🔍 Key Insights
📈 Returns: Tech-Heavy Portfolio shows stronger performance (2340.0% vs 2575.0%)

🛡️ Risk Profile: Retirement Portfolio demonstrates lower volatility (2442.0% vs 2894.0%)

⚖️ Sharpe Ratio: Retirement Portfolio has better risk-adjusted returns (0.90 vs 0.87)

📊 Diversification Analysis: Consider increasing diversification if one portfolio is heavily concentrated in few assets.

⚡ Recommended Actions
🔄 Consider rebalancing to optimize returns and manage risk.

📈 Review asset allocation to ensure diversification.

🛡️ Implement risk management strategies to mitigate potential losses.

🔍 Monitor benchmark performance to maintain alignment with investment goals.

📝 Summary and Recommendations
Retirement Portfolio Score

76.36/100
Tech-Heavy Portfolio Score

77.30/100
• 📈 Returns: Tech-Heavy Portfolio shows stronger performance (2340.00% vs 2575.00%)

• 🛡️ Risk Profile: Retirement Portfolio demonstrates lower volatility (2442.00% vs 2894.00%)

• ⚖️ Risk-Adjusted Returns: Retirement Portfolio has better Sharpe Ratio (0.90 vs 0.87)

• 🔻 Maximum Loss: Retirement Portfolio has smaller drawdown (-3621.00% vs -4338.00%)

• 📊 Asset Count: Retirement Portfolio (5 assets) vs Tech-Heavy Portfolio (5 assets)

fix the above bugs and redundant
Please review my code and identify specific sections that need editing. For each section, provide the updated code that I can directly use to replace the old version. I don't need to see the original code—only the revised part.
Additionally, include clear, beginner-friendly instructions for replacing each section. Focus on where to find the code in my project files, any specific lines or keywords to look for, and exactly how to insert the new code. Keep it concise. Thank you!

%%writefile portfolio_optimizer.py
import datetime
from datetime import datetime, timedelta
from functools import wraps
from typing import Optional, Union, List, Dict, Tuple

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import requests
import streamlit as st
import yfinance as yf
from joblib import Parallel, delayed
from pandas_datareader import data as pdr
from scipy.optimize import minimize
from scipy.stats import skew, kurtosis
from statsmodels.api import OLS
from urllib3.util.retry import Retry
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.exceptions import InsecureRequestWarning

import warnings

# Suppress specific warnings
warnings.simplefilter('ignore', InsecureRequestWarning)
requests.packages.urllib3.disable_warnings()  # Use cautiously

# ----------------------------
# Frequency Mapping
# ----------------------------
FREQUENCY_MAPPING = {
    "Daily": "D",
    "Weekly": "W",
    "Monthly": "M",
    "Quarterly": "Q",
    "Yearly": "Y"
}

# Inverse mapping to get frequency name from code
rebalance_freq_inverse_mapping = {v: k for k, v in FREQUENCY_MAPPING.items()}

# ----------------------------
# Global Theme Configuration
# ----------------------------
THEME = 'plotly_dark'  # Options: 'plotly_dark', 'plotly_white', 'seaborn', etc.

# ----------------------------
# Global Plot Layout Configuration
# ----------------------------
FIG_HEIGHT = 600
HOVERMODE = 'x unified'

# ----------------------------
# Example Portfolios Configuration
# ----------------------------

def create_example_portfolios() -> list:
    """
    Creates a list of predefined example portfolios without benchmark symbols.
    
    Returns:
        list: A list of dictionaries, each representing a portfolio.
    """
    today = datetime.today()
    today_pd = pd.to_datetime(today)
    example_definitions = [
        {
            'name': "Retirement Portfolio",
            'years': 10,
            'rebalance_freq': 'M',
            'selected': ['AAPL', 'MSFT', 'GOOGL', 'JPM', 'XOM'],
            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
        },
        {
            'name': "Tech-Heavy Portfolio",
            'years': 5,
            'rebalance_freq': 'M',
            'selected': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META'],
            'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]
        },
        {
            'name': "Balanced Risk-Return Portfolio",
            'years': 7,
            'rebalance_freq': 'M',
            'selected': ['AAPL', 'JNJ', 'V', 'PG', 'XOM'],
            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
        },
        {
            'name': "Income-Focused Portfolio",
            'years': 7,
            'rebalance_freq': 'Q',
            'selected': ['PG', 'KO', 'JNJ', 'T', 'PFE'],
            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
        },
        {
            'name': "Growth-Oriented Portfolio",
            'years': 3,
            'rebalance_freq': 'M',
            'selected': ['TSLA', 'NVDA', 'AMD', 'META', 'NFLX'],
            'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]
        },
        {
            'name': "Value Portfolio",
            'years': 10,
            'rebalance_freq': 'Q',
            'selected': ['KO', 'PFE', 'XOM', 'WMT', 'CVX'],
            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
        },
        {
            'name': "Dividend-Focused Portfolio",
            'years': 7,
            'rebalance_freq': 'Q',
            'selected': ['T', 'VZ', 'PFE', 'KO', 'IBM'],
            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
        },
        {
            'name': "Global Diversification Portfolio",
            'years': 5,
            'rebalance_freq': 'M',
            'selected': ['AAPL', 'TSM', 'BABA', 'SAP', 'UL'],
            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
        },
        {
            'name': "High-Risk Growth Portfolio",
            'years': 3,
            'rebalance_freq': 'M',
            'selected': ['TSLA', 'ARKK', 'NVDA', 'SHOP', 'CRWD'],
            'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]
        },
        {
            'name': "Emerging Markets Portfolio",
            'years': 5,
            'rebalance_freq': 'M',
            'selected': ['BABA', 'TSM', 'PDD', 'INFY', 'VALE'],
            'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]
        }
    ]
    
    portfolios = []
    for port_def in example_definitions:
        portfolio = {
            'name': port_def['name'],
            'start_date': today - timedelta(days=365 * port_def['years']),
            'end_date': today,
            'rf_rate': 0.02,
            'broker_fee': 0.0,
            'rebalance_freq': port_def['rebalance_freq'],
            'selected': port_def['selected'],
            'allocations': port_def['allocations']
        }
        portfolios.append(portfolio)
    
    return portfolios

# Initialize example portfolios if not already in session state
if 'portfolios' not in st.session_state or not st.session_state.portfolios:
    st.session_state.portfolios = create_example_portfolios()

# ----------------------------
# Session State Initialization
# ----------------------------

def initialize_session_state():
    """
    Initializes Streamlit session state with default values.
    """
    # Initialize example portfolios
    st.session_state.setdefault('portfolios', create_example_portfolios())
    
    # Other session state variables
    st.session_state.setdefault('backtest_results', {})
    st.session_state.setdefault('step', "Configure Portfolio")
    st.session_state.setdefault('edit_portfolio', None)
    st.session_state.setdefault('add_new_portfolio', False)
    st.session_state.setdefault('default_config', {
        'rf_rate': 0.02,
        'broker_fee': 0.0
    })
    st.session_state.setdefault('show_proceed', False)
    st.session_state.setdefault('selected_time_frames', ['Annually'])
    st.session_state.setdefault('selected_rolling_periods', [252])

# Initialize session state
initialize_session_state()
# ----------------------------
# Utility Decorators
# ----------------------------

def handle_exceptions(func):
    """
    Decorator to handle exceptions in Streamlit applications.
    Displays errors using Streamlit's error message system.
    """
    @wraps(func)
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            st.error(f"Error in {func.__name__}: {e}")
            return None
    return wrapper

# ----------------------------
# Helper Functions
# ----------------------------

@handle_exceptions
def get_company_name(ticker_df: pd.DataFrame, ticker: str) -> str:
    """
    Retrieve the company name for a given ticker.

    Parameters:
    - ticker_df (pd.DataFrame): DataFrame containing 'Ticker' and 'Company Name' columns.
    - ticker (str): Stock ticker symbol.

    Returns:
    - str: Company name or "Unknown" if not found.
    """
    match = ticker_df[ticker_df['Ticker'] == ticker]
    if not match.empty:
        return match.iloc[0]['Company Name']
    return "Unknown"

def format_asset_option(ticker: str, company_name: str) -> str:
    """
    Format the asset option display string.

    Parameters:
    - ticker (str): Stock ticker symbol.
    - company_name (str): Name of the company.

    Returns:
    - str: Formatted string combining ticker and company name.
    """
    return f"{ticker} - {company_name}"

def handle_portfolio_data(portfolio, date_range=None):
    """
    Helper function to download data and handle missing tickers.

    Parameters:
    - portfolio (dict): Portfolio details.
    - date_range (tuple, optional): (start_date, end_date)

    Returns:
    - available_selected (list): Tickers with available data.
    - missing_selected (set): Tickers missing data.
    - price_data (pd.DataFrame): Adjusted close prices.
    """
    start_date = date_range[0] if date_range else portfolio['start_date']
    end_date = date_range[1] if date_range else portfolio['end_date']
    price_data = download_data(portfolio['selected'], start_date, end_date)
    if price_data.empty:
        return [], set(portfolio['selected']), price_data
    available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
    missing_selected = set(portfolio['selected']) - set(available_selected)
    if missing_selected:
        st.warning(f"Excluded tickers with no data: {', '.join(missing_selected)}")
    return available_selected, missing_selected, price_data

def adjust_allocations(allocations: list) -> list:
    """
    Adjusts portfolio allocations to ensure they sum to 100%.
    
    Parameters:
    - allocations (list): List of allocation percentages
    
    Returns:
    - list: Adjusted allocation percentages that sum to 100%
    """
    if not allocations:
        return []
        
    total = sum(allocations)
    if total == 0:
        return [0] * len(allocations)
        
    # Normalize allocations to sum to 100%
    return [alloc / total * 100 for alloc in allocations]

@handle_exceptions
def run_portfolio_comparison(comparison_type, portfolio1, portfolio2=None):
    """
    Function to run portfolio comparisons (Portfolio vs Portfolio).
    
    Parameters:
        comparison_type (str): Should always be "vs_portfolio".
        portfolio1 (dict): First portfolio.
        portfolio2 (dict, optional): Second portfolio for comparison.
    
    Returns:
        dict: Backtest results or error message.
    """
    try:
        if comparison_type == "vs_portfolio":
            # Ensure both portfolios are provided
            if not portfolio1 or not portfolio2:
                return {"error": "Both portfolios must be provided for comparison."}
            
            # 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 between the selected portfolios."}
            
            # 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 or both portfolios have 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."}
            
            # 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'])
            
            # 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]
            
            # 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}"}

def create_new_portfolio():
    """Creates a new empty portfolio template with default values."""
    return {
        'name': "",
        'start_date': pd.to_datetime(datetime.today() - timedelta(days=365)),
        'end_date': pd.to_datetime(datetime.today()),
        'rf_rate': st.session_state.default_config['rf_rate'],
        'broker_fee': st.session_state.default_config['broker_fee'],
        'rebalance_freq': 'M',
        'selected': [],
        'allocations': []
    }

def update_session_state(updates: dict):
    """
    Helper function to update Streamlit session state.

    Parameters:
    - updates (dict): Dictionary of key-value pairs to update in session state.
    """
    for key, value in updates.items():
        st.session_state[key] = value

def highlight_better_performance(portfolio1, portfolio2):
    """
    Creates a styling function for comparing two portfolios.
    
    Args:
        portfolio1 (str): Name of the first portfolio
        portfolio2 (str): Name of the second portfolio
        
    Returns:
        function: A styling function that can be used with DataFrame.style.apply()
    """
    def _style_row(row):
        styles = [''] * len(row)
        
        # Check if both portfolios exist in the row
        if portfolio1 in row.index and portfolio2 in row.index:
            val1 = row[portfolio1]
            val2 = row[portfolio2]
            
            # Convert string values to float if possible
            try:
                if isinstance(val1, str):
                    val1 = float(str(val1).strip('%').strip('$').replace(',', ''))
                if isinstance(val2, str):
                    val2 = float(str(val2).strip('%').strip('$').replace(',', ''))
                
                # Only proceed if both values are numeric
                if isinstance(val1, (int, float)) and isinstance(val2, (int, float)):
                    metric_name = row.name
                    # Determine if higher or lower is better based on the metric
                    lower_is_better = any(keyword in metric_name for keyword in [
                        'Drawdown', 'Deviation', 'Tracking Error', 'Downside Capture Ratio', 'R2'
                    ])
                    if lower_is_better:
                        better = val1 < val2
                    else:
                        better = val1 > val2
                    
                    # Apply styling
                    if better:
                        styles[row.index.get_loc(portfolio1)] = 'background-color: #d4f7d4'  # Green
                        styles[row.index.get_loc(portfolio2)] = 'background-color: #f7d4d4'  # Red
                    else:
                        styles[row.index.get_loc(portfolio1)] = 'background-color: #f7d4d4'  # Red
                        styles[row.index.get_loc(portfolio2)] = 'background-color: #d4f7d4'  # Green
            except (ValueError, TypeError):
                pass  # Skip if conversion fails
        return styles
    return _style_row

def display_backtest_results(results, comparison_type, portfolio1_name, portfolio2_name=None):
    if comparison_type == "Portfolio vs Portfolio":
        display_portfolio_vs_portfolio(results, portfolio1_name, portfolio2_name)

def get_significant_events() -> dict[pd.Timestamp, str]:
    """
    Define significant market events/dates for marking on the growth comparison graph.
    Includes major market crashes, bubbles, policy changes, and significant corporate events.
    
    Returns:
        dict[pd.Timestamp, str]: Dictionary mapping dates to significant market events.
        Each key is a pandas Timestamp and value is the event description.
    """
    return {
        # 1990s Events
        pd.Timestamp('1995-08-09'): 'Netscape IPO - Start of Dot-com Boom',
        pd.Timestamp('1997-07-02'): 'Asian Financial Crisis Begins',
        pd.Timestamp('1998-08-17'): 'Russian Financial Crisis/LTCM Collapse',
        pd.Timestamp('1999-03-29'): 'Dow Jones Breaks 10,000',
        pd.Timestamp('1999-12-31'): 'Peak of Dot-com Bubble',
        
        # 2000s Events
        pd.Timestamp('2000-03-10'): 'Dot-com Bubble Peak (NASDAQ)',
        pd.Timestamp('2001-09-17'): 'Markets Reopen After 9/11',
        pd.Timestamp('2002-07-22'): 'WorldCom Bankruptcy',
        pd.Timestamp('2004-08-19'): 'Google IPO',
        pd.Timestamp('2006-05-10'): 'NYSE/Archipelago Merger',
        pd.Timestamp('2007-07-19'): 'Dow Jones Passes 14,000',
        pd.Timestamp('2008-09-15'): 'Lehman Brothers Bankruptcy',
        pd.Timestamp('2008-09-29'): 'Largest Single-Day Point Drop (777)',
        pd.Timestamp('2009-03-09'): 'Financial Crisis Market Bottom',
        
        # 2010s Events
        pd.Timestamp('2010-05-06'): 'Flash Crash',
        pd.Timestamp('2012-05-18'): 'Facebook IPO',
        pd.Timestamp('2013-05-07'): 'Dow Jones Breaks 15,000',
        pd.Timestamp('2015-08-24'): 'Black Monday China Crash',
        pd.Timestamp('2016-06-24'): 'Brexit Vote Market Reaction',
        pd.Timestamp('2017-01-25'): 'Dow Jones Breaks 20,000',
        pd.Timestamp('2018-02-05'): 'VIX Products Collapse',
        pd.Timestamp('2019-05-10'): 'Uber IPO',
        
        # 2020s Events
        pd.Timestamp('2020-03-16'): 'COVID-19 Market Crash',
        pd.Timestamp('2020-03-23'): 'COVID-19 Market Bottom',
        pd.Timestamp('2020-08-31'): 'Tesla/Apple Stock Splits',
        pd.Timestamp('2020-11-03'): 'US Election Day',
        pd.Timestamp('2021-01-27'): 'GameStop Short Squeeze Peak',
        pd.Timestamp('2021-04-14'): 'Coinbase Direct Listing',
        pd.Timestamp('2021-11-08'): 'Tesla Stock Split Announcement',
        pd.Timestamp('2022-01-03'): 'Fed Rate Hike Cycle Begins',
        pd.Timestamp('2022-02-24'): 'Russia-Ukraine War Begins',
        pd.Timestamp('2022-06-16'): 'Bitcoin Below $20k/Crypto Crash',
        pd.Timestamp('2022-11-11'): 'FTX Collapse',
        pd.Timestamp('2023-03-10'): 'Silicon Valley Bank Collapse',
        pd.Timestamp('2023-05-01'): 'First Republic Bank Failure',
        pd.Timestamp('2023-08-24'): 'NVIDIA $1T Milestone',
        pd.Timestamp('2023-11-09'): 'ARM IPO',
        pd.Timestamp('2024-01-11'): 'Bitcoin ETFs Approval',
        pd.Timestamp('2024-03-01'): 'Dow Jones Breaks 39,000'
    }

def create_portfolio_comparison_summary(portfolio1_name: str, portfolio2_name: str, metrics_a: dict, metrics_b: dict):
    """
    Creates a single, comprehensive summary for portfolio comparison with actionable insights.
    """
    st.markdown("---")  # Visual separator
    
    with st.expander("📋 Portfolio Comparison Insights", expanded=True):
        # Performance Comparison
        st.markdown("### 📊 Key Performance Comparison")
        
        # Create two columns for side-by-side comparison
        col1, col2 = st.columns(2)
        
        metrics_to_compare = {
            'Returns': 'Annualized Return (CAGR)',
            'Risk': 'Standard Deviation (Annualized)',
            'Risk-Adjusted': 'Sharpe Ratio',
            'Maximum Loss': 'Maximum Drawdown'
        }
        
        with col1:
            st.markdown(f"**{portfolio1_name}**")
            for label, metric in metrics_to_compare.items():
                value = metrics_a.get(metric, "N/A")
                st.metric(label, value)
                
        with col2:
            st.markdown(f"**{portfolio2_name}**")
            for label, metric in metrics_to_compare.items():
                value = metrics_b.get(metric, "N/A")
                st.metric(label, value)
        
        # Key Insights
        st.markdown("### 🔍 Key Insights")
        
        # Safely extract and compare metrics
        def safe_extract(metric_dict, metric_name):
            value = metric_dict.get(metric_name, 0)
            if isinstance(value, str):
                try:
                    return float(value.strip('%'))
                except AttributeError:
                    return float(value)
                except ValueError:
                    st.warning(f"⚠️ Unable to convert metric '{metric_name}' to float. Using 0.")
                    return 0.0
            elif isinstance(value, (float, np.float64, int)):
                return float(value)
            else:
                st.warning(f"⚠️ Unexpected type for metric '{metric_name}'. Using 0.")
                return 0.0
        returns_a = safe_extract(metrics_a, 'Annualized Return (CAGR)')
        returns_b = safe_extract(metrics_b, 'Annualized Return (CAGR)')
        better_returns = portfolio1_name if returns_a > returns_b else portfolio2_name
        
        vol_a = safe_extract(metrics_a, 'Standard Deviation (Annualized)')
        vol_b = safe_extract(metrics_b, 'Standard Deviation (Annualized)')
        lower_risk = portfolio1_name if vol_a < vol_b else portfolio2_name
        
        sharpe_a = safe_extract(metrics_a, 'Sharpe Ratio')
        sharpe_b = safe_extract(metrics_b, 'Sharpe Ratio')
        better_sharpe = portfolio1_name if sharpe_a > sharpe_b else portfolio2_name
        
        # Develop insights based on metric comparisons
        insights = [
            f"📈 **Returns:** {better_returns} shows stronger performance ({returns_a:.1%} vs {returns_b:.1%})",
            f"🛡️ **Risk Profile:** {lower_risk} demonstrates lower volatility ({vol_a:.1%} vs {vol_b:.1%})",
            f"⚖️ **Sharpe Ratio:** {better_sharpe} has better risk-adjusted returns ({sharpe_a:.2f} vs {sharpe_b:.2f})",
            "📊 **Diversification Analysis:** Consider increasing diversification if one portfolio is heavily concentrated in few assets."
        ]
        
        for insight in insights:
            st.markdown(insight)
        
        # Action Items
        st.markdown("### ⚡ Recommended Actions")
        actions = [
            "🔄 Consider rebalancing to optimize returns and manage risk.",
            "📈 Review asset allocation to ensure diversification.",
            "🛡️ Implement risk management strategies to mitigate potential losses.",
            "🔍 Monitor benchmark performance to maintain alignment with investment goals."
        ]
        
        for action in actions:
            st.markdown(action)

def calculate_win_loss_counts(cum_returns_a: pd.Series, cum_returns_b: pd.Series) -> dict:
    """
    Calculate the number of times each portfolio outperformed the other.
    
    Parameters:
        cum_returns_a (pd.Series): Cumulative returns of portfolio A.
        cum_returns_b (pd.Series): Cumulative returns of portfolio B.
    
    Returns:
        dict: Counts of wins for each portfolio and ties.
    """
    wins_a = (cum_returns_a > cum_returns_b).sum()
    wins_b = (cum_returns_b > cum_returns_a).sum()
    ties = (cum_returns_a == cum_returns_b).sum()
    return {'wins_a': wins_a, 'wins_b': wins_b, 'ties': ties}

def define_market_conditions() -> dict:
    """
    Define different market conditions based on return thresholds.
    
    Returns:
        dict: Mapping of market condition names to their defining periods.
    """
    return {
        "Bull Market": {"start": "2020-01-01", "end": "2020-12-31"},
        "Bear Market": {"start": "2020-03-01", "end": "2020-05-31"},
        "Sideways Market": {"start": "2021-01-01", "end": "2021-06-30"}
    }

def compare_performance_in_conditions(cum_returns_a: pd.Series, cum_returns_b: pd.Series, periods: dict) -> str:
    """
    Compare portfolio performances within specified market conditions.
    
    Parameters:
        cum_returns_a (pd.Series): Cumulative returns of portfolio A.
        cum_returns_b (pd.Series): Cumulative returns of portfolio B.
        periods (dict): Defines the start and end dates of the market condition.
    
    Returns:
        str: Name of the portfolio that performed better.
    """
    start = pd.to_datetime(periods['start'])
    end = pd.to_datetime(periods['end'])
    mask = (cum_returns_a.index >= start) & (cum_returns_a.index <= end)
    if not mask.any():
        return "No data available for this period."
    
    final_a = cum_returns_a[mask].iloc[-1]
    final_b = cum_returns_b[mask].iloc[-1]
    
    if final_a > final_b:
        return "Portfolio A"
    elif final_b > final_a:
        return "Portfolio B"
    else:
        return "Tie"

def calculate_diversification_score(allocations: list) -> int:
    """
    Calculate a diversification score (0-100) based on the allocation spread.
    A higher score indicates a more diversified portfolio.
    
    Parameters:
        allocations (list): Allocation percentages.
    
    Returns:
        int: Diversification score.
    """
    # Calculate the Herfindahl-Hirschman Index (HHI)
    hh_index = sum((alloc / 100) ** 2 for alloc in allocations)
    # Convert HHI to Diversification Score (inverse)
    diversification_score = int((1 - hh_index) * 100)
    return diversification_score

def determine_risk_level(metrics: dict) -> str:
    """
    Determine the risk level based on Annualized Volatility and Maximum Drawdown.
    
    Parameters:
        metrics (dict): Portfolio metrics.
    
    Returns:
        str: 'Low', 'Medium', or 'High'.
    """
    volatility = metrics.get('Standard Deviation (Annualized)', 0)
    drawdown = abs(metrics.get('Maximum Drawdown', 0))
    
    if volatility < 10 and drawdown < 15:
        return "Low 🔵"
    elif 10 <= volatility < 20 or 15 <= drawdown < 30:
        return "Medium 🟡"
    else:
        return "High 🔴"

def get_metric_tooltip(metric_name: str) -> str:
    """
    Return a tooltip explanation for each metric.
    
    Parameters:
        metric_name (str): The name of the metric.
    
    Returns:
        str: Tooltip text.
    """
    tooltips = {
        "📈 Total Return": "The overall return of the portfolio over the backtesting period.",
        "📉 Annualized Return": "The average yearly return of the portfolio.",
        "⚖️ Sharpe Ratio": "Measures risk-adjusted return. Higher is better.",
        "📉 Max Drawdown": "The largest peak-to-trough decline. Lower is better.",
        "🔍 Risk Level": "Overall risk assessment based on volatility and drawdown."
    }
    return tooltips.get(metric_name, "No description available.")

def generate_portfolio_comparison_recommendations(portfolio1_name: str, portfolio2_name: str, metrics_a: dict, metrics_b: dict) -> list:
    """
    Generate clear, non-redundant portfolio comparison recommendations.
    
    Parameters:
        portfolio1_name (str): Name of first portfolio
        portfolio2_name (str): Name of second portfolio 
        metrics_a (dict): Metrics for first portfolio
        metrics_b (dict): Metrics for second portfolio
        
    Returns:
        list: List of unique recommendations
    """
    recommendations = []
    
    def parse_percentage(value):
        if isinstance(value, str):
            try:
                return float(value.strip('%')) / 100
            except (ValueError, AttributeError):
                return 0.0
        return float(value) if isinstance(value, (float, int)) else 0.0
    
    try:
        # Compare returns
        returns_a = parse_percentage(metrics_a.get('Annualized Return (CAGR)', 0))
        returns_b = parse_percentage(metrics_b.get('Annualized Return (CAGR)', 0))
        better_returns = portfolio1_name if returns_a > returns_b else portfolio2_name
        recommendations.append(f"📈 Returns: {better_returns} shows stronger performance ({returns_a:.2%} vs {returns_b:.2%})")
        
        # Compare volatility
        vol_a = parse_percentage(metrics_a.get('Standard Deviation (Annualized)', 0))
        vol_b = parse_percentage(metrics_b.get('Standard Deviation (Annualized)', 0))
        lower_risk = portfolio1_name if vol_a < vol_b else portfolio2_name
        recommendations.append(f"🛡️ Risk Profile: {lower_risk} demonstrates lower volatility ({vol_a:.2%} vs {vol_b:.2%})")
        
        # Compare Sharpe ratios
        sharpe_a = float(metrics_a.get('Sharpe Ratio', 0))
        sharpe_b = float(metrics_b.get('Sharpe Ratio', 0))
        better_sharpe = portfolio1_name if sharpe_a > sharpe_b else portfolio2_name
        recommendations.append(f"⚖️ Risk-Adjusted Returns: {better_sharpe} has better Sharpe Ratio ({sharpe_a:.2f} vs {sharpe_b:.2f})")
        
        # Compare maximum drawdowns
        dd_a = parse_percentage(metrics_a.get('Maximum Drawdown', 0))
        dd_b = parse_percentage(metrics_b.get('Maximum Drawdown', 0))
        dd_a, dd_b = -abs(dd_a), -abs(dd_b)  # Ensure negative values
        lower_drawdown = portfolio1_name if abs(dd_a) < abs(dd_b) else portfolio2_name
        recommendations.append(f"🔻 Maximum Loss: {lower_drawdown} has smaller drawdown ({dd_a:.2%} vs {dd_b:.2%})")
        
        # Portfolio composition comparison
        portfolio1 = next(p for p in st.session_state.portfolios if p['name'] == portfolio1_name)
        portfolio2 = next(p for p in st.session_state.portfolios if p['name'] == portfolio2_name)
        recommendations.append(f"📊 Asset Count: {portfolio1_name} ({len(portfolio1['selected'])} assets) vs {portfolio2_name} ({len(portfolio2['selected'])} assets)")
        
        # Rebalancing frequency comparison
        freq_1 = rebalance_freq_inverse_mapping.get(portfolio1['rebalance_freq'], portfolio1['rebalance_freq'])
        freq_2 = rebalance_freq_inverse_mapping.get(portfolio2['rebalance_freq'], portfolio2['rebalance_freq'])
        if freq_1 != freq_2:
            recommendations.append(f"🔄 Rebalancing: {portfolio1_name} ({freq_1}) vs {portfolio2_name} ({freq_2})")
        
    except Exception as e:
        recommendations.append(f"⚠️ Error generating recommendations: {str(e)}")
    
    return recommendations

def display_portfolio_vs_portfolio(results, portfolio1_name, portfolio2_name):
    """Displays comparison between two portfolios with enhanced UI/UX."""
    # Extract data from results
    returns_a = results.get('returns_a', pd.Series(dtype=float))
    returns_b = results.get('returns_b', pd.Series(dtype=float))
    cum_returns_a = results.get('cum_returns_a', pd.Series(dtype=float))
    cum_returns_b = results.get('cum_returns_b', pd.Series(dtype=float))
    weights_a = results.get('weights_a', [])
    weights_b = results.get('weights_b', [])
    price_data_a = results.get('price_data_a', pd.DataFrame())
    price_data_b = results.get('price_data_b', pd.DataFrame())
    metrics_a = results.get('metrics_a', {})
    metrics_b = results.get('metrics_b', {})

    # Define available_a and available_b based on price data columns
    available_a = list(price_data_a.columns)
    available_b = list(price_data_b.columns)

    # Ensure that available_a and available_b are not empty
    if not available_a:
        st.error(f"No available data for Portfolio '{portfolio1}'.")
        return
    if not available_b:
        st.error(f"No available data for Portfolio '{portfolio2}'.")
        return

    # Define a consistent theme for all plots
    THEMES = {
        'plotly_dark': 'plotly_dark',
        'plotly_light': 'plotly_white'
    }
    selected_theme = THEMES.get('plotly_light')  # Change as needed

    # Create tabs for better organization
    overview_tab, comparison_tab, risk_tab, analysis_tab = st.tabs([
        "📊 Overview", "🔄 Comparison", "🎯 Risk Analysis", "🔍 Deep Analysis"
    ])

    with overview_tab:
        st.header("🔹 Portfolio Overview")
        
        # Calculate Diversification Score for both portfolios
        diversification_score_a = calculate_diversification_score(weights_a)
        diversification_score_b = calculate_diversification_score(weights_b)
        
        # Risk Level Determination
        risk_level = determine_risk_level(metrics_a)
        
        # Display Portfolio Composition with Diversification Score
        col1, col2 = st.columns([1, 1])
        
        with col1:
            st.subheader(f"{portfolio1} Composition")
            fig_comp_a = px.pie(
                values=weights_a,
                names=price_data_a.columns,
                title=f"{portfolio1} Allocation",
                template=selected_theme,
                hole=0.4,
                color_discrete_sequence=px.colors.sequential.Blues
            )
            fig_comp_a.update_traces(textinfo='percent+label')
            st.plotly_chart(fig_comp_a, use_container_width=True)
            
            # Display Diversification Score for Portfolio A
            diversification_score_a = calculate_diversification_score(weights_a)
            st.metric("📊 Diversification Score", f"{diversification_score_a}/100", 
                    help="Measures how diversified the portfolio is. Higher scores indicate better diversification.")
            # Display Key Metrics with Tooltips and Indicators
            st.subheader(f"{portfolio1} Key Metrics")
            metrics_display_a = {
                "📈 Total Return": (f"{(cum_returns_a.iloc[-1] - 1) * 100:.2f}%", "↑"),
                "📉 Annualized Return": (metrics_a.get('Annualized Return (CAGR)', "N/A"), "↑"),
                "⚖️ Sharpe Ratio": (metrics_a.get('Sharpe Ratio', "N/A"), "↑"),
                "📉 Max Drawdown": (metrics_a.get('Maximum Drawdown', "N/A"), "↓"),
                "🔍 Risk Level": (risk_level, "")
            }
            for metric, (value, indicator) in metrics_display_a.items():
                tooltip = get_metric_tooltip(metric)
                st.metric(label=metric, value=value, delta=indicator, delta_color="normal", help=tooltip)
        
        with col2:
            st.subheader(f"{portfolio2} Composition")
            fig_comp_b = px.pie(
                values=weights_b,
                names=price_data_b.columns,
                title=f"{portfolio2} Allocation",
                template=selected_theme,
                hole=0.4,
                color_discrete_sequence=px.colors.sequential.Oranges
            )
            fig_comp_b.update_traces(textinfo='percent+label')
            st.plotly_chart(fig_comp_b, use_container_width=True)
            
            # Display Diversification Score for Portfolio B
            diversification_score_b = calculate_diversification_score(weights_b)
            st.metric("📊 Diversification Score", f"{diversification_score_b}/100", 
                    help="Measures how diversified the portfolio is. Higher scores indicate better diversification.")
            st.subheader(f"{portfolio2} Key Metrics")
            metrics_display_b = {
                "📈 Total Return": (f"{(cum_returns_b.iloc[-1] - 1) * 100:.2f}%", "↑"),
                "📉 Annualized Return": (metrics_b.get('Annualized Return (CAGR)', "N/A"), "↑"),
                "⚖️ Sharpe Ratio": (metrics_b.get('Sharpe Ratio', "N/A"), "↑"),
                "📉 Max Drawdown": (metrics_b.get('Maximum Drawdown', "N/A"), "↓"),
                "🔍 Risk Level": (risk_level, "")
            }
            for metric, (value, indicator) in metrics_display_b.items():
                tooltip = get_metric_tooltip(metric)
                st.metric(label=metric, value=value, delta=indicator, delta_color="normal", help=tooltip)
        
        # Cumulative Returns Comparison with Indicators
        st.subheader("Cumulative Returns Comparison")
        fig_returns = go.Figure()
        fig_returns.add_trace(go.Scatter(
            x=cum_returns_a.index,
            y=cum_returns_a,
            name=portfolio1,
            line=dict(color='cyan', width=2),
            hovertemplate='%{y:.2f}'
        ))
        fig_returns.add_trace(go.Scatter(
            x=cum_returns_b.index,
            y=cum_returns_b,
            name=portfolio2,
            line=dict(color='orange', width=2, dash='dash'),
            hovertemplate='%{y:.2f}'
        ))
        
        # Add Moving Averages
        fig_returns.add_trace(go.Scatter(
            x=cum_returns_a.index,
            y=cum_returns_a.rolling(window=30).mean(),
            name=f"{portfolio1} 30-Day MA",
            line=dict(color='cyan', width=1, dash='dot'),
            hovertemplate='%{y:.2f}'
        ))
        fig_returns.add_trace(go.Scatter(
            x=cum_returns_b.index,
            y=cum_returns_b.rolling(window=30).mean(),
            name=f"{portfolio2} 30-Day MA",
            line=dict(color='orange', width=1, dash='dot'),
            hovertemplate='%{y:.2f}'
        ))
        
        # Add Relative Strength Indicator
        relative_strength = cum_returns_a / cum_returns_b
        fig_returns.add_trace(go.Scatter(
            x=relative_strength.index,
            y=relative_strength,
            name="Relative Strength",
            line=dict(color='green', width=2, dash='dashdot'),
            hovertemplate='%{y:.2f}'
        ))
        
        fig_returns.update_layout(
            template=selected_theme,
            hovermode='x unified',
            height=500,
            xaxis_title='Date',
            yaxis_title='Cumulative Returns',
            legend=dict(x=0.01, y=0.99)
        )
        st.plotly_chart(fig_returns, use_container_width=True)
        
        create_portfolio_comparison_summary(
            portfolio1_name=portfolio1_name,
            portfolio2_name=portfolio2_name,
            metrics_a=metrics_a,
            metrics_b=metrics_b
        )

    with comparison_tab:
        st.header("📊 Performance Metrics Comparison")
        
        # 1. Growth Comparison Graph with Enhanced Features
        st.subheader("📈 Growth Comparison")
        fig_growth_comp = go.Figure()
        fig_growth_comp.add_trace(go.Scatter(
            x=cum_returns_a.index,
            y=cum_returns_a,
            name=portfolio1_name,
            line=dict(color='cyan', width=2),
            hovertemplate='%{y:.2f}'
        ))
        fig_growth_comp.add_trace(go.Scatter(
            x=cum_returns_b.index,
            y=cum_returns_b,
            name=portfolio2_name,
            line=dict(color='orange', width=2, dash='dash'),
            hovertemplate='%{y:.2f}'
        ))
        
        # Add Moving Averages
        fig_growth_comp.add_trace(go.Scatter(
            x=cum_returns_a.index,
            y=cum_returns_a.rolling(window=30).mean(),
            name=f"{portfolio1_name} 30-Day MA",
            line=dict(color='cyan', width=1, dash='dot'),
            hovertemplate='%{y:.2f}'
        ))
        fig_growth_comp.add_trace(go.Scatter(
            x=cum_returns_b.index,
            y=cum_returns_b.rolling(window=30).mean(),
            name=f"{portfolio2_name} 30-Day MA",
            line=dict(color='orange', width=1, dash='dot'),
            hovertemplate='%{y:.2f}'
        ))
        
        # Add Significant Event Markers with datetime conversion
        significant_events = get_significant_events()
        for event_date, event_name in significant_events.items():
            # Convert Timestamp to Unix timestamp (milliseconds)
            x_value = event_date.timestamp() * 1000
            fig_growth_comp.add_vline(
                x=x_value,
                line_dash="dash",
                line_color="red",
                annotation_text=event_name,
                annotation_position="top left",
                annotation_font_size=10
            )
        
        # Add Relative Strength Indicator
        relative_strength = cum_returns_a / cum_returns_b
        fig_growth_comp.add_trace(go.Scatter(
            x=relative_strength.index,
            y=relative_strength,
            name="Relative Strength",
            line=dict(color='green', width=2, dash='dashdot'),
            hovertemplate='%{y:.2f}'
        ))
        
        # Update Layout with Indicators
        fig_growth_comp.update_layout(
            xaxis=dict(
            type='date',
            tickformat='%Y-%m-%d',
            range=[returns_a.index[0].timestamp() * 1000, returns_a.index[-1].timestamp() * 1000]),
            template=selected_theme,
            hovermode='x unified',
            height=500,
            xaxis_title='Date',
            yaxis_title='Cumulative Returns',
            legend=dict(x=0.01, y=0.99)
        )
        
        st.plotly_chart(fig_growth_comp, use_container_width=True)
        
        # 2. Detailed Performance Metrics with Plain English Explanations and Win/Loss Counts
        st.subheader("📊 Detailed Performance Metrics")
        
        # Organize metrics into categories for clarity
        return_metrics = [
            'Annualized Return (CAGR)',
            'Sharpe Ratio',
            'Sortino Ratio'
        ]
        
        risk_metrics = [
            'Maximum Drawdown',
            'Standard Deviation (Annualized)',
            'Beta',
            'Alpha (annualized)',
            'Information Ratio',
            'Modigliani–Modigliani Measure',
            'Tracking Error',
            'Calmar Ratio'
        ]
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown(f"### **{portfolio1_name}**")
            for metric in return_metrics:
                value = metrics_a.get(metric, "N/A")
                st.metric(label=metric, value=value, help=get_metric_tooltip(metric))
        
        with col2:
            st.markdown(f"### **{portfolio2_name}**")
            for metric in return_metrics:
                value = metrics_b.get(metric, "N/A")
                st.metric(label=metric, value=value, help=get_metric_tooltip(metric))
        
        st.markdown("### **Risk Metrics Comparison**")
        metrics_comparison = pd.DataFrame({
            'Metric': risk_metrics,
            portfolio1_name: [metrics_a.get(metric, "N/A") for metric in risk_metrics],
            portfolio2_name: [metrics_b.get(metric, "N/A") for metric in risk_metrics]
        })
        
        # Apply styling to highlight better performance
        styled_comparison = metrics_comparison.style.apply(
            highlight_better_performance(portfolio1_name, portfolio2_name),
            axis=1
        )
        
        st.dataframe(styled_comparison, height=400)
        
        # Plain English Explanation
        st.subheader("🔍 Plain English Explanation")
        with st.expander("📖 Learn More"):
            st.markdown("""
            - **Annualized Return (CAGR):** The geometric progression ratio that provides a constant rate of return over a specified period.
            - **Sharpe Ratio:** Indicates how much excess return you receive for the extra volatility endured for holding a riskier asset.
            - **Sortino Ratio:** A variation of the Sharpe ratio that differentiates harmful volatility from total overall volatility by using the asset's standard deviation of negative asset returns.
            - **Maximum Drawdown:** The maximum observed loss from a peak to a trough of a portfolio before a new peak is attained.
            - **Standard Deviation (Annualized):** Measures the amount of variation or dispersion of a set of values.
            - **Beta:** Measures the volatility or systemic risk of a portfolio in comparison to the market as a whole.
            - **Alpha (annualized):** The active return on an investment compared to a market index.
            - **Information Ratio:** Measures portfolio returns beyond the returns of a benchmark, usually an index, compared to the volatility of those returns.
            - **Modigliani–Modigliani Measure:** Adjusts the portfolio return to match the risk of the benchmark, allowing for comparison.
            - **Tracking Error:** The standard deviation of the difference between the portfolio returns and the benchmark returns.
            - **Calmar Ratio:** The ratio of the annualized return of an investment to its maximum drawdown.
            """)
        
        # 3. Win/Loss Count Between Portfolios
        st.subheader("🏆 Win/Loss Count Between Portfolios")
        win_loss_counts = calculate_win_loss_counts(cum_returns_a, cum_returns_b)
        st.write(f"**{portfolio1_name} Wins:** {win_loss_counts['wins_a']}")
        st.write(f"**{portfolio2_name} Wins:** {win_loss_counts['wins_b']}")
        st.write(f"**Ties:** {win_loss_counts['ties']}")
        
        # 4. Time-Based Performance Comparison
        st.subheader("⏰ Time-Based Performance Comparison")
        market_conditions = define_market_conditions()  # Define this function as needed
        for condition, periods in market_conditions.items():
            better_portfolio = compare_performance_in_conditions(cum_returns_a, cum_returns_b, periods)
            st.write(f"**{condition}:** {better_portfolio} performed better.")
        
        # Additional Plain English Insights
        st.subheader("💡 Insights")
        st.markdown("""
        - **Performance by Market Condition:** This section shows which portfolio performs better during different market scenarios such as bull, bear, or sideways markets.
        - **Win/Loss Count:** Indicates the number of periods each portfolio outperformed the other.
        - **Overall Comparison:** Provides a holistic view of both portfolios' performances across various metrics and conditions.
        """)

    with risk_tab:
        st.header("🎯 Risk Analysis")
        
        # 1. Rolling Volatility Comparison
        st.subheader("📉 Rolling Volatility Comparison (30-Day)")
        col1, col2 = st.columns([1,1])
        
        with col1:
            rolling_vol_a = returns_a.rolling(window=30).std() * np.sqrt(252)
            fig_vol_a = go.Figure()
            fig_vol_a.add_trace(go.Scatter(
                x=rolling_vol_a.index,
                y=rolling_vol_a,
                name=f"{portfolio1_name} Volatility",
                line=dict(color='cyan', width=2)
            ))
            fig_vol_a.update_layout(
                template=selected_theme,
                hovermode='x unified',
                height=500,
                xaxis_title='Date',
                yaxis_title='Annualized Volatility (%)',
                legend=dict(x=0.01, y=0.99)
            )
            st.plotly_chart(fig_vol_a, use_container_width=True)
        
        with col2:
            rolling_vol_b = returns_b.rolling(window=30).std() * np.sqrt(252)
            fig_vol_b = go.Figure()
            fig_vol_b.add_trace(go.Scatter(
                x=rolling_vol_b.index,
                y=rolling_vol_b,
                name=f"{portfolio2_name} Volatility",
                line=dict(color='orange', width=2, dash='dash')
            ))
            fig_vol_b.update_layout(
                template=selected_theme,
                hovermode='x unified',
                height=500,
                xaxis_title='Date',
                yaxis_title='Annualized Volatility (%)',
                legend=dict(x=0.01, y=0.99)
            )
            st.plotly_chart(fig_vol_b, use_container_width=True)
        
        # 2. Rolling Correlation Comparison
        st.subheader("🔄 Rolling Correlation (30-Day) Between Portfolios")
        rolling_corr = returns_a.rolling(window=30).corr(returns_b)
        
        fig_corr = px.line(
            rolling_corr,
            title="📈 30-Day Rolling Correlation",
            labels={"index": "Date", "value": "Correlation"},
            template=selected_theme
        )
        fig_corr.update_traces(line=dict(color='purple'))
        fig_corr.update_layout(
            hovermode='x unified',
            height=500,
            xaxis_title='Date',
            yaxis_title='Correlation'
        )
        st.plotly_chart(fig_corr, use_container_width=True)
        
        # 3. Top 10 Drawdowns Comparison with Improved Visualization
        st.subheader("📉 Top 10 Drawdowns Comparison")
        col3, col4 = st.columns([1,1])
        
        with col3:
            drawdowns_a = get_drawdown_details(cum_returns_a)
            if drawdowns_a:
                drawdowns_a_df = pd.DataFrame(drawdowns_a).sort_values(by='Drawdown', ascending=True).head(10)
                st.markdown(f"### **{portfolio1_name} Top 10 Drawdowns**")
                st.table(drawdowns_a_df)
            else:
                st.write("⚠️ Insufficient drawdown data for Portfolio 1.")
        
        with col4:
            drawdowns_b = get_drawdown_details(cum_returns_b)
            if drawdowns_b:
                drawdowns_b_df = pd.DataFrame(drawdowns_b).sort_values(by='Drawdown', ascending=True).head(10)
                st.markdown(f"### **{portfolio2_name} Top 10 Drawdowns**")
                st.table(drawdowns_b_df)
            else:
                st.write("⚠️ Insufficient drawdown data for Portfolio 2.")
        
        # Enhanced Drawdown Distribution Comparison
        st.subheader("🔍 Drawdown Distribution Analysis")
        if drawdowns_a and drawdowns_b:
            # Convert drawdowns to numeric values for analysis
            drawdowns_a_values = pd.to_numeric([float(d['Drawdown'].strip('%')) for d in drawdowns_a])
            drawdowns_b_values = pd.to_numeric([float(d['Drawdown'].strip('%')) for d in drawdowns_b])
            
            # Create DataFrame for visualization
            drawdown_data = pd.DataFrame({
                portfolio1_name: drawdowns_a_values,
                portfolio2_name: drawdowns_b_values
            })
            
            # Statistical comparison
            stats_col1, stats_col2 = st.columns(2)
            
            with stats_col1:
                st.markdown(f"**{portfolio1_name} Statistics**")
                st.write(f"Mean Drawdown: {drawdowns_a_values.mean():.2f}%")
                st.write(f"Max Drawdown: {drawdowns_a_values.min():.2f}%")
                st.write(f"Median Drawdown: {np.median(drawdowns_a_values):.2f}%")
                st.write(f"Drawdown Volatility: {drawdowns_a_values.std():.2f}%")
            
            with stats_col2:
                st.markdown(f"**{portfolio2_name} Statistics**")
                st.write(f"Mean Drawdown: {drawdowns_b_values.mean():.2f}%")
                st.write(f"Max Drawdown: {drawdowns_b_values.min():.2f}%")
                st.write(f"Median Drawdown: {np.median(drawdowns_b_values):.2f}%")
                st.write(f"Drawdown Volatility: {drawdowns_b_values.std():.2f}%")
            
            # Enhanced visualization options
            viz_type = st.radio(
                "Select Visualization Type",
                ["Box Plot", "Violin Plot", "Combined View"],
                horizontal=True
            )
            
            if viz_type == "Box Plot":
                fig = px.box(
                    drawdown_data,
                    title="📉 Drawdown Distribution Comparison",
                    labels={"value": "Drawdown (%)", "variable": "Portfolio"},
                    template=selected_theme,
                    points="all"  # Show all points
                )
            elif viz_type == "Violin Plot":
                fig = px.violin(
                    drawdown_data,
                    title="📉 Drawdown Distribution Comparison",
                    labels={"value": "Drawdown (%)", "variable": "Portfolio"},
                    template=selected_theme,
                    box=True,  # Show box plot inside violin
                    points="all"  # Show all points
                )
            else:  # Combined View
                fig = go.Figure()
                # Add violin plots
                for col in drawdown_data.columns:
                    fig.add_trace(go.Violin(
                        x=[col] * len(drawdown_data[col]),
                        y=drawdown_data[col],
                        name=col,
                        box_visible=True,
                        meanline_visible=True
                    ))
                # Add individual points
                for col in drawdown_data.columns:
                    fig.add_trace(go.Box(
                        x=[col] * len(drawdown_data[col]),
                        y=drawdown_data[col],
                        name=col,
                        boxpoints='all',
                        jitter=0.3,
                        pointpos=-1.8,
                        marker_color='rgba(0,0,0,0.3)',
                        showlegend=False
                    ))
                fig.update_layout(
                    title="📉 Drawdown Distribution Comparison",
                    yaxis_title="Drawdown (%)",
                    template=selected_theme
                )

            # Add mean lines and annotations
            for col in drawdown_data.columns:
                mean_val = drawdown_data[col].mean()
                fig.add_hline(
                    y=mean_val,
                    line_dash="dash",
                    line_color="red",
                    annotation_text=f"Mean ({col}): {mean_val:.2f}%",
                    annotation_position="right"
                )

            fig.update_layout(
                height=600,
                showlegend=True,
                yaxis_title="Drawdown (%)"
            )
            st.plotly_chart(fig, use_container_width=True)
            
            # Add interpretation
            st.markdown("### 📊 Distribution Analysis")
            mean_diff = abs(drawdowns_a_values.mean() - drawdowns_b_values.mean())
            vol_diff = abs(drawdowns_a_values.std() - drawdowns_b_values.std())
            
            interpretation = [
                f"• The average drawdown difference between portfolios is **{mean_diff:.2f}%**",
                f"• Drawdown volatility difference is **{vol_diff:.2f}%**",
                f"• {portfolio1_name if drawdowns_a_values.mean() > drawdowns_b_values.mean() else portfolio2_name} shows larger average drawdowns",
                f"• {portfolio1_name if drawdowns_a_values.std() > drawdowns_b_values.std() else portfolio2_name} shows more volatile drawdowns"
            ]
            
            for insight in interpretation:
                st.markdown(insight)
        else:
            st.warning("⚠️ Insufficient drawdown data for comparison analysis.")

    with analysis_tab:
        st.header("🔍 Deep Analysis")
        col1, col2 = st.columns([1,1])
        
        with col1:
            # Monthly Returns Heatmap for Portfolio1
            st.subheader(f"{portfolio1} Monthly Returns Heatmap")
            monthly_returns_a = returns_a.resample('M').agg(lambda x: (1 + x).prod() - 1)
            monthly_matrix_a = monthly_returns_a.groupby([monthly_returns_a.index.year, monthly_returns_a.index.month]).first().unstack()
            
            fig_heat_a = px.imshow(
                monthly_matrix_a,
                labels=dict(x="Month", y="Year", color="Returns"),
                color_continuous_scale="RdYlGn",
                title=f"{portfolio1} Monthly Returns Heatmap",
                template=selected_theme
            )
            fig_heat_a.update_layout(
                xaxis_title='Month',
                yaxis_title='Year',
                height=500
            )
            st.plotly_chart(fig_heat_a, use_container_width=True)

        with col2:
            # Monthly Returns Heatmap for Portfolio2
            st.subheader(f"{portfolio2} Monthly Returns Heatmap")
            monthly_returns_b = returns_b.resample('M').agg(lambda x: (1 + x).prod() - 1)
            monthly_matrix_b = monthly_returns_b.groupby([monthly_returns_b.index.year, monthly_returns_b.index.month]).first().unstack()
            
            fig_heat_b = px.imshow(
                monthly_matrix_b,
                labels=dict(x="Month", y="Year", color="Returns"),
                color_continuous_scale="RdYlGn",
                title=f"{portfolio2} Monthly Returns Heatmap",
                template=selected_theme
            )
            fig_heat_b.update_layout(
                xaxis_title='Month',
                yaxis_title='Year',
                height=500
            )
            st.plotly_chart(fig_heat_b, use_container_width=True)

        # Risk-Return Scatter Plot with Interactive Annotations
        st.subheader("🔥 Risk vs Return Scatter Plot")
        fig_risk_return = go.Figure()
        fig_risk_return.add_trace(go.Scatter(
            x=[returns_a.std() * np.sqrt(252)],
            y=[returns_a.mean() * 252],
            mode='markers+text',
            marker=dict(
                size=20,
                color='cyan',
                line=dict(width=2, color='DarkSlateGrey')
            ),
            text=[portfolio1],
            textposition="top center",
            name=portfolio1
        ))
        fig_risk_return.add_trace(go.Scatter(
            x=[returns_b.std() * np.sqrt(252)],
            y=[returns_b.mean() * 252],
            mode='markers+text',
            marker=dict(
                size=20,
                color='orange',
                line=dict(width=2, color='DarkSlateGrey')
            ),
            text=[portfolio2],
            textposition="top center",
            name=portfolio2
        ))
        fig_risk_return.update_layout(
            title='🔥 Risk vs Return Scatter Plot',
            xaxis_title='Annualized Volatility (Std Dev)',
            yaxis_title='Annualized Return',
            template=selected_theme,
            xaxis=dict(range=[0, max(returns_a.std(), returns_b.std()) * np.sqrt(252) * 1.2]),
            yaxis=dict(range=[0, max(returns_a.mean(), returns_b.mean()) * 252 * 1.2]),
            height=600
        )
        st.plotly_chart(fig_risk_return, use_container_width=True)

    # Final Summary with Interactive Components
    st.subheader("📝 Summary and Recommendations")
    
    # Calculate overall scores
    score_a = calculate_final_score(metrics_a, metrics_b)
    score_b = calculate_final_score(metrics_b, metrics_a)
    
    col7, col8 = st.columns([1,1])
    with col7:
        st.metric(f"{portfolio1} Score", f"{score_a:.2f}/100", help="Based on performance and risk metrics.")
    with col8:
        st.metric(f"{portfolio2} Score", f"{score_b:.2f}/100", help="Based on performance and risk metrics.")
    
    # Generate and display recommendations
    recommendations = generate_portfolio_comparison_recommendations(
        portfolio1_name=portfolio1,
        portfolio2_name=portfolio2,
        metrics_a=metrics_a,
        metrics_b=metrics_b
    )
    for rec in recommendations:
        st.markdown(f"• {rec}")

    # Update session state
    update_session_state({
        'comparison_results': results,
        'show_proceed': True
    })
    st.success("Portfolio comparison analysis completed! 🎉")

def plot_risk_return_attribution(returns, weights):
    """Plots the risk-return attribution analysis."""
    fig_rra = px.scatter(
        x=returns.mean() * 252,
        y=returns.std() * np.sqrt(252),
        size=weights*100,
        color=returns.mean() / returns.std(),
        hover_name=returns.index,
        title='Risk-Return Attribution',
        labels={'x': 'Annualized Return', 'y': 'Annualized Risk (Std Dev)'},
        template='plotly_dark',
        size_max=60
    )
    st.plotly_chart(fig_rra, use_container_width=True)

def plot_rolling_metrics(returns, windows):
    """Plots rolling metrics based on selected window periods."""
    for window in windows:
        rolling_mean = returns.rolling(window=window).mean()
        rolling_std = returns.rolling(window=window).std()
        fig = px.line(title=f'Rolling {window}-Day Mean and Std Dev')
        fig.add_scatter(x=rolling_mean.index, y=rolling_mean, mode='lines', name='Rolling Mean')
        fig.add_scatter(x=rolling_std.index, y=rolling_std, mode='lines', name='Rolling Std Dev')
        st.plotly_chart(fig, use_container_width=True)

@handle_exceptions
def calculate_final_score(primary_metrics: dict, comparison_metrics: dict) -> float:
    metrics_to_compare = [
        'Annualized Return (CAGR)',
        'Sharpe Ratio',
        'Sortino Ratio',
        'Treynor Ratio',
        'Calmar Ratio',
        'Alpha (annualized)',
        'Information Ratio',
        'Modigliani–Modigliani Measure',
        'Upside Capture Ratio',
        'Gain/Loss Ratio'
    ]
    metric_weights = {
        'Annualized Return (CAGR)': 15,
        'Sharpe Ratio': 15,
        'Sortino Ratio': 10,
        'Treynor Ratio': 10,
        'Calmar Ratio': 10,
        'Alpha (annualized)': 10,
        'Information Ratio': 10,
        'Modigliani–Modigliani Measure': 10,
        'Upside Capture Ratio': 5,
        'Gain/Loss Ratio': 5
    }

    score = 0
    total = 0

    for metric in metrics_to_compare:
        primary = primary_metrics.get(metric, "N/A")
        comparison = comparison_metrics.get(metric, "N/A")
        
        # Handle numpy types and convert to native Python types
        if isinstance(primary, (np.float64, np.float32)):
            primary = float(primary)
        if isinstance(comparison, (np.float64, np.float32)):
            comparison = float(comparison)
            
        if primary != "N/A" and comparison != "N/A":
            try:
                # Convert string values to float if necessary
                primary_val = float(str(primary).strip('%')) if isinstance(primary, str) else primary
                comparison_val = float(str(comparison).strip('%')) if isinstance(comparison, str) else comparison
                
                weight = metric_weights.get(metric, 1)
                
                if abs(comparison_val) < 1e-10:  # Better way to check for near-zero values
                    st.warning(f"Comparison metric '{metric}' is too close to zero. Skipping this metric.")
                    continue
                    
                if primary_val > comparison_val:
                    score += weight * (primary_val / comparison_val)
                else:
                    score += weight * (primary_val / comparison_val) * 0.5  # Partial credit
                    
                total += weight
            except (ValueError, TypeError) as e:
                st.warning(f"Error processing metric '{metric}': {str(e)}")
                continue
                
    return (score / total) * 100 if total > 0 else 0.0

@handle_exceptions
def backtest(weights: list, prices: pd.DataFrame, rebalance_freq: str = 'M', broker_fee: float = 0.0, debug: bool = False) -> tuple:
    """
    Backtest the portfolio based on weights and price data.

    Parameters:
    - weights (list): Allocation weights for each asset.
    - prices (pd.DataFrame): Adjusted closing prices of assets.
    - rebalance_freq (str): Rebalancing frequency (e.g., 'D', 'W', 'M').
    - broker_fee (float): Broker fee as a percentage.
    - debug (bool): If True, prints debug information.

    Returns:
    - tuple: (portfolio_returns: pd.Series, cum_returns: pd.Series)
    """
    try:
        # Calculate returns and drop any NaN values
        returns = prices.pct_change().dropna()
        if returns.empty:
            st.error("Returns data is empty after calculating percentage changes. Please ensure your price data is accurate and spans multiple periods.")
            return pd.Series(dtype=float), pd.Series(dtype=float)

        # Ensure weights are a NumPy array and normalized
        weights = np.array(weights)
        if not np.isclose(weights.sum(), 1.0):
            weights = weights / weights.sum()

        # Calculate portfolio returns
        portfolio_returns = returns.dot(weights)
        if not isinstance(portfolio_returns, pd.Series):
            portfolio_returns = portfolio_returns.squeeze()

        # Identify rebalancing dates
        if rebalance_freq.upper() == 'D':
            rebalance_dates = returns.index
        else:
            rebalance_dates = returns.resample(rebalance_freq.upper()).last().dropna().index

        # Debug: Show rebalancing dates
        if debug:
            st.write(f"Rebalancing Dates: {rebalance_dates.tolist()}")

        # Ensure rebalance_dates are in the portfolio_returns index
        valid_rebalance_dates = rebalance_dates.intersection(portfolio_returns.index)
        if debug:
            st.write(f"Valid Rebalancing Dates: {valid_rebalance_dates.tolist()}")

        # Apply broker fees on rebalancing dates
        if not valid_rebalance_dates.empty:
            portfolio_returns.loc[valid_rebalance_dates] -= broker_fee / 100  # Convert to decimal
        else:
            st.warning("⚠️ No valid rebalancing dates found within the returns data.")

        # Calculate cumulative returns
        cum_returns = (1 + portfolio_returns).cumprod()
        if cum_returns.empty:
            st.error("Cumulative returns are empty. Check the data and allocations.")
            return pd.Series(dtype=float), pd.Series(dtype=float)

        # Debug: Show cumulative returns
        if debug:
            st.write("Cumulative Returns:")
            st.write(cum_returns)

        return portfolio_returns, cum_returns
    except Exception as e:
        st.error(f"Error during backtesting: {e}")
        return pd.Series(dtype=float), pd.Series(dtype=float)

@handle_exceptions
def calculate_sharpe_ratio(returns: pd.Series, rf: float = 0.02) -> float:
    """
    Calculate the Sharpe Ratio for a given set of returns.

    Parameters:
    - returns (pd.Series): Daily returns of the portfolio.
    - rf (float): Risk-free rate (default is 2%).

    Returns:
    - float: Sharpe Ratio.
    """
    excess_return = returns.mean() * 252 - rf
    std_dev = returns.std() * np.sqrt(252)
    return excess_return / std_dev if std_dev != 0 else np.nan

@handle_exceptions
def calculate_sortino_ratio(returns: pd.Series, rf: float = 0.02) -> float:
    """
    Calculate the Sortino Ratio for a given set of returns.

    Parameters:
    - returns (pd.Series): Daily returns of the portfolio.
    - rf (float): Risk-free rate (default is 2%).

    Returns:
    - float: Sortino Ratio.
    """
    excess_return = returns.mean() * 252 - rf
    downside_std = returns[returns < 0].std() * np.sqrt(252)
    return excess_return / downside_std if downside_std != 0 else np.nan

def calculate_drawdown(cum_returns: pd.Series) -> float:
    """
    Calculate the maximum drawdown of the portfolio.
    
    Parameters:
    - cum_returns (pd.Series): Cumulative returns of the portfolio.
    
    Returns:
    - float: Maximum Drawdown.
    """
    peak = cum_returns.expanding(min_periods=1).max()
    drawdown = (cum_returns - peak) / peak
    max_drawdown = drawdown.min()
    return max_drawdown if not np.isnan(max_drawdown) else np.nan

@handle_exceptions
def calculate_cagr(cum_returns: pd.Series, rf: float = 0.02) -> float:
    """
    Calculate the Compound Annual Growth Rate (CAGR).
    
    Parameters:
    - cum_returns (pd.Series): Cumulative returns of the portfolio.
    - rf (float): Risk-free rate (default is 2%).
    
    Returns:
    - float: CAGR as a decimal.
    """
    if cum_returns.empty:
        return np.nan
    initial_date = cum_returns.index[0]
    final_date = cum_returns.index[-1]
    years = (final_date - initial_date).days / 365.25
    if years <= 0:
        return np.nan
    ending_value = cum_returns.iloc[-1]
    initial_value = cum_returns.iloc[0]
    cagr = (ending_value / initial_value) ** (1 / years) - 1
    return cagr

@handle_exceptions
def calculate_calmar_ratio(returns: pd.Series, cum_returns: pd.Series, rf: float = 0.02) -> float:
    """
    Calculate the Calmar Ratio of the portfolio.
    
    Parameters:
    - returns (pd.Series): Daily returns of the portfolio.
    - cum_returns (pd.Series): Cumulative returns of the portfolio.
    - rf (float): Risk-free rate (default is 2%).
    
    Returns:
    - float: Calmar Ratio.
    """
    cagr = calculate_cagr(cum_returns, rf)
    max_dd = calculate_drawdown(cum_returns)
    return cagr / abs(max_dd) if max_dd != 0 else np.nan

@handle_exceptions
def calculate_beta(returns: pd.Series, benchmark_returns: pd.Series) -> float:
    """
    Calculate the Beta of the portfolio relative to the benchmark.

    Parameters:
    - returns (pd.Series): Daily returns of the portfolio.
    - benchmark_returns (pd.Series): Daily returns of the benchmark.

    Returns:
    - float: Beta value.
    """
    if returns.empty or benchmark_returns.empty:
        return np.nan
    covariance = np.cov(returns, benchmark_returns)
    cov = covariance[0, 1]
    var_bench = covariance[1, 1]
    return cov / var_bench if var_bench != 0 else np.nan

@handle_exceptions
def get_drawdown_details(cum_returns: pd.Series) -> list:
    """
    Generate detailed drawdown information.

    Parameters:
    - cum_returns (pd.Series): Cumulative returns of the portfolio.

    Returns:
    - list: List of dictionaries containing drawdown details.
    """
    drawdowns = []
    if cum_returns.empty:
        return drawdowns

    peak = cum_returns.iloc[0]
    peak_date = cum_returns.index[0]
    trough = cum_returns.iloc[0]
    trough_date = cum_returns.index[0]

    for date, value in cum_returns.items():
        if value > peak:
            if trough < peak:
                drawdowns.append({
                    'Start': peak_date.strftime('%Y-%m-%d'),
                    'End': trough_date.strftime('%Y-%m-%d'),
                    'Drawdown': f"{((trough / peak) - 1) * 100:.2f}%",
                    'Date': trough_date  # Adding 'Date' as the end date
                })
            peak = value
            peak_date = date
            trough = value
            trough_date = date
        elif value < trough:
            trough = value
            trough_date = date

    # Final drawdown
    if trough < peak:
        drawdowns.append({
            'Start': peak_date.strftime('%Y-%m-%d'),
            'End': trough_date.strftime('%Y-%m-%d'),
            'Drawdown': f"{((trough / peak) - 1) * 100:.2f}%",
            'Date': trough_date
        })

    # Sort drawdowns by severity (most negative first)
    drawdowns_sorted = sorted(drawdowns, key=lambda x: float(x['Drawdown'].strip('%')), reverse=False)
    return drawdowns_sorted[:10]  # Return top 10 most severe drawdowns

def calculate_var(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:
    return np.percentile(portfolio_returns, (1 - confidence_level) * 100)

def calculate_cvar(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:
    var = calculate_var(portfolio_returns, confidence_level)
    return portfolio_returns[portfolio_returns <= var].mean()

@handle_exceptions
def optimize_portfolio(
    returns: pd.DataFrame,
    benchmark_returns: pd.Series = None,
    objectives: list = ['sharpe'],
    rf: float = 0.02,
    max_weight: float = 1.0,
    min_weight: float = 0.0,
    target_return: float = None
) -> np.ndarray:
    """
    Optimize a portfolio based on specified objectives.

    Parameters:
    - returns (pd.DataFrame): Historical returns of assets.
    - benchmark_returns (pd.Series, optional): Returns of the benchmark index.
    - objectives (list): Objectives to optimize.
        Options include 'sharpe', 'min_variance', 'max_return', 'min_drawdown', 'maximize_alpha', 'minimize_beta'.
    - rf (float): Risk-free rate for alpha calculation.
    - max_weight (float): Maximum weight per asset.
    - min_weight (float): Minimum weight per asset.
    - target_return (float, optional): Target return for the portfolio.

    Returns:
    - np.ndarray: Optimized asset weights or None if optimization fails.
    """
    from scipy.optimize import minimize

    def calculate_beta(portfolio_returns: pd.Series, benchmark_returns: pd.Series) -> float:
        covariance = np.cov(portfolio_returns, benchmark_returns)
        variance = covariance[1, 1]
        return covariance[0, 1] / variance if variance != 0 else 0

    # Define individual objective functions
    def sharpe_ratio(weights: np.ndarray) -> float:
        portfolio_return = np.dot(returns.mean(), weights) * 252
        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
        return -portfolio_return / portfolio_volatility if portfolio_volatility > 1e-6 else 0  # Negative for maximization

    def min_variance(weights: np.ndarray) -> float:
        return np.dot(weights.T, np.dot(returns.cov() * 252, weights))

    def max_return(weights: np.ndarray) -> float:
        return -np.dot(returns.mean(), weights) * 252  # Negative for maximization

    def min_drawdown(weights: np.ndarray) -> float:
        portfolio_returns = returns.dot(weights)
        cvar = calculate_cvar(portfolio_returns, 0.95)
        return cvar  # Minimizing CVaR as a proxy for drawdown

    # Initialize list of objective functions
    objective_functions = []

    # Map objectives to functions
    for obj in objectives:
        if obj == 'sharpe':
            objective_functions.append(sharpe_ratio)
        elif obj == 'min_variance':
            objective_functions.append(min_variance)
        elif obj == 'max_return':
            objective_functions.append(max_return)
        elif obj == 'min_drawdown':
            objective_functions.append(min_drawdown)
        elif obj == 'maximize_alpha':
            if benchmark_returns is None:
                st.error("benchmark_returns must be provided for 'maximize_alpha' objective.")
                return None

            benchmark_return = benchmark_returns.mean() * 252  # Assuming daily returns annualized

            def maximize_alpha(weights: np.ndarray) -> float:
                portfolio_return = np.dot(returns.mean(), weights) * 252
                portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
                portfolio_returns = returns.dot(weights)
                beta = calculate_beta(portfolio_returns, benchmark_returns)
                alpha = portfolio_return - (rf + beta * (benchmark_return - rf))
                return -alpha  # Negative for maximization

            objective_functions.append(maximize_alpha)
        elif obj == 'minimize_beta':
            if benchmark_returns is None:
                st.error("benchmark_returns must be provided for 'minimize_beta' objective.")
                return None

            def minimize_beta(weights: np.ndarray) -> float:
                portfolio_returns = returns.dot(weights)
                beta = calculate_beta(portfolio_returns, benchmark_returns)
                return beta

            objective_functions.append(minimize_beta)
        else:
            st.error(f"Invalid optimization objective: {obj}")
            return None

    # Composite objective function: weighted sum of individual objectives
    def composite_objective(weights: np.ndarray) -> float:
        return sum(fn(weights) for fn in objective_functions)

    # Define constraints
    constraints = [
        {'type': 'eq', 'fun': lambda x: np.sum(x) - 1},
    ]

    if target_return is not None:
        def target_return_constraint(x: np.ndarray) -> float:
            return np.dot(x, returns.mean()) * 252 - target_return
        constraints.append({
            'type': 'ineq',
            'fun': target_return_constraint
        })

    # Define bounds for weights
    bounds = tuple((min_weight, max_weight) for _ in range(returns.shape[1]))

    # Initial guess (equally distributed weights)
    initial_guess = np.array([1.0 / returns.shape[1]] * returns.shape[1])

    # Perform optimization
    result = minimize(
        composite_objective,
        initial_guess,
        method='SLSQP',
        bounds=bounds,
        constraints=constraints
    )

    if result.success:
        optimized_weights = result.x
        portfolio_volatility = np.sqrt(np.dot(optimized_weights.T, np.dot(returns.cov() * 252, optimized_weights)))
        portfolio_return = np.dot(returns.mean(), optimized_weights) * 252
        sharpe_ratio_val = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else np.nan

        if portfolio_volatility <= 1e-6:
            st.error("Optimized portfolio has near-zero volatility. Optimization constraints may be too restrictive.")
            return None

        # Store optimized results in session state
        st.session_state.backtest_results['optimized_weights'] = optimized_weights
        st.session_state.backtest_results['optimized_return'] = portfolio_return
        st.session_state.backtest_results['optimized_volatility'] = portfolio_volatility
        st.session_state.backtest_results['optimized_sharpe'] = sharpe_ratio_val

        return optimized_weights
    else:
        st.error("Optimization failed. Try adjusting your constraints or target return.")
        return None

def monte_carlo_simulation(
    returns: pd.DataFrame,
    num_simulations: int = 1000,
    periods: int = 252,
    mean_returns: pd.Series = None,
    cov_matrix: pd.DataFrame = None,
    mean_reversion: bool = False,
    mean_reversion_speed: float = 0.1,
    long_term_mean: pd.Series = None,
    time_varying_vol: bool = False,
    vol_change_rate: float = 0.0,
    stress_shocks: dict = None,
    stress_period: int = None,
    return_distribution: str = 'normal'
) -> np.ndarray:
    """
    Perform Monte Carlo simulations for portfolio returns.

    Parameters:
    - returns (pd.DataFrame): Historical returns of assets.
    - num_simulations (int): Number of simulation paths.
    - periods (int): Number of periods to simulate.
    - mean_returns (pd.Series, optional): Mean returns of assets.
    - cov_matrix (pd.DataFrame, optional): Covariance matrix of asset returns.
    - mean_reversion (bool): If True, applies mean reversion.
    - mean_reversion_speed (float): Speed of mean reversion.
    - long_term_mean (pd.Series, optional): Long-term mean for mean reversion.
    - time_varying_vol (bool): If True, allows volatility to change over time.
    - vol_change_rate (float): Rate at which volatility changes.
    - stress_shocks (dict, optional): Dict of shocks to apply at specified period.
    - stress_period (int, optional): Period at which to apply stress shocks.
    - return_distribution (str): Distribution type ('normal' or 'log-normal').

    Returns:
    - np.ndarray: Array of simulated cumulative returns.
    """
    if mean_returns is None:
        mean_returns = returns.mean()
    if cov_matrix is None:
        cov_matrix = returns.cov()

    num_assets = returns.shape[1]
    available_selected = returns.columns.tolist()

    def simulate():
        weights = np.random.random(num_assets)
        weights /= np.sum(weights)
        simulated_prices = [1]  # Initial price
        current_mean = mean_returns.copy()
        current_vol = np.sqrt(np.diag(cov_matrix))
        for t in range(periods):
            if mean_reversion and long_term_mean is not None:
                current_mean += mean_reversion_speed * (long_term_mean - current_mean)
            if time_varying_vol:
                current_vol += vol_change_rate
                current_vol = np.clip(current_vol, 0, None)  # Ensure volatility doesn't go negative

            adjusted_cov_matrix = np.outer(current_vol, current_vol) * np.corrcoef(returns.T)
            if return_distribution == 'log-normal':
                simulated_returns = np.random.lognormal(mean=np.log(1 + current_mean), sigma=current_vol) - 1
            else:
                simulated_returns = np.random.multivariate_normal(current_mean, adjusted_cov_matrix)

            # Apply stress shocks if applicable
            if stress_shocks and t == stress_period:
                for idx, ticker in enumerate(available_selected):
                    simulated_returns[idx] += stress_shocks.get(ticker, 0)

            portfolio_return = np.dot(simulated_returns, weights)
            simulated_prices.append(simulated_prices[-1] * (1 + portfolio_return))
        cumulative_return = simulated_prices[-1] - 1
        return cumulative_return

    portfolio_returns = Parallel(n_jobs=-1)(
        delayed(simulate)() for _ in range(num_simulations)
    )

    return np.array(portfolio_returns)

@handle_exceptions
def plot_efficient_frontier(returns: pd.DataFrame, num_portfolios: int = 1000, rf: float = 0.0, portfolio_name='Portfolio') -> None:
    """
    Plot an enhanced Efficient Frontier with optimized portfolio marked.

    Parameters:
    - returns (pd.DataFrame): Historical returns of assets.
    - num_portfolios (int): Number of portfolios to simulate.
    - rf (float): Risk-free rate for Sharpe Ratio calculation.
    - portfolio_name (str): Name of the portfolio being optimized.
    """
    from scipy.optimize import minimize

    num_assets = returns.shape[1]

    def generate_portfolio_metrics(weights: np.ndarray) -> tuple:
        portfolio_return = np.dot(returns.mean(), weights) * 252
        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
        sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0
        return portfolio_volatility, portfolio_return, sharpe_ratio

    # Generate random portfolios
    weights_list = np.random.dirichlet(np.ones(num_assets), num_portfolios)
    portfolio_metrics = np.array([generate_portfolio_metrics(w) for w in weights_list])

    # Filter out invalid portfolios
    portfolio_metrics = portfolio_metrics[~np.isnan(portfolio_metrics).any(axis=1)]

    if portfolio_metrics.size == 0:
        st.warning("No valid portfolios to plot on the Efficient Frontier.")
        return

    ef_df = pd.DataFrame({
        'Std Dev': portfolio_metrics[:, 0],
        'Return': portfolio_metrics[:, 1],
        'Sharpe Ratio': portfolio_metrics[:, 2]
    })
    fig = px.scatter(
        ef_df,
        x='Std Dev',
        y='Return',
        color='Sharpe Ratio',
        color_continuous_scale='Turbo',
        title='📈 Efficient Frontier',
        labels={
            'Std Dev': 'Annualized Volatility (Std Dev)',
            'Return': 'Annualized Return (%)',
            'Sharpe Ratio': 'Sharpe Ratio'
        },
        hover_data={
            'Sharpe Ratio': ':.2f',
            'Std Dev': ':.2f',
            'Return': ':.2f'
        },
        template=THEME
    )

    # Highlight the portfolio with the maximum Sharpe ratio
    max_sharpe_idx = ef_df['Sharpe Ratio'].idxmax()
    max_sharpe = ef_df.loc[max_sharpe_idx]
    fig.add_trace(go.Scatter(
        x=[max_sharpe['Std Dev']],
        y=[max_sharpe['Return']],
        mode='markers+text',
        marker=dict(color='gold', size=12, symbol='star'),
        name='Max Sharpe Ratio',
        text=["Max Sharpe"],
        textposition="top center",
        hoverinfo='text'
    ))
    fig.add_annotation(
        x=max_sharpe['Std Dev'],
        y=max_sharpe['Return'],
        text="Max Sharpe",
        showarrow=True,
        arrowhead=1,
        ax=0,
        ay=-40
    )

    # Add optimized portfolio if available
    optimized_weights = st.session_state.backtest_results.get('optimized_weights', None)
    optimized_return = st.session_state.backtest_results.get('optimized_return', None)
    optimized_volatility = st.session_state.backtest_results.get('optimized_volatility', None)
    optimized_sharpe = st.session_state.backtest_results.get('optimized_sharpe', None)

    if all(v is not None for v in [optimized_weights, optimized_return, optimized_volatility, optimized_sharpe]):
        if optimized_volatility > 1e-6 and np.isfinite(optimized_sharpe):
            fig.add_trace(go.Scatter(
                x=[optimized_volatility],
                y=[optimized_return],
                mode='markers+text',
                marker=dict(color='red', size=16, symbol='diamond'),
                name=f'{portfolio_name} Optimized',
                text=["Optimized"],
                textposition="top center",
                hoverinfo='text'
            ))
            fig.add_annotation(
                x=optimized_volatility,
                y=optimized_return,
                text="Optimized",
                showarrow=True,
                arrowhead=2,
                ax=0,
                ay=-50
            )

    fig.update_layout(
        hovermode=HOVERMODE,
        xaxis=dict(showgrid=True, title=dict(text='Annualized Volatility (Std Dev)')),
        yaxis=dict(showgrid=True, title=dict(text='Annualized Return (%)')),
        legend=dict(title="Portfolio", x=0.01, y=0.99)
    )
    fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')), selector=dict(mode='markers'))
    st.plotly_chart(fig, use_container_width=True, config={'responsive': True, 'scrollZoom': True})

@handle_exceptions
def generate_portfolio(returns: pd.DataFrame, rf: float = 0.0) -> tuple:
    """
    Generate a random portfolio and calculate its metrics.

    Parameters:
    - returns (pd.DataFrame): Historical returns of assets.
    - rf (float): Risk-free rate for Sharpe Ratio calculation.

    Returns:
    - tuple: (volatility: float, return: float, sharpe_ratio: float)
    """
    try:
        weights = np.random.dirichlet(np.ones(returns.shape[1]))
        portfolio_return = np.dot(returns.mean(), weights) * 252
        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
        sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0
        return portfolio_volatility, portfolio_return, sharpe_ratio
    except Exception as e:
        st.error(f"Error generating portfolio: {e}")
        return np.nan, np.nan, np.nan  # Return NaNs to indicate failure

def plot_allocation_pie(weights: list, assets: list, title: str = '🥧 Portfolio Allocation', hover_info: str = "percent+name") -> None:
    """
    Plots the portfolio allocation pie chart.
    
    Parameters:
    - weights (list): Allocation weights for each asset.
    - assets (list): List of asset ticker symbols.
    - title (str): Title of the pie chart.
    - hover_info (str): Information to display on hover ('percent+name', etc.).
    
    Returns:
    - None
    """
    allocation_df = pd.DataFrame({
        'Asset': assets,
        'Weight': weights
    })
    
    # Determine hover data based on hover_info
    if hover_info == "percent+name":
        hover_data = {'Weight': ':.2f%'}
    elif hover_info == "name":
        hover_data = {}
    else:
        hover_data = {'Weight': ':.2f%'}
    
    fig = px.pie(
        allocation_df,
        names='Asset',
        values='Weight',
        title=title,
        hover_data=hover_data,
        hole=0.3  # Donut chart appearance
    )
    
    fig.update_traces(textposition='inside', textinfo='percent+label')
    fig.update_layout(
        uniformtext_minsize=12,
        uniformtext_mode='hide',
        legend_title="Assets",
        showlegend=True,
        height=FIG_HEIGHT
    )
    
    st.plotly_chart(fig, use_container_width=True, height=600, config={'responsive': True})

@handle_exceptions
def plot_rolling_metrics(returns: pd.Series, windows: list = [30, 90, 180, 252]) -> None:
    """
    Plot rolling metrics (Volatility and Sharpe Ratio) over multiple windows.

    Parameters:
    - returns (pd.Series): Daily returns of the portfolio.
    - windows (list): List of window sizes for rolling calculations.
    """
    metrics = {}
    for window in windows:
        rolling_return = returns.rolling(window).mean() * 252
        rolling_vol = returns.rolling(window).std() * np.sqrt(252)
        rolling_sharpe = (rolling_return - 0.02) / rolling_vol
        metrics[f'Rolling {window}-Day Volatility'] = rolling_vol
        metrics[f'Rolling {window}-Day Sharpe Ratio'] = rolling_sharpe
    fig = go.Figure()
    for metric_name, metric_series in metrics.items():
        fig.add_trace(go.Scatter(x=metric_series.index, y=metric_series, mode='lines', name=metric_name))
    fig.update_layout(
        title='Rolling Metrics Over User-Defined Periods',
        xaxis_title='Date',
        yaxis_title='Value',
        hovermode='x unified',
        template=THEME
    )
    st.plotly_chart(fig, use_container_width=True)

@handle_exceptions
def plot_risk_return_attribution(returns: pd.Series, weights: list) -> None:
    """
    Plot Risk-Return Attribution for the portfolio.

    Parameters:
    - returns (pd.Series or pd.DataFrame): Returns of assets.
    - weights (list): Allocation weights for each asset.
    """
    try:
        if isinstance(returns, pd.Series):
            asset_returns = returns.mean() * 252
            asset_volatility = returns.std() * np.sqrt(252)
            asset_contribution = weights[0] * asset_volatility if len(weights) > 0 else 0
            hover_names = [returns.name] if returns.name else ['Asset']
            x = [asset_volatility]
            y = [asset_returns]
            size = [asset_contribution]
            color = [weights[0]]
        else:
            asset_returns = returns.mean() * 252
            asset_volatility = returns.std() * np.sqrt(252)
            asset_contribution = np.array(weights) * asset_volatility
            hover_names = returns.columns.tolist()
            x = asset_volatility.values
            y = asset_returns.values
            size = asset_contribution
            color = weights

        fig = px.scatter(
            x=x,
            y=y,
            size=size,
            color=color,
            hover_name=hover_names,
            title='Risk-Return Attribution',
            labels={'x': 'Annualized Volatility', 'y': 'Annualized Return', 'color': 'Weight (%)'},
            size_max=60,
            color_continuous_scale='Viridis'
        )
        fig.update_layout(template='plotly_dark')
        st.plotly_chart(fig, use_container_width=True)
    except Exception as e:
        st.error(f"Error plotting risk-return attribution: {e}")
# ----------------------------
# Caching Functions
# ----------------------------
@st.cache_data(show_spinner=False, persist=True)
def get_tickers() -> pd.DataFrame:
    """
    Fetch and combine S&P 500 and NASDAQ-100 tickers from Wikipedia.

    Returns:
    - pd.DataFrame: DataFrame containing combined tickers and company names.
    """
    try:
        # Fetch S&P 500 companies
        sp500_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
        sp500_response = requests.get(sp500_url, verify=False)
        sp500_table = pd.read_html(sp500_response.text)[0]
        sp500 = sp500_table[['Symbol', 'Security']].rename(columns={'Symbol': 'Ticker', 'Security': 'Company Name'})
        sp500['Ticker'] = sp500['Ticker'].str.replace('.', '-', regex=False)

        # Fetch NASDAQ-100 companies
        nasdaq100_url = 'https://en.wikipedia.org/wiki/NASDAQ-100'
        nasdaq100_response = requests.get(nasdaq100_url, verify=False)
        nasdaq100_tables = pd.read_html(nasdaq100_response.text)

        nasdaq100 = pd.DataFrame()
        for table in nasdaq100_tables:
            if 'Ticker' in table.columns and 'Company' in table.columns:
                nasdaq100 = table[['Ticker', 'Company']].rename(columns={'Ticker': 'Ticker', 'Company': 'Company Name'})
                nasdaq100['Ticker'] = nasdaq100['Ticker'].str.replace('.', '-', regex=False)
                break

        combined = pd.concat([sp500, nasdaq100], ignore_index=True)
        combined = combined.drop_duplicates(subset=['Ticker'])
        return combined.sort_values('Ticker').reset_index(drop=True)
    except Exception as e:
        st.error(f"Error fetching tickers: {e}")
        return pd.DataFrame(columns=['Ticker', 'Company Name'])

@st.cache_data(show_spinner=False, persist=True)
def download_data(tickers: list, start: str, end: str, retries: int = 3, backoff_factor: float = 0.3) -> pd.DataFrame:
    """
    Download historical adjusted closing prices for specified tickers.

    Parameters:
    - tickers (list): List of ticker symbols.
    - start (str): Start date in 'YYYY-MM-DD' format.
    - end (str): End date in 'YYYY-MM-DD' format.
    - retries (int): Number of retries for failed requests.
    - backoff_factor (float): Backoff factor for retries.

    Returns:
    - pd.DataFrame: DataFrame containing adjusted closing prices.
    """
    try:
        # Configure retry strategy for requests (used by yfinance internally)
        session = requests.Session()
        retry = Retry(
            total=retries,
            read=retries,
            connect=retries,
            backoff_factor=backoff_factor,
            status_forcelist=(500, 502, 504),
        )
        adapter = HTTPAdapter(max_retries=retry)
        session.mount('http://', adapter)
        session.mount('https://', adapter)

        # Attempt to download data
        data = yf.download(tickers, start=start, end=end, progress=False, session=session)['Adj Close']

        # Handle potential empty data
        if isinstance(data, pd.Series):
            data = data.to_frame()
        if data.empty:
            st.warning("No price data available for the selected portfolio. Please check the ticker symbols and date range.")
            return pd.DataFrame()

        if data.index.tz is not None:
            data.index = data.index.tz_localize(None)

        # Fill missing data
        data_filled = data.fillna(method='ffill').fillna(method='bfill')

        # Check for remaining missing values
        if data_filled.isnull().values.any():
            missing_count = data_filled.isnull().sum().sum()
            st.warning(f"Data contains {missing_count} missing values after filling. Some calculations may be affected.")
            # Optionally, you can drop remaining missing values or handle them as needed
            data_filled = data_filled.dropna()

            if data_filled.empty:
                st.error("All data was missing after filling. Please revise ticker selections or date range.")
                return pd.DataFrame()

        return data_filled
    except Exception as e:
        st.error(f"Error downloading data: {e}")
        return pd.DataFrame()

@handle_exceptions
def calculate_metrics(
    returns: pd.Series, 
    cum_returns: pd.Series, 
    rf: float = 0.02
) -> dict:
    """
    Calculate portfolio performance metrics without benchmark comparisons.
    
    Args:
        returns (pd.Series): Portfolio returns
        cum_returns (pd.Series): Cumulative returns
        rf (float): Risk-free rate
    
    Returns:
        dict: Dictionary of metrics with consistent data types
    """
    metrics = {}
    
    # Initialize with default values
    initial_balance = 10000.0
    metrics['Start Balance'] = initial_balance
    
    def safe_calculation(calculation, default="N/A"):
        """Helper function to safely perform calculations"""
        try:
            result = calculation()
            if isinstance(result, (int, float)):
                if not np.isinf(result) and not np.isnan(result):
                    return result
            return default
        except Exception:
            return default
    
    if not cum_returns.empty and not returns.empty:
        # Basic portfolio metrics
        end_balance = safe_calculation(
            lambda: initial_balance * cum_returns.iloc[-1]
        )
        years = (cum_returns.index[-1] - cum_returns.index[0]).days / 365.25

        metrics.update({
            'End Balance': end_balance,
            'Annualized Return (CAGR)': safe_calculation(
                lambda: round(calculate_cagr(cum_returns, rf) * 100, 2) if years > 0 else "N/A"
            ),
            'Best Year': safe_calculation(
                lambda: round(
                    returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).max() * 100, 
                    2
                )  # Percentage
            ),
            'Worst Year': safe_calculation(
                lambda: round(
                    returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).min() * 100, 
                    2
                )  # Percentage
            ),
            'Standard Deviation (Annualized)': safe_calculation(
                lambda: round(returns.std() * np.sqrt(252) * 100, 2)  # Percentage
            ),
            'Maximum Drawdown': safe_calculation(
                lambda: round(calculate_drawdown(cum_returns) * 100, 2)  # Percentage
            ),
            'Sharpe Ratio': safe_calculation(
                lambda: round(calculate_sharpe_ratio(returns, rf), 2)
            ),
            'Sortino Ratio': safe_calculation(
                lambda: round(calculate_sortino_ratio(returns, rf), 2)
            ),
            'Calmar Ratio': safe_calculation(
                lambda: round(calculate_calmar_ratio(returns, cum_returns, rf), 2) if cum_returns is not None else "N/A"
            )
        })
    else:
        # Set default values for empty data
        default_metrics = {
            'End Balance': "N/A",
            'Annualized Return (CAGR)': "N/A",
            'Best Year': "N/A",
            'Worst Year': "N/A",
            'Standard Deviation (Annualized)': "N/A",
            'Maximum Drawdown': "N/A",
            'Sharpe Ratio': "N/A",
            'Sortino Ratio': "N/A",
            'Calmar Ratio': "N/A"
        }
        metrics.update(default_metrics)
    
    # Ensure all metrics are present
    expected_metrics = [
        'Start Balance', 'End Balance', 'Annualized Return (CAGR)', 'Best Year',
        'Worst Year', 'Standard Deviation (Annualized)', 'Maximum Drawdown',
        'Sharpe Ratio', 'Sortino Ratio', 'Calmar Ratio'
    ]

    for metric in expected_metrics:
        if metric not in metrics:
            metrics[metric] = "N/A"

    return metrics
# ----------------------------
# End of Helper Functions
# ----------------------------

# ----------------------------
# Streamlit Layout
# ----------------------------
st.set_page_config(page_title="🎯 Portfolio Optimizer", layout="wide")

# Display loading spinner
with st.spinner("🎯 Portfolio Optimizer is loading, please be patient..."):
    # Simulate loading delay if necessary
    pass

nav_steps = [
    {"name": "Configure Portfolio", "icon": "📁"},
    {"name": "Run Backtest", "icon": "📊"},
    {"name": "Optimize Portfolio", "icon": "🔧"},
    {"name": "Monte Carlo Simulations", "icon": "📈"},
    {"name": "Risk Analysis", "icon": "⚠️"}
]

with st.sidebar:
    st.header("📂 Navigation")
    # Create a list of (label, value) tuples for radio buttons
    nav_options = [(f"{step['icon']} {step['name']}", step['name']) for step in nav_steps]
    labels, values = zip(*nav_options)
    
    # Use radio buttons for direct navigation with a callback
    def set_step():
        selected_label = st.session_state.nav_radio  # Access the selected label from session_state
        selected_idx = labels.index(selected_label)
        st.session_state.step = values[selected_idx]
    
    selected_label = st.radio("Select a Step:", options=labels, key="nav_radio", on_change=set_step)

current_step = st.session_state.step

tickers = get_tickers()
# Define asset_options by formatting ticker symbols with company names
asset_options = tickers.apply(
    lambda row: format_asset_option(row['Ticker'], row['Company Name']), 
    axis=1
).tolist()

if current_step == "Configure Portfolio":
    st.title("🎯 Portfolio Optimizer")
    # Display success message after deletion
    if 'delete_success' in st.session_state:
        st.success(st.session_state.delete_success)
        if st.session_state.get('show_balloons', False):
            st.balloons()
            st.session_state.show_balloons = False  # Reset balloon flag
        del st.session_state.delete_success
    # Display success message after editing
    if 'edit_success' in st.session_state:
        st.success(st.session_state.edit_success)
        del st.session_state.edit_success
    
    # Initialize show_proceed in session state
    if 'show_proceed' not in st.session_state:
        st.session_state.show_proceed = False
        
    # Define callback function to hide welcome message
    def hide_welcome():
        st.session_state.show_proceed = True
        st.session_state.selected_subtab = "➕ Create New Portfolio"  # Set default tab

    # Display onboarding message and button if not proceeded
    if not st.session_state.show_proceed:
        st.info("Welcome! Let's get you started with your first portfolio.")
        st.write("""
            1. **Configure your portfolio** by entering a name, start date, end date, and other details.
            2. **Select the assets** you want to include and set their allocations.
            3. **Run a backtest** to see how your portfolio performs.
            4. **Compare your portfolio** with a benchmark or another portfolio.
            5. **Explore the performance metrics** and visualizations.
        """)
        st.button("Got U, Let's Explore", key="explore_button", on_click=hide_welcome)
    # ----------------------------
    # Configure Portfolio
    # ----------------------------

    if st.session_state.add_new_portfolio:
        if st.session_state.add_new_portfolio:
            if st.session_state.edit_portfolio:
                st.header(f"✏️ Editing {st.session_state.edit_portfolio} Portfolio")
            else:
                st.header("➕ Create New Portfolio")
        
        # Initialize or fetch the portfolio being edited
        if st.session_state.edit_portfolio:
            # Editing existing portfolio
            portfolio_to_edit = next((p for p in st.session_state.portfolios if p['name'] == st.session_state.edit_portfolio), None)
            if portfolio_to_edit:
                new_portfolio = portfolio_to_edit.copy()
                st.text_input("📛 Portfolio Name", value=new_portfolio['name'], key="portfolio_name")
                start_date_input = st.date_input(
                    "📅 Start Date:",
                    value=new_portfolio['start_date'],
                    min_value=datetime(1900, 1, 1),
                    help="Choose the start date for backtesting."
                )
                end_date_input = st.date_input(
                    "📅 End Date:",
                    value=new_portfolio['end_date'],
                    min_value=datetime(1900, 1, 1),
                    help="Choose the end date for backtesting."
                )
                rf_rate = st.number_input(
                    "📈 Risk-Free Rate (%)",
                    min_value=0.0,
                    max_value=10.0,
                    value=new_portfolio['rf_rate'] * 100,
                    format="%.2f",
                    help="Enter the risk-free rate as a percentage."
                ) / 100
                broker_fee = st.number_input(
                    "💸 Broker Fee (%)",
                    min_value=0.0,
                    max_value=10.0,
                    value=new_portfolio['broker_fee'] * 100,
                    step=0.0001,
                    format="%.4f",
                    help="Enter the broker fee as a percentage per transaction."
                ) / 100
                rebalance_freq = st.selectbox(
                    "🔄 Rebalance Frequency",
                    options=list(FREQUENCY_MAPPING.keys()),
                    index=list(FREQUENCY_MAPPING.values()).index(new_portfolio['rebalance_freq']),
                    help="Choose how often the portfolio should be rebalanced."
                )
                selected_assets_new = st.multiselect(
                    "🗂️ Select Assets:",
                    options=asset_options,
                    format_func=lambda x: f"{x} - {get_company_name(tickers, x)}",
                    default=[f"{ticker} - {get_company_name(tickers, ticker)}" for ticker in new_portfolio['selected']],
                    help="Choose the assets you want to include in your portfolio."
                )
            else:
                st.error("❌ Selected portfolio to edit was not found.")
                st.session_state.add_new_portfolio = False
                st.session_state.edit_portfolio = None
                st.stop()
        else:
            # Creating a new portfolio
            st.text_input("📛 Portfolio Name", key="portfolio_name")
            start_date_input = st.date_input(
                "📅 Start Date:",
                value=datetime.today() - timedelta(days=365 * 15),
                min_value=datetime(1900, 1, 1),
                help="Choose the start date for backtesting."
            )
            end_date_input = st.date_input(
                "📅 End Date:",
                value=datetime.today(),
                min_value=datetime(1900, 1, 1),
                help="Choose the end date for backtesting."
            )
            rf_rate = st.number_input(
                "📈 Risk-Free Rate (%)",
                min_value=0.0,
                max_value=10.0,
                value=st.session_state.default_config['rf_rate'] * 100,
                format="%.2f",
                help="Enter the risk-free rate as a percentage."
            ) / 100
            broker_fee = st.number_input(
                "💸 Broker Fee (%)",
                min_value=0.0,
                max_value=10.0,
                value=st.session_state.default_config['broker_fee'] * 100,
                step=0.0001,
                format="%.4f",
                help="Enter the broker fee as a percentage per transaction."
            ) / 100
            rebalance_freq = st.selectbox(
                "🔄 Rebalance Frequency",
                options=list(FREQUENCY_MAPPING.keys()),
                index=list(FREQUENCY_MAPPING.keys()).index('Monthly'),
                help="Choose how often the portfolio should be rebalanced."
            )
            selected_assets_new = st.multiselect(
                "🗂️ Select Assets:",
                options=asset_options,
                format_func=lambda x: f"{x} - {get_company_name(tickers, x)}",
                default=[],
                help="Choose the assets you want to include in your portfolio."
            )
        
        if selected_assets_new:
            # Extract tickers from selected assets
            selected_tickers_new = [option.split(' - ')[0] for option in selected_assets_new if ' - ' in option]
            # Asset Allocation
            st.subheader("💰 Asset Allocation")
            allocation_df = pd.DataFrame({
                'Ticker': selected_tickers_new,
                'Allocation (%)': [100.0 / len(selected_tickers_new)] * len(selected_tickers_new)
            })
            allocations = []
            cols = st.columns(len(selected_tickers_new))
            for idx, ticker in enumerate(selected_tickers_new):
                with cols[idx]:
                    allocation = st.number_input(
                        f"{ticker} (%)",
                        min_value=0.0,
                        max_value=100.0,
                        value=100.0 / len(selected_tickers_new),
                        step=0.1,
                        key=f"alloc_{ticker}",
                        help=f"Set the allocation percentage for {ticker}."
                    )
                    allocations.append(allocation)
                    allocation_df.at[idx, 'Allocation (%)'] = allocation
        
            total_allocation_new = sum(allocations)
            st.markdown(f"**🧮 Total Allocation:** {total_allocation_new:.2f}%")
        
            if not np.isclose(total_allocation_new, 100.0, atol=1e-2):
                st.warning("⚠️ Allocations must sum to 100%. Please adjust the allocations.")
        
        else:
            st.info("🧐 Select at least one asset to allocate your portfolio.")
        
        # Submit Button
        submit_button_label = "✅ Update Portfolio" if st.session_state.edit_portfolio else "✅ Add Portfolio"
        if st.button(submit_button_label):
            # Validation
            if not st.session_state.portfolio_name:
                st.error("❌ Please provide a portfolio name.")
            elif not selected_assets_new:
                st.error("❌ Please select at least one asset.")
            elif not np.isclose(total_allocation_new, 100.0, atol=1e-2):
                st.error(f"❌ Allocations must sum to 100%. Currently sum to {total_allocation_new:.2f}%.")
            else:
                # Prepare the new or updated portfolio
                new_portfolio = {
                    'name': st.session_state.portfolio_name,
                    'start_date': pd.to_datetime(start_date_input),
                    'end_date': pd.to_datetime(end_date_input),
                    'rf_rate': rf_rate,
                    'broker_fee': broker_fee,
                    'rebalance_freq': FREQUENCY_MAPPING.get(rebalance_freq, 'M'),
                    'selected': selected_tickers_new,
                    'allocations': allocations
                }
        
                if st.session_state.edit_portfolio:
                    # Update existing portfolio
                    for idx, p in enumerate(st.session_state.portfolios):
                        if p['name'] == st.session_state.edit_portfolio:
                            st.session_state.portfolios[idx] = new_portfolio
                            break
                    st.success(f"🎉 Portfolio '{new_portfolio['name']}' updated successfully!")
                    st.session_state.edit_portfolio = None  # Reset edit mode
                else:
                    # Add as a new portfolio
                    if new_portfolio['name'] in [p['name'] for p in st.session_state.portfolios]:
                        st.error("❌ Portfolio name already exists. Please choose a unique name.")
                    else:
                        st.session_state.portfolios.append(new_portfolio)
                        st.success(f"🎉 Portfolio '{new_portfolio['name']}' added successfully!")
        
                st.balloons()
                st.session_state.add_new_portfolio = False  # Hide the form
                st.rerun()
        
        if st.button("❌ Cancel"):
            st.session_state.add_new_portfolio = False
            st.session_state.edit_portfolio = None
            st.rerun()
    
    # Show portfolio management interface if proceeded
    elif st.session_state.show_proceed:
        # Show existing portfolios if any
        if st.session_state.portfolios:
            st.subheader("📁 Manage Existing Portfolios")
            portfolio_names = [p['name'] for p in st.session_state.portfolios]
            selected_portfolio = st.selectbox("🔍 Select a Portfolio", portfolio_names)
            
            if selected_portfolio:
                portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)
                
                with st.expander(f"ℹ️ Details for **{selected_portfolio}**"):
                    st.write(f"**Start Date:** {portfolio['start_date'].strftime('%Y-%m-%d')}")
                    st.write(f"**End Date:** {portfolio['end_date'].strftime('%Y-%m-%d')}")
                    st.write(f"**Rebalance Frequency:** {rebalance_freq_inverse_mapping.get(portfolio['rebalance_freq'], portfolio['rebalance_freq'])}")
                    st.write("**Allocations:**")
                    allocation_df = pd.DataFrame({
                        'Ticker': portfolio['selected'],
                        'Allocation (%)': portfolio['allocations']
                    })
                    st.table(allocation_df)
                
                # Action Buttons with Icons
                action_col0, action_col1, action_col2, action_col3 = st.columns(4)

                with action_col0:
                    if st.button("➕ Create New Portfolio", key="create_new_portfolio"):
                        st.session_state.add_new_portfolio = True
                        st.rerun()

                with action_col1:
                    if st.button(f"🗑️ Delete {selected_portfolio}", key=f"delete_{selected_portfolio}"):
                        st.session_state.portfolios = [p for p in st.session_state.portfolios if p['name'] != selected_portfolio]
                        st.session_state.delete_success = f"✅ Portfolio '{selected_portfolio}' deleted successfully."
                        st.session_state.show_balloons = True  # Optional: To control balloon display
                        st.rerun()

                with action_col2:
                    if st.button(f"✏️ Edit {selected_portfolio}", key=f"edit_{selected_portfolio}"):
                        st.session_state.edit_portfolio = selected_portfolio
                        st.session_state.add_new_portfolio = True  # Ensure the form is visible
                        st.rerun()

                with action_col3:
                    if st.button(f"📄 Duplicate {selected_portfolio}", key=f"duplicate_{selected_portfolio}"):
                        duplicated_portfolio = portfolio.copy()
                        duplicated_portfolio['name'] = f"{selected_portfolio}_Copy"
                        # Ensure the new name is unique
                        if duplicated_portfolio['name'] in portfolio_names:
                            counter = 1
                            while f"{selected_portfolio}_Copy{counter}" in portfolio_names:
                                counter += 1
                            duplicated_portfolio['name'] = f"{selected_portfolio}_Copy{counter}"
                        st.session_state.portfolios.append(duplicated_portfolio)
                        st.success(f"🎉 Portfolio '{duplicated_portfolio['name']}' duplicated successfully!")
                        st.rerun()
        
        st.markdown("---")  # Separator line for clarity

    # ----------------------------
    # Documentation
    # ----------------------------
    with st.expander("📖 Documentation"):  
        st.markdown("""
        ### Documentation

        **Portfolio Configuration:**
        - **Portfolio Name:** Enter a unique name for your portfolio.
        - **Risk-Free Rate:** The theoretical return of an investment with zero risk, often based on government bonds.
        - **Sharpe Ratio:** Measures the performance of an investment compared to a risk-free asset, after adjusting for its risk.
            *Formula:* $$\\frac{R_p - R_f}{\\sigma_p}$$
        - **VaR (Value at Risk):** Estimates the maximum potential loss over a specific time frame at a given confidence level.
        - **CVaR (Conditional Value at Risk):** The expected loss exceeding the VaR, providing insight into tail risk.
        - **Sortino Ratio:** Similar to the Sharpe Ratio but only penalizes downside volatility.
            *Formula:* $$\\frac{R_p - R_f}{\\sigma_d}$$
        - **Maximum Drawdown:** The largest peak-to-trough decline in the portfolio's value over a specific period.
        - **Alpha:** Measures the active return on an investment compared to a market index.
            *Formula:* $$R_p - [R_f + \\beta (R_m - R_f)]$$
        - **Beta:** Indicates the volatility of an investment relative to the market.
            *Formula:* $$\\beta = \\frac{Cov(R_p, R_m)}{Var(R_m)}$$
        - **Information Ratio:** Measures portfolio returns beyond the returns of a benchmark, adjusted for the volatility of those returns.
            *Formula:* $$\\frac{R_p - R_b}{TE}$$
        - **Gain/Loss Ratio:** The ratio of total gains to total losses in the portfolio.
        - **Modigliani–Modigliani Measure (M²):** Adjusts the portfolio return to the risk of a benchmark, allowing for comparison.
            *Formula:* $$M² = \\text{Sharpe Ratio}_{\\text{Portfolio}} \\times \\sigma_{\\text{Benchmark}} + R_f$$
        - **Tracking Error:** Measures the standard deviation of the difference between portfolio returns and benchmark returns.
        - **Upside/Downside Capture Ratio:** Measures how well the portfolio captures the benchmark's positive and negative movements respectively.
        - **Risk Factor Attribution:** Decomposes portfolio returns based on exposure to different risk factors like Momentum, Value, and Size.
        - **Performance Attribution:** Breaks down portfolio performance by individual assets, showing each asset's contribution to overall returns.
        - **Recommendation Engine:** Provides suggestions for portfolio adjustments to optimize performance based on current allocations and objectives.
        """)

if current_step == "Run Backtest":
        st.title("📊 Run Backtest")
        if not st.session_state.portfolios:
            st.warning("Please add at least one portfolio to run backtest.")
        else:
            st.subheader("🔍 Select Portfolios to Compare")
            comparison_type = st.selectbox(
                "Comparison Type",
                ["Portfolio vs Portfolio"],
                key="comparison_type"
            )

            portfolio_names = [p['name'] for p in st.session_state.portfolios]

            # Dynamic labels based on comparison_type
            portfolio1_label = "Select Portfolio" if comparison_type == "Portfolio vs Benchmark" else "Select First Portfolio"
            portfolio2_disabled = comparison_type == "Portfolio vs Benchmark"

            portfolio1 = st.selectbox(
                portfolio1_label,
                portfolio_names,
                key="p1"
            )

            portfolio2 = st.selectbox(
                "Select Second Portfolio",
                portfolio_names,
                key="p2",
                disabled=portfolio2_disabled
            )

            run = st.button("Run Backtest")
            if run:
                with st.spinner("Running backtest..."):
                    comparison_type = "Portfolio vs Portfolio"
                    if comparison_type == "Portfolio vs Portfolio":
                        if portfolio1 == portfolio2:
                            st.error("Please select two different portfolios for comparison.")
                            st.stop()
                        try:
                            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)
                        except StopIteration as e:
                            st.error("One of the selected portfolios was not found.")
                            st.stop()

                        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)

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..."):
                                # Run optimization
                                optimized_weights = optimize_portfolio(
                                    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.")

if current_step == "Monte Carlo Simulations":
    st.title("📈 Monte Carlo Simulations")
    
    if st.session_state.portfolios:
        portfolio_names = [p['name'] for p in st.session_state.portfolios]
        selected_portfolio = st.selectbox("📁 Select Portfolio for Simulation", portfolio_names)
        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)
        
        # 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()
                
                # Organize simulation settings and results into tabs
                simulation_tabs = st.tabs(["🔧 Settings", "📊 Results"])
                
                # Settings Tab
                with simulation_tabs[0]:
                    st.header("🔧 Simulation Settings")
                    
                    # Simulation Parameters
                    sim_col1, sim_col2 = st.columns(2)
                    
                    with sim_col1:
                        num_simulations = st.number_input(
                            "🔢 Number of Simulations",
                            min_value=100,
                            max_value=10000,
                            value=1000,
                            step=100,
                            help="Define how many simulation paths to generate."
                        )
                        periods = st.number_input(
                            "📆 Number of Periods",
                            min_value=1,
                            max_value=252,
                            value=252,
                            step=1,
                            help="Set the number of periods (e.g., days) for each simulation."
                        )
                    
                    with sim_col2:
                        return_distribution = st.selectbox(
                            "📊 Return Distribution",
                            options=["Normal", "Log-Normal"],
                            index=0,
                            help="Choose the statistical distribution for asset returns."
                        )
                        enable_mean_reversion = st.checkbox("🔄 Enable Mean Reversion", help="Implement mean reversion in simulated returns.")
                    
                    # Mean Reversion Parameters
                    if enable_mean_reversion:
                        mr_col1, mr_col2 = st.columns(2)
                        with mr_col1:
                            mean_reversion_speed = st.slider(
                                "⚡ Mean Reversion Speed",
                                min_value=0.0,
                                max_value=1.0,
                                value=0.1,
                                step=0.01,
                                help="Adjust the speed at which returns revert to the mean."
                            )
                        with mr_col2:
                            long_term_mean_input = st.number_input(
                                "📈 Long-term Mean Return (%)",
                                value=0.0,
                                step=0.1,
                                help="Set the target mean return for mean reversion."
                            ) / 100  # Convert to decimal
                
                    # Time-Varying Volatility
                    enable_time_varying_vol = st.checkbox("📉 Enable Time-Varying Volatility", help="Allow volatility to change over time in simulations.")
                    if enable_time_varying_vol:
                        vol_change_rate = st.slider(
                            "📈 Volatility Change Rate",
                            min_value=-0.5,
                            max_value=0.5,
                            value=0.0,
                            step=0.01,
                            help="Set the rate at which volatility changes each period."
                        )
                
                    # Stress Testing
                    st.subheader("⚠️ Stress Testing Parameters")
                    enable_stress_testing = st.checkbox("⚠️ Enable Stress Testing")
                    stress_shocks = {}
                    if enable_stress_testing:
                        selected_stress_assets = st.multiselect(
                            "🔍 Select Assets to Apply Stress Shocks",
                            options=available_selected,
                            help="Choose assets to apply specific stress shocks."
                        )
                        for ticker in selected_stress_assets:
                            shock = st.number_input(
                                f"💥 Stress Shock to {ticker} (%)",
                                min_value=-100.0,
                                max_value=100.0,
                                value=0.0,
                                step=0.1,
                                help=f"Define the percentage shock for {ticker}."
                            ) / 100  # Convert to decimal
                            stress_shocks[ticker] = shock
                
                    # Sensitivity Analysis (Optional)
                    st.subheader("🔄 Sensitivity Analysis Parameters")
                    enable_sensitivity_analysis = st.checkbox("🔎 Enable Sensitivity Analysis", help="Assess how changes in market factors affect portfolio risk.")
                    sensitivity_adjustments = {}
                    if enable_sensitivity_analysis:
                        sensitivity_col1, sensitivity_col2 = st.columns(2)
                        with sensitivity_col1:
                            interest_rate_change = st.number_input(
                                "💹 Change in Interest Rates (bps)",
                                value=0.0,
                                step=0.1,
                                help="Specify the change in interest rates in basis points."
                            ) / 10000  # Convert to decimal
                        with sensitivity_col2:
                            inflation_rate_change = st.number_input(
                                "📈 Change in Inflation Rates (bps)",
                                value=0.0,
                                step=0.1,
                                help="Specify the change in inflation rates in basis points."
                            ) / 10000  # Convert to decimal
                        
                        for ticker in available_selected:
                            col_a, col_b = st.columns(2)
                            with col_a:
                                interest_sens = st.number_input(
                                    f"📊 Interest Rate Sensitivity for {ticker}",
                                    value=1.0,
                                    step=0.1,
                                    help=f"Set sensitivity of {ticker} to interest rate changes."
                                )
                            with col_b:
                                inflation_sens = st.number_input(
                                    f"📊 Inflation Rate Sensitivity for {ticker}",
                                    value=1.0,
                                    step=0.1,
                                    help=f"Set sensitivity of {ticker} to inflation rate changes."
                                )
                            adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change
                            sensitivity_adjustments[ticker] = adjustment
                
                    # Simulation Trigger
                    if st.button("✅ Run Simulations"):
                        with st.spinner("🧮 Running Monte Carlo simulations..."):
                            simulated_returns = monte_carlo_simulation(
                                returns=returns, 
                                num_simulations=int(num_simulations), 
                                periods=int(periods),
                                mean_returns=returns.mean(), 
                                cov_matrix=returns.cov(),
                                mean_reversion=enable_mean_reversion,
                                mean_reversion_speed=mean_reversion_speed if enable_mean_reversion else 0.0,
                                long_term_mean=np.full(len(available_selected), long_term_mean_input / 252) if enable_mean_reversion else None,
                                time_varying_vol=enable_time_varying_vol,
                                vol_change_rate=vol_change_rate if enable_time_varying_vol else 0.0,
                                stress_shocks=stress_shocks if enable_stress_testing else {},
                                return_distribution=return_distribution.lower()
                            )
                            st.session_state.simulated_returns = simulated_returns
                            st.success("🎉 Simulations completed successfully!")
                
                # Results Tab
                with simulation_tabs[1]:
                    if 'simulated_returns' in st.session_state:
                        simulated_returns = st.session_state.simulated_returns
                        st.header("📊 Simulation Results")
        
                        # Descriptive Statistics
                        st.subheader("📈 Descriptive Statistics")
                        st.write(pd.Series(simulated_returns).describe())
        
                        # Histogram
                        st.subheader("📊 Returns Distribution")
                        fig_hist = px.histogram(
                            simulated_returns,
                            nbins=50,
                            title='📊 Simulated Returns Distribution',
                            labels={'value': 'Simulated Returns', 'count': 'Frequency'},
                            template=THEME
                        )
                        st.plotly_chart(fig_hist, use_container_width=True)
        
                        # Cumulative Distribution Function (CDF)
                        st.subheader("📉 Cumulative Distribution Function (CDF)")
                        fig_cdf = px.ecdf(
                            simulated_returns,
                            title='📉 Cumulative Distribution of Simulated Returns',
                            labels={'value': 'Simulated Returns', 'cumcount': 'CDF'},
                            template=THEME
                        )
                        st.plotly_chart(fig_cdf, use_container_width=True)
        
                        # Box Plot
                        st.subheader("📦 Box Plot of Simulated Returns")
                        fig_box = px.box(
                            pd.DataFrame(simulated_returns, columns=['Returns']),
                            y='Returns',
                            title='📦 Box Plot of Simulated Returns',
                            template=THEME
                        )
                        st.plotly_chart(fig_box, use_container_width=True)
        
                        # Summary Statistics Table
                        st.subheader("📝 Summary Statistics")
                        summary_stats = pd.DataFrame({
                            'Metric': ['Mean', 'Median', 'Standard Deviation', 'Minimum', 'Maximum'],
                            'Value': [
                                f"{np.mean(simulated_returns):.4f}",
                                f"{np.median(simulated_returns):.4f}",
                                f"{np.std(simulated_returns):.4f}",
                                f"{np.min(simulated_returns):.4f}",
                                f"{np.max(simulated_returns):.4f}"
                            ]
                        })
                        st.table(summary_stats)
        
                        # Interpretation
                        st.markdown("""
                        **📖 Interpretation:**
                        - **Descriptive Statistics:** Provides an overview of the distribution of simulated portfolio returns.
                        - **Histogram:** Visualizes the frequency distribution of returns.
                        - **CDF:** Shows the probability that a return is less than or equal to a particular value.
                        - **Box Plot:** Highlights the median, quartiles, and potential outliers in the return distribution.
                        - **Summary Statistics:** Summarizes key metrics from the simulation runs.
                        """)
                    else:
                        st.info("🔍 Run simulations to view results here.")
    #else:
        #st.warning("Please add at least one portfolio to run Monte Carlo simulations.")

# Main Risk Analysis Section
if current_step == "Risk Analysis":
    st.title("⚠️ Risk Analysis")
    
    if st.session_state.portfolios:
        portfolio_names = [p['name'] for p in st.session_state.portfolios]
        selected_portfolio = st.selectbox("📁 Select Portfolio for Risk Analysis", portfolio_names)
        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)
        
        # 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()
                
                # Organize risk analysis into tabs
                risk_analysis_tabs = st.tabs(["📉 Risk Metrics", "📈 Visualizations", "🛠️ Sensitivity Analysis"])
                
                # Risk Metrics Tab
                with risk_analysis_tabs[0]:
                    st.header("📉 Risk Metrics")
                    
                    # Risk Parameters
                    risk_params_col1, risk_params_col2 = st.columns(2)
                    
                    with risk_params_col1:
                        confidence_level = st.number_input(
                            "🔍 Confidence Level for VaR/CVaR (%)",
                            min_value=90.0,
                            max_value=99.0,
                            value=95.0,
                            step=1.0,
                            help="Set the confidence level for risk metrics like VaR and CVaR."
                        ) / 100  # Convert to decimal
                    
                    with risk_params_col2:
                        enable_stress_testing = st.checkbox("⚠️ Enable Stress Testing", help="Apply specific shocks to assets to assess portfolio resilience.")
                    
                    if enable_stress_testing:
                        st.markdown("#### ⚠️ Stress Testing Shocks")
                        stress_shocks = {}
                        for ticker in available_selected:
                            shock = st.number_input(
                                f"💥 Stress Shock to {ticker} (%)",
                                min_value=-100.0,
                                max_value=100.0,
                                value=0.0,
                                step=0.1,
                                help=f"Define the percentage shock for {ticker}."
                            ) / 100  # Convert to decimal
                            stress_shocks[ticker] = shock
                        if not any(shock != 0 for shock in stress_shocks.values()):
                            st.warning("⚠️ No stress shocks applied. Consider adding at least one to perform stress testing.")
                    
                    # Sensitivity Analysis Inputs
                    st.subheader("🔎 Sensitivity Analysis Parameters")
                    enable_sensitivity_analysis = st.checkbox("🔄 Enable Sensitivity Analysis", help="Assess how changes in market factors affect portfolio risk.")
                    sensitivity_adjustments = {}
                    if enable_sensitivity_analysis:
                        sensitivity_col1, sensitivity_col2 = st.columns(2)
                        with sensitivity_col1:
                            interest_rate_change = st.number_input(
                                "💹 Change in Interest Rates (bps)",
                                value=0.0,
                                step=0.1,
                                help="Specify the change in interest rates in basis points."
                            ) / 10000  # Convert to decimal
                        with sensitivity_col2:
                            inflation_rate_change = st.number_input(
                                "📈 Change in Inflation Rates (bps)",
                                value=0.0,
                                step=0.1,
                                help="Specify the change in inflation rates in basis points."
                            ) / 10000  # Convert to decimal
                        
                        for ticker in available_selected:
                            col_a, col_b = st.columns(2)
                            with col_a:
                                interest_sens = st.number_input(
                                    f"📊 Interest Rate Sensitivity for {ticker}",
                                    value=1.0,
                                    step=0.1,
                                    help=f"Set sensitivity of {ticker} to interest rate changes."
                                )
                            with col_b:
                                inflation_sens = st.number_input(
                                    f"📊 Inflation Rate Sensitivity for {ticker}",
                                    value=1.0,
                                    step=0.1,
                                    help=f"Set sensitivity of {ticker} to inflation rate changes."
                                )
                            adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change
                            sensitivity_adjustments[ticker] = adjustment
                
                    # Calculate Risk Metrics Button
                    if st.button("📊 Calculate Risk Metrics"):
                        with st.spinner("🧮 Calculating risk metrics..."):
                            # Apply stress shocks if enabled
                            portfolio_returns = returns.copy()
                            if enable_stress_testing:
                                for ticker, shock in stress_shocks.items():
                                    portfolio_returns[ticker] += shock
                            
                            # Apply sensitivity adjustments if enabled
                            if enable_sensitivity_analysis:
                                for ticker, adjustment in sensitivity_adjustments.items():
                                    portfolio_returns[ticker] += adjustment
                            
                            # Ensure no extreme negative returns
                            portfolio_returns = portfolio_returns.clip(lower=-1.0)
                            
                            # Calculate weighted portfolio returns
                            weights = np.array(portfolio['allocations']) / 100
                            portfolio_returns = portfolio_returns.dot(weights)
                            
                            # Store in session state
                            st.session_state.portfolio_returns = portfolio_returns
                            
                            # Calculate metrics
                            cum_returns = (1 + portfolio_returns).cumprod()
                            var = calculate_var(portfolio_returns, confidence_level)
                            cvar = calculate_cvar(portfolio_returns, confidence_level)
                            skewness = skew(portfolio_returns)
                            kurt = kurtosis(portfolio_returns)
                            annual_vol = portfolio_returns.std() * np.sqrt(252)
                            max_drawdown_val = calculate_drawdown(cum_returns) * 100
                            
                            # Store metrics
                            risk_metrics = {
                                'Value at Risk (VaR)': f"{var * 100:.2f}%",
                                'Conditional Value at Risk (CVaR)': f"{cvar * 100:.2f}%",
                                'Skewness': f"{skewness:.2f}",
                                'Kurtosis': f"{kurt:.2f}",
                                'Annualized Volatility (%)': f"{annual_vol * 100:.2f}%",
                                'Maximum Drawdown (%)': f"{max_drawdown_val:.2f}%"
                            }
                            
                            st.session_state.risk_metrics = risk_metrics
                            st.success("✅ Risk metrics calculated successfully!")
        
                # Visualizations Tab
                with risk_analysis_tabs[1]:
                    st.header("📈 Risk Visualizations")
                    
                    if 'risk_metrics' in st.session_state:
                        var = st.session_state.risk_metrics.get('Value at Risk (VaR)', None)
                        cvar = st.session_state.risk_metrics.get('Conditional Value at Risk (CVaR)', None)
                        portfolio_returns_local = st.session_state.portfolio_returns
                        cum_returns = (1 + portfolio_returns_local).cumprod()
                        
                        # VaR and CVaR Plot
                        st.subheader("📉 Cumulative Returns with VaR and CVaR")
                        fig_cum = px.line(
                            cum_returns,
                            x=cum_returns.index,
                            y=cum_returns,
                            title='📉 Cumulative Returns Over Time',
                            labels={'y': 'Cumulative Returns', 'x': 'Date'},
                            template=THEME
                        )
                        if var is not None and cvar is not None:
                            fig_cum.add_hline(
                                y=1 + var,
                                line_dash="dash",
                                line_color="red",
                                annotation_text=f"VaR ({confidence_level*100:.0f}%): {var*100:.2f}%",
                                annotation_position="bottom right"
                            )
                            fig_cum.add_hline(
                                y=1 + cvar,
                                line_dash="dash",
                                line_color="blue",
                                annotation_text=f"CVaR ({confidence_level*100:.0f}%): {cvar*100:.2f}%",
                                annotation_position="bottom right"
                            )
                        st.plotly_chart(fig_cum, use_container_width=True)
                        
                        # Returns Distribution with VaR and CVaR
                        st.subheader("📊 Returns Distribution with VaR and CVaR")
                        fig_dist = px.histogram(
                            portfolio_returns_local,
                            nbins=50,
                            title='📊 Simulated Returns Distribution',
                            labels={'value': 'Returns', 'count': 'Frequency'},
                            template=THEME
                        )
                        if var is not None and cvar is not None:
                            fig_dist.add_vline(
                                x=var,
                                line_dash="dash",
                                line_color="red",
                                annotation_text=f"VaR: {var*100:.2f}%",
                                annotation_position="top left"
                            )
                            fig_dist.add_vline(
                                x=cvar,
                                line_dash="dash",
                                line_color="blue",
                                annotation_text=f"CVaR: {cvar*100:.2f}%",
                                annotation_position="top left"
                            )
                        st.plotly_chart(fig_dist, use_container_width=True)
                        
                        # Box Plot
                        st.subheader("📦 Box Plot of Portfolio Returns")
                        fig_box = px.box(
                            pd.DataFrame(portfolio_returns_local, columns=['Returns']),
                            y='Returns',
                            title='📦 Box Plot of Portfolio Returns',
                            template=THEME
                        )
                        st.plotly_chart(fig_box, use_container_width=True)
                        
                        # Interpretation
                        st.markdown("""
                        **📖 Interpretation:**
                        - **VaR (Value at Risk):** Represents the maximum expected loss at the specified confidence level.
                        - **CVaR (Conditional Value at Risk):** Indicates the average loss exceeding the VaR.
                        - **Skewness:** Measures the asymmetry of the return distribution.
                        - **Kurtosis:** Indicates the "tailedness" of the return distribution.
                        - **Annualized Volatility:** Measures the dispersion of returns, indicating risk.
                        - **Maximum Drawdown:** Shows the largest peak-to-trough decline, reflecting potential risk exposure.
                        """)
                    else:
                        st.info("🔍 Calculate risk metrics to view visualizations.")
                
                # Sensitivity Analysis Tab
                with risk_analysis_tabs[2]:
                    st.header("🛠️ Sensitivity Analysis")
                    
                    if 'portfolio_returns' in st.session_state:
                        portfolio_returns_local = st.session_state.portfolio_returns
                        st.subheader("📈 Adjusted Returns Distribution")
                        fig_adj_returns = px.histogram(
                            portfolio_returns_local,
                            nbins=50,
                            title='📈 Adjusted Portfolio Returns Distribution',
                            labels={'value': 'Returns', 'count': 'Frequency'},
                            template=THEME
                        )
                        st.plotly_chart(fig_adj_returns, use_container_width=True)
                        
                        st.subheader("🔄 Risk-Return Scatter Plot")
                        fig_risk_return = px.scatter(
                            x=portfolio_returns_local.std() * np.sqrt(252),
                            y=portfolio_returns_local.mean() * 252,
                            size=[w * 100 for w in weights],
                            color=[w * 100 for w in weights],
                            hover_name=available_selected,
                            title='🔄 Risk vs Return Scatter Plot',
                            labels={'x': 'Annualized Volatility (Std Dev)', 'y': 'Annualized Return'},
                            template=THEME
                        )
                        st.plotly_chart(fig_risk_return, use_container_width=True)
                        
                        # Interpretation
                        st.markdown("""
                        **📖 Interpretation:**
                        - **Adjusted Returns Distribution:** Reflects how stress shocks and sensitivity adjustments affect portfolio returns.
                        - **Risk-Return Scatter Plot:** Visualizes the relationship between risk (volatility) and return for each asset in the portfolio.
                        - **Size & Color Indicators:** Represent the allocation percentage, highlighting each asset's influence on the overall portfolio.
                        """)
                    else:
                        st.info("🔍 Calculate risk metrics to perform sensitivity analysis.")
    #else:
        #st.warning("Please add at least one portfolio to run risk analysis.")