimization objective.")
                    
                # Target Return Section
                with st.expander("🎯 Target Return", expanded=True):
                    target_return = st.number_input(
                        "📈 Target Annual Return (%)",
                        min_value=0.0,
                        max_value=100.0,
                        value=10.0,
                        step=0.1,
                        help="Set the target annual return for portfolio optimization."
                    ) / 100  # Convert to decimal
                
                # Optimization Trigger
                if st.button("✅ Run Optimization"):
                    if not selected_objectives:
                        st.error("❌ Please select at least one optimization objective.")
                    else:
                        # Map objectives to their internal codes
                        objective_map = {v: k for k, v in objective_options.items()}
                        objectives_selected = [objective_options[obj] for obj in selected_objectives]
                        
                        with st.spinner("🧮 Optimizing portfolio..."):
                            # Fetch benchmark returns
                            benchmark_symbol = portfolio['benchmark_symbol']
                            benchmark_data = download_data([benchmark_symbol], portfolio['start_date'], portfolio['end_date'])
                            
                            if benchmark_data.empty:
                                st.error(f"Benchmark symbol '{benchmark_symbol}' does not have available data for the chosen period.")
                            else:
                                benchmark_returns = benchmark_data[benchmark_symbol].pct_change().dropna()
                                benchmark_returns = benchmark_returns.reindex(returns.index, method='ffill').dropna()
    
                                # Run optimization
                                optimized_weights = optimize_portfolio(
                                    returns,
                                    benchmark_returns=benchmark_returns,
                                    objectives=objectives_selected,
                                    rf=portfolio['rf_rate'],
                                    max_weight=max_weight,
                                    min_weight=min_weight,
                                    target_return=target_return
                                )
    
                                if optimized_weights is not None:
                                    # Ensure allocations sum to 100%
                                    total_allocation = sum(optimized_weights)
                                    if not np.isclose(total_allocation, 1.0, atol=1e-4):
                                        st.warning(f"🔄 Allocations sum to {total_allocation*100:.2f}%. Adjusting proportionally.")
                                        optimized_weights = [w / total_allocation for w in optimized_weights]
                                    
                                    # Display Optimized Weights
                                    st.subheader("📊 Optimized Portfolio Allocation")
                                    weights_df = pd.DataFrame({
                                        'Ticker': available_selected,
                                        'Allocation (%)': [f"{w*100:.2f}" for w in optimized_weights]
                                    })
                                    st.table(weights_df)
    
                                    # Plot Allocation Pie Chart
                                    plot_allocation_pie(
                                        optimized_weights,
                                        available_selected,
                                        title="🥧 Optimized Allocation",
                                        hover_info="percent+name"
                                    )
    
                                    # Display Performance Metrics
                                    st.subheader("📈 Optimized Portfolio Performance Metrics")
                                    optimized_return = np.dot(optimized_weights, returns.mean()) * 252
                                    optimized_volatility = np.sqrt(np.dot(optimized_weights, np.dot(returns.cov() * 252, optimized_weights)))
                                    optimized_sharpe = (optimized_return - portfolio['rf_rate']) / optimized_volatility if optimized_volatility > 0 else np.nan
    
                                    optimized_metrics = {
                                        'Expected Annual Return (%)': f"{optimized_return * 100:.2f}%",
                                        'Annualized Volatility (%)': f"{optimized_volatility * 100:.2f}%",
                                        'Sharpe Ratio': f"{optimized_sharpe:.2f}"
                                    }
    
                                    optimized_metrics_df = pd.DataFrame(list(optimized_metrics.items()), columns=['Metric', 'Value'])
                                    st.table(optimized_metrics_df)
    
                                    # Store optimized weights and metrics in session state
                                    st.session_state.backtest_results.update({
                                        'optimized_weights': optimized_weights,
                                        'optimized_return': optimized_return,
                                        'optimized_volatility': optimized_volatility,
                                        'optimized_sharpe': optimized_sharpe
                                    })
    
                                    st.success("🎉 Portfolio optimization completed successfully!")
                                else:
                                    st.error("❌ Optimization did not return any weights.")
    else:
        st.warning("Please add at least one portfolio to optimize.")

File "/content/portfolio_optimizer.py", line 3369
      else:
      ^
SyntaxError: invalid syntax

fix it