app.py

from flask import Flask, render_template, jsonify, request, session
import datetime
import json
import os
import time
import requests
from functools import wraps
import yfinance as yf
import pandas as pd
import numpy as np
import random
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor, as_completed
from sklearn.preprocessing import MinMaxScaler
from textblob import TextBlob
import warnings
warnings.filterwarnings('ignore')

app = Flask(__name__)
app.secret_key = 'your_secret_key_here'  # Required for session management

# Custom JSON encoder to handle numpy and pandas types
class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
            np.int16, np.int32, np.int64, np.uint8,
            np.uint16, np.uint32, np.uint64)):
            return int(obj)
        elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
            # Handle NaN and Infinity
            if np.isnan(obj):
                return None
            if np.isinf(obj):
                return None
            return float(obj)
        elif isinstance(obj, (np.bool_)):
            return bool(obj)
        elif isinstance(obj, (np.ndarray,)):
            return [self.default(x) for x in obj.tolist()]
        elif isinstance(obj, (datetime.datetime, datetime.date)):
            return obj.isoformat()
        elif isinstance(obj, pd.Series):
            return [self.default(x) for x in obj.tolist()]
        elif isinstance(obj, pd.Timestamp):
            return obj.isoformat()
        elif pd.isna(obj):  # Handle pandas NA/NaN
            return None
        return super().default(obj)

# Configure Flask to use the custom JSON encoder
app.json_encoder = CustomJSONEncoder

# Configuration constants
NEWS_API_KEY = os.getenv('NEWS_API_KEY', 'fd941d9b5c46456a953dc6ecafbe7b50')
FINNHUB_API_KEY = os.getenv('FINNHUB_API_KEY', 'd0b7ec1r01qo0h63fns0d0b7ec1r01qo0h63fnsg')
REQUEST_TIMEOUT = 10
MAX_RETRIES = 3
BATCH_SIZE = 10

# Define sectors and their representative tickers
SECTORS = {
    "Technology": ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "INTC", "AMD", "CRM", "CSCO"],
    "Healthcare": ["JNJ", "PFE", "UNH", "MRK", "ABBV", "LLY", "BMY", "AMGN", "TMO", "ABT"],
    "Energy": ["XOM", "CVX", "COP", "SLB", "EOG", "MPC", "PSX", "VLO", "OXY", "DVN"],
    "Automobile": ["TSLA", "F", "GM", "TM", "STLA", "RIVN", "LCID", "NIO", "HMC", "RACE"],
    "Finance": ["JPM", "BAC", "WFC", "GS", "MS", "C", "BLK", "AXP", "V", "MA"]
}

# Sector colors for UI
SECTOR_COLORS = {
    "Technology": "#3498db",
    "Healthcare": "#27ae60",
    "Energy": "#e67e22",
    "Automobile": "#e74c3c",
    "Finance": "#9b59b6"
}

CACHE_DIR = os.path.join("/tmp", "data_cache")
os.makedirs(CACHE_DIR, exist_ok=True)

# Cache functions
def get_from_cache(key):
    cache_file = os.path.join(CACHE_DIR, f"{key.replace('/', '_')}.json")
    if os.path.exists(cache_file):
        try:
            with open(cache_file, 'r') as f:
                cache_data = json.load(f)
            if time.time() - cache_data['timestamp'] < 3600:  # 1 hour cache
                return cache_data['data']
        except Exception as e:
            print(f"Error reading cache file {cache_file}: {str(e)}")
    return None

def save_to_cache(key, data):
    cache_file = os.path.join(CACHE_DIR, f"{key.replace('/', '_')}.json")
    try:
        with open(cache_file, 'w') as f:
            json.dump({
                'data': data,
                'timestamp': time.time()
            }, f, cls=CustomJSONEncoder)
    except Exception as e:
        print(f"Error writing to cache file {cache_file}: {str(e)}")

# Retry decorator
def retry_with_backoff(retries=MAX_RETRIES, backoff_factor=0.5):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            mtries, mdelay = retries, backoff_factor
            while mtries > 0:
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    mtries -= 1
                    if mtries == 0:
                        raise
                    time.sleep(mdelay * (2 ** (retries - mtries)))
            return None
        return wrapper
    return decorator

# News API functions
@retry_with_backoff()
def fetch_latest_news(ticker="TSLA"):
    url = f"https://newsapi.org/v2/everything?q={ticker}&apiKey={NEWS_API_KEY}&sortBy=publishedAt&language=en"
    response = requests.get(url, timeout=REQUEST_TIMEOUT)
    response.raise_for_status()
    data = response.json()
    
    if data.get("status") == "ok" and data.get("totalResults", 0) > 0:
        article = data["articles"][0]
        return {
            "title": article["title"],
            "content": article.get("content") or article.get("description", ""),
            "ticker": ticker,
            "url": article.get("url", ""),
            "source": article.get("source", {}).get("name", "Unknown"),
            "publishedAt": article.get("publishedAt", "")
        }
    return None

@retry_with_backoff()
def get_multiple_news(query, count=5):
    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={NEWS_API_KEY}&sortBy=publishedAt&language=en&pageSize={count}"
    response = requests.get(url, timeout=REQUEST_TIMEOUT)
    response.raise_for_status()
    data = response.json()
    
    if data.get("status") == "ok" and data.get("totalResults", 0) > 0:
        articles = []
        for article in data["articles"][:count]:
            if article.get("title") and (article.get("content") or article.get("description")):
                articles.append({
                    "title": article["title"],
                    "content": article.get("content") or article.get("description", ""),
                    "url": article.get("url", ""),
                    "source": article.get("source", {}).get("name", "Unknown"),
                    "publishedAt": article.get("publishedAt", "")
                })
        return articles
    return []

# Stock data functions
@lru_cache(maxsize=100)
def get_stock_data(ticker, period="1mo"):
    try:
        stock = yf.Ticker(ticker)
        
        # Try to get history with a longer interval if shorter fails
        for attempt_period in [period, "3mo", "6mo"]:
            hist = stock.history(period=attempt_period)
            if not hist.empty:
                break
        
        if hist.empty:
            print(f"Error: No data available for {ticker}")
            return None
            
        # Get company info with error handling
        try:
            info = stock.info
        except Exception as e:
            print(f"Error fetching info for {ticker}: {str(e)}")
            info = {}
            
        # Verify we have the minimum required data
        if 'Close' not in hist.columns or len(hist) < 2:
            print(f"Error: Insufficient price data for {ticker}")
            return None
            
        return {
            'history': hist,
            'info': info
        }
    except Exception as e:
        print(f"Error fetching stock data for {ticker}: {str(e)}")
        return None

def get_sector_performance(sector_tickers, days=30):
    period = f"{days}d"
    if days > 30:
        period = f"{days//30}mo"  # Convert to months for longer periods
    
    sector_performance = {
        'avg_price_change': 0.0,
        'avg_volume_change': 0.0,
        'top_performers': [],
        'is_booming': False
    }

    stock_performances = []

    for ticker in sector_tickers[:5]:
        try:
            stock_data = get_stock_data(ticker, period=period)
            if not stock_data or stock_data['history'].empty:
                print(f"Skipping {ticker} due to missing data")
                continue

            hist = stock_data['history']
            info = stock_data['info']

            if len(hist) < 2:
                print(f"Insufficient history for {ticker}")
                continue

            first_price = float(hist['Close'].iloc[0])
            last_price = float(hist['Close'].iloc[-1])
            price_change_pct = float(((last_price - first_price) / first_price) * 100)

            first_volume = float(hist['Volume'].iloc[0])
            avg_volume = float(hist['Volume'].mean())
            volume_change_pct = float(((avg_volume - first_volume) / first_volume) * 100 if first_volume > 0 else 0)

            stock_data = {
                'ticker': str(ticker),
                'company': str(info.get('shortName', ticker)),
                'current_price': float(last_price),
                'price_change_pct': float(price_change_pct),
                'volume_change_pct': float(volume_change_pct),
                'market_cap': int(info.get('marketCap', 0)),
                'chart_data': {
                    'dates': [d.strftime('%Y-%m-%d') for d in hist.index],
                    'prices': [float(p) for p in hist['Close'].values]
                }
            }

            stock_performances.append(stock_data)
        except Exception as e:
            print(f"Error processing {ticker}: {str(e)}")
            continue

    if stock_performances:
        sector_performance['avg_price_change'] = float(sum(stock['price_change_pct'] for stock in stock_performances) / len(stock_performances))
        sector_performance['avg_volume_change'] = float(sum(stock['volume_change_pct'] for stock in stock_performances) / len(stock_performances))
        stock_performances.sort(key=lambda x: x['price_change_pct'], reverse=True)
        sector_performance['top_performers'] = stock_performances[:5]
        sector_performance['is_booming'] = bool(sector_performance['avg_price_change'] > 2.0)

    return sector_performance

def generate_stock_insight(ticker, sector):
    try:
        stock_data = get_stock_data(ticker, period="30d")
        if not stock_data or stock_data['history'].empty:
            return {
                "analysis": f"Insufficient data available for {ticker}.",
                "outlook": "Unable to provide outlook due to limited data.",
                "risk_factors": ["Data availability"]
            }

        hist = stock_data['history']
        info = stock_data['info']

        first_price = hist['Close'].iloc[0]
        last_price = hist['Close'].iloc[-1]
        price_change_30d = ((last_price - first_price) / first_price) * 100
        price_volatility = hist['Close'].pct_change().std() * 100

        recent_news = fetch_latest_news(ticker)
        news_factor = f"Recent news: {recent_news['title']}" if recent_news else ""

        industry_trends = {
            "Technology": "ongoing AI innovations and chip demand",
            "Healthcare": "post-pandemic recovery and aging population demands",
            "Energy": "transition to renewables and fluctuating oil prices",
            "Automobile": "EV adoption and supply chain improvements",
            "Finance": "interest rate adjustments and fintech integration"
        }

        trend = industry_trends.get(sector, "evolving market conditions")

        if price_change_30d > 10:
            analysis = f"{ticker} has shown strong performance with a {price_change_30d:.2f}% gain over the last 30 days. This outperformance appears driven by {trend}. {news_factor}"
            outlook = "Short-term outlook remains positive with momentum indicators suggesting continued strength."
            risk_factors = ["Market volatility", "Potential market-wide corrections", "Overextended valuations"]
        elif price_change_30d > 0:
            analysis = f"{ticker} has shown moderate growth with a {price_change_30d:.2f}% gain over the last 30 days, in line with {sector} sector trends. {news_factor}"
            outlook = f"The stock appears to be following the {sector} sector with steady growth potential."
            risk_factors = ["Competitive pressures", "Sector rotation", "Modest growth projections"]
        else:
            analysis = f"{ticker} has underperformed with a {price_change_30d:.2f}% decline over the last 30 days. {news_factor}"
            outlook = "The stock may face continued headwinds in the short term."
            risk_factors = ["Continued underperformance", "Negative sentiment", "Technical weakness"]

        if price_volatility > 3:
            risk_factors.append(f"High price volatility ({price_volatility:.2f}%)")

        return {
            "analysis": analysis,
            "outlook": outlook,
            "risk_factors": risk_factors
        }
    except Exception as e:
        print(f"Error generating insight for {ticker}: {str(e)}")
        return {
            "analysis": f"Error analyzing {ticker}",
            "outlook": "Unable to provide outlook.",
            "risk_factors": ["Data error"]
        }

# Technical Analysis Functions
def calculate_technical_indicators(historical_data):
    """Calculate various technical indicators for the given historical data."""
    try:
        df = historical_data.copy()
        
        # Calculate RSI (14-day)
        delta = df['Close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
        rs = gain / loss
        df['RSI'] = 100 - (100 / (1 + rs))
        
        # Calculate MACD
        exp1 = df['Close'].ewm(span=12, adjust=False).mean()
        exp2 = df['Close'].ewm(span=26, adjust=False).mean()
        df['MACD'] = exp1 - exp2
        df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
        
        # Calculate Moving Averages
        df['SMA_20'] = df['Close'].rolling(window=20, min_periods=1).mean()
        df['SMA_50'] = df['Close'].rolling(window=50, min_periods=1).mean()
        df['SMA_200'] = df['Close'].rolling(window=200, min_periods=1).mean()
        
        # Calculate Bollinger Bands
        df['BB_middle'] = df['Close'].rolling(window=20, min_periods=1).mean()
        bb_std = df['Close'].rolling(window=20, min_periods=1).std()
        df['BB_upper'] = df['BB_middle'] + 2 * bb_std
        df['BB_lower'] = df['BB_middle'] - 2 * bb_std
        
        # Handle NaN values
        for column in df.columns:
            if df[column].dtype in [np.float64, np.float32]:
                df[column] = df[column].fillna(method='ffill').fillna(method='bfill')
                df[column] = df[column].fillna(0)
        
        return df
    except Exception as e:
        print(f"Error calculating technical indicators: {str(e)}")
        return historical_data

def get_technical_analysis_summary(df):
    """Generate a summary of technical indicators."""
    try:
        latest = df.iloc[-1]
        prev = df.iloc[-2]
        
        # Handle potential NaN values in the summary
        rsi_value = float(latest.get('RSI', 0)) if not pd.isna(latest.get('RSI', 0)) else 0
        macd_value = float(latest.get('MACD', 0)) if not pd.isna(latest.get('MACD', 0)) else 0
        signal_value = float(latest.get('Signal_Line', 0)) if not pd.isna(latest.get('Signal_Line', 0)) else 0
        
        summary = {
            'indicators': {
                'RSI': {
                    'value': round(rsi_value, 2),
                    'signal': 'Overbought' if rsi_value > 70 else 'Oversold' if rsi_value < 30 else 'Neutral'
                },
                'MACD': {
                    'value': round(macd_value, 2),
                    'signal': 'Bullish' if macd_value > signal_value else 'Bearish'
                },
                'Moving_Averages': {
                    'SMA_20': round(float(latest.get('SMA_20', 0)), 2),
                    'SMA_50': round(float(latest.get('SMA_50', 0)), 2),
                    'SMA_200': round(float(latest.get('SMA_200', 0)), 2),
                    'trend': 'Bullish' if latest.get('SMA_20', 0) > latest.get('SMA_50', 0) > latest.get('SMA_200', 0) 
                            else 'Bearish' if latest.get('SMA_20', 0) < latest.get('SMA_50', 0) < latest.get('SMA_200', 0) 
                            else 'Mixed'
                },
                'Bollinger_Bands': {
                    'upper': round(float(latest.get('BB_upper', 0)), 2),
                    'middle': round(float(latest.get('BB_middle', 0)), 2),
                    'lower': round(float(latest.get('BB_lower', 0)), 2),
                    'position': 'Upper' if latest['Close'] > latest.get('BB_upper', float('inf')) 
                               else 'Lower' if latest['Close'] < latest.get('BB_lower', float('-inf')) 
                               else 'Middle'
                }
            },
            'analysis': []
        }
        
        # Generate analysis points
        if rsi_value > 70:
            summary['analysis'].append('RSI indicates overbought conditions')
        elif rsi_value < 30:
            summary['analysis'].append('RSI indicates oversold conditions')
            
        if macd_value > signal_value and prev.get('MACD', 0) <= prev.get('Signal_Line', 0):
            summary['analysis'].append('MACD shows a fresh bullish crossover')
        elif macd_value < signal_value and prev.get('MACD', 0) >= prev.get('Signal_Line', 0):
            summary['analysis'].append('MACD shows a fresh bearish crossover')
            
        if latest['Close'] > latest.get('BB_upper', float('inf')):
            summary['analysis'].append('Price is trading above upper Bollinger Band, suggesting strong upward momentum')
        elif latest['Close'] < latest.get('BB_lower', float('-inf')):
            summary['analysis'].append('Price is trading below lower Bollinger Band, suggesting strong downward momentum')
            
        if not summary['analysis']:
            summary['analysis'].append('No significant technical signals at this time')
            
        return summary
    except Exception as e:
        print(f"Error generating technical analysis summary: {str(e)}")
        return {
            'indicators': {
                'RSI': {'value': 0, 'signal': 'Neutral'},
                'MACD': {'value': 0, 'signal': 'Neutral'},
                'Moving_Averages': {
                    'SMA_20': 0,
                    'SMA_50': 0,
                    'SMA_200': 0,
                    'trend': 'Neutral'
                },
                'Bollinger_Bands': {
                    'upper': 0,
                    'middle': 0,
                    'lower': 0,
                    'position': 'Middle'
                }
            },
            'analysis': ['Technical analysis currently unavailable']
        }

# User watchlist storage (in-memory for demonstration)
user_watchlists = {}

@app.route('/api/watchlist', methods=['GET', 'POST', 'DELETE'])
def manage_watchlist():
    user_id = session.get('user_id', 'default_user')
    
    if request.method == 'GET':
        # Get user's watchlist
        watchlist = user_watchlists.get(user_id, [])
        
        # Fetch current data for all watchlist stocks
        watchlist_data = []
        for ticker in watchlist:
            try:
                stock_data = get_stock_data(ticker, period="1mo")  # Get 1 month of data for charts
                if stock_data and not stock_data['history'].empty:
                    hist = stock_data['history']
                    latest_price = float(hist['Close'].iloc[-1])
                    price_change = float(hist['Close'].pct_change().iloc[-1] * 100)
                    
                    # Calculate technical indicators
                    tech_data = calculate_technical_indicators(hist)
                    
                    watchlist_data.append({
                        'ticker': ticker,
                        'current_price': latest_price,
                        'price_change': price_change,
                        'last_updated': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
                        'chart_data': {
                            'dates': [d.strftime('%Y-%m-%d') for d in hist.index],
                            'prices': [float(p) for p in hist['Close'].values],
                            'volume': [float(v) for v in hist['Volume'].values],
                            'technical_indicators': {
                                'sma_20': [float(s) if not pd.isna(s) else None for s in tech_data['SMA_20'].values] if 'SMA_20' in tech_data else [],
                                'sma_50': [float(s) if not pd.isna(s) else None for s in tech_data['SMA_50'].values] if 'SMA_50' in tech_data else [],
                                'rsi': [float(r) if not pd.isna(r) else None for r in tech_data['RSI'].values] if 'RSI' in tech_data else []
                            }
                        }
                    })
            except Exception as e:
                print(f"Error fetching data for {ticker}: {str(e)}")
                
        return jsonify({
            'status': 'success',
            'data': watchlist_data
        })
        
    elif request.method == 'POST':
        # Add stock to watchlist
        data = request.get_json()
        ticker = data.get('ticker')
        
        if not ticker:
            return jsonify({'status': 'error', 'message': 'No ticker provided'})
            
        if user_id not in user_watchlists:
            user_watchlists[user_id] = []
            
        if ticker not in user_watchlists[user_id]:
            user_watchlists[user_id].append(ticker)
            
        return jsonify({
            'status': 'success',
            'message': f'Added {ticker} to watchlist'
        })
        
    elif request.method == 'DELETE':
        # Remove stock from watchlist
        data = request.get_json()
        ticker = data.get('ticker')
        
        if not ticker:
            return jsonify({'status': 'error', 'message': 'No ticker provided'})
            
        if user_id in user_watchlists and ticker in user_watchlists[user_id]:
            user_watchlists[user_id].remove(ticker)
            
        return jsonify({
            'status': 'success',
            'message': f'Removed {ticker} from watchlist'
        })

@app.route('/api/price_alerts', methods=['GET', 'POST', 'DELETE'])
def manage_price_alerts():
    user_id = session.get('user_id', 'default_user')
    
    if request.method == 'GET':
        # Get user's price alerts
        alerts = price_alerts.get(user_id, [])
        return jsonify({
            'status': 'success',
            'data': alerts
        })
        
    elif request.method == 'POST':
        # Add new price alert
        data = request.get_json()
        ticker = data.get('ticker')
        target_price = data.get('target_price')
        alert_type = data.get('alert_type')  # 'above' or 'below'
        
        if not all([ticker, target_price, alert_type]):
            return jsonify({
                'status': 'error',
                'message': 'Missing required fields'
            })
            
        if user_id not in price_alerts:
            price_alerts[user_id] = []
            
        # Check if alert already exists for this ticker
        existing_alert = next((a for a in price_alerts[user_id] if a['ticker'] == ticker), None)
        if existing_alert:
            existing_alert.update({
                'target_price': float(target_price),
                'alert_type': alert_type,
                'updated_at': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            })
        else:
            price_alerts[user_id].append({
                'ticker': ticker,
                'target_price': float(target_price),
                'alert_type': alert_type,
                'created_at': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            })
        
        return jsonify({
            'status': 'success',
            'message': 'Price alert created successfully'
        })
        
    elif request.method == 'DELETE':
        # Remove price alert
        data = request.get_json()
        ticker = data.get('ticker')
        
        if not ticker:
            return jsonify({
                'status': 'error',
                'message': 'No ticker provided'
            })
            
        if user_id in price_alerts:
            price_alerts[user_id] = [alert for alert in price_alerts[user_id] if alert['ticker'] != ticker]
            
        return jsonify({
            'status': 'success',
            'message': f'Removed alert for {ticker}'
        })

@app.route('/api/portfolio_analysis', methods=['GET', 'POST', 'DELETE'])
def portfolio_analysis():
    user_id = session.get('user_id', 'default_user')
    
    if request.method == 'DELETE':
        # Remove holding from portfolio
        data = request.get_json()
        ticker = data.get('ticker')
        
        if not ticker:
            return jsonify({
                'status': 'error',
                'message': 'No ticker provided'
            })
            
        if user_id in user_portfolios:
            user_portfolios[user_id] = [h for h in user_portfolios[user_id] if h['ticker'] != ticker]
            
        return jsonify({
            'status': 'success',
            'message': f'Removed {ticker} from portfolio'
        })
    
    elif request.method == 'POST':
        # Add or update portfolio holdings
        data = request.get_json()
        new_holdings = data.get('holdings', [])
        
        if not new_holdings:
            return jsonify({
                'status': 'error',
                'message': 'No holdings provided'
            })
            
        if user_id not in user_portfolios:
            user_portfolios[user_id] = []
            
        # Update or add new holdings
        for new_holding in new_holdings:
            ticker = new_holding['ticker']
            shares = new_holding['shares']
            
            # Check if holding already exists
            existing_holding = next((h for h in user_portfolios[user_id] if h['ticker'] == ticker), None)
            if existing_holding:
                existing_holding['shares'] = shares
            else:
                user_portfolios[user_id].append({
                    'ticker': ticker,
                    'shares': shares
                })
        
    # Get portfolio analysis
    holdings = user_portfolios.get(user_id, [])
    
    if not holdings:
        return jsonify({
            'status': 'success',
            'data': {
                'total_value': 0,
                'daily_change': 0,
                'holdings': [],
                'risk_metrics': {
                    'portfolio_return': 0,
                    'portfolio_volatility': 0,
                    'sharpe_ratio': 0
                }
            }
        })
        
    # Calculate portfolio metrics
    portfolio_data = []
    total_value = 0
    total_daily_change = 0
    
    for holding in holdings:
        ticker = holding['ticker']
        shares = holding['shares']
        
        try:
            stock_data = get_stock_data(ticker, period="2d")
            if stock_data and not stock_data['history'].empty:
                current_price = float(stock_data['history']['Close'].iloc[-1])
                prev_price = float(stock_data['history']['Close'].iloc[-2])
                value = current_price * shares
                daily_change = ((current_price - prev_price) / prev_price) * 100
                
                total_value += value
                total_daily_change += (daily_change * value)
                
                portfolio_data.append({
                    'ticker': ticker,
                    'shares': shares,
                    'current_price': current_price,
                    'value': value,
                    'daily_change': daily_change
                })
        except Exception as e:
            print(f"Error calculating portfolio data for {ticker}: {str(e)}")
            
    # Calculate portfolio risk metrics
    risk_metrics = {
        'portfolio_return': 0,
        'portfolio_volatility': 0,
        'sharpe_ratio': 0
    }
    
    if portfolio_data:
        returns = []
        weights = []
        
        for holding in portfolio_data:
            stock_data = get_stock_data(holding['ticker'], period="1mo")
            if stock_data and not stock_data['history'].empty:
                returns.append(stock_data['history']['Close'].pct_change().dropna())
                weights.append(holding['value'] / total_value)
        
        if returns and weights:
            returns_df = pd.concat(returns, axis=1)
            portfolio_return = np.sum(returns_df.mean() * weights) * 252
            portfolio_volatility = np.sqrt(np.dot(weights, np.dot(returns_df.cov() * 252, weights)))
            sharpe_ratio = portfolio_return / portfolio_volatility if portfolio_volatility != 0 else 0
            
            risk_metrics = {
                'portfolio_return': float(portfolio_return),
                'portfolio_volatility': float(portfolio_volatility),
                'sharpe_ratio': float(sharpe_ratio)
            }
    
    return jsonify({
        'status': 'success',
        'data': {
            'total_value': total_value,
            'daily_change': total_daily_change / total_value if total_value > 0 else 0,
            'holdings': portfolio_data,
            'risk_metrics': risk_metrics
        }
    })

# Storage for user data (in-memory for demonstration)
price_alerts = {}
user_portfolios = {}

# API Routes
@app.route('/')
def index():
    return render_template('index.html')

@app.route('/dashboard')
def dashboard():
    return render_template('user_dashboard.html')

@app.route('/advanced')
def advanced_dashboard():
    return render_template('advanced_dashboard.html')

@app.route('/api/news')
def api_news():
    try:
        count = int(request.args.get('count', BATCH_SIZE))
        news = get_multiple_news("stock market", count=count)
        return jsonify({"status": "success", "data": news})
    except Exception as e:
        return jsonify({"status": "error", "message": str(e)})

@app.route('/api/sectors')
def api_sectors():
    try:
        days = int(request.args.get('days', 7))
        sectors_data = {}

        for sector, tickers in SECTORS.items():
            sector_data = get_sector_performance(tickers, days=days)
            if sector_data['top_performers']:  # Only include sectors with data
                sectors_data[sector] = sector_data

        filter_booming = request.args.get('booming', 'false').lower() == 'true'
        if filter_booming:
            sectors_data = {k: v for k, v in sectors_data.items() if v.get('is_booming', False)}

        return jsonify({"status": "success", "data": sectors_data})
    except Exception as e:
        print(f"Error in api_sectors: {str(e)}")  # Add debug print
        return jsonify({"status": "error", "message": str(e)})

@app.route('/api/stock/<ticker>')
def api_stock_detail(ticker):
    try:
        print(f"\nFetching details for {ticker}...")
        
        # Find sector
        sector = next((s for s, tickers in SECTORS.items() if ticker in tickers), "Unknown")
        print(f"Sector identified: {sector}")
        
        # Get stock data
        print(f"Fetching stock data for {ticker}...")
        stock_data = get_stock_data(ticker, period="1mo")
        
        if not stock_data:
            print(f"No stock data returned for {ticker}")
            return jsonify({
                "status": "error",
                "message": f"Unable to fetch data for {ticker}. Please try again later."
            })

        hist = stock_data['history']
        info = stock_data['info']
        
        if hist.empty:
            print(f"Empty history data for {ticker}")
            return jsonify({
                "status": "error",
                "message": f"No historical data available for {ticker}"
            })
            
        print(f"Calculating technical indicators for {ticker}...")
        try:
            tech_data = calculate_technical_indicators(hist)
            tech_summary = get_technical_analysis_summary(tech_data)
        except Exception as e:
            print(f"Error calculating technical indicators: {str(e)}")
            tech_data = hist
            tech_summary = {
                "indicators": {
                    "RSI": {"value": 0, "signal": "Neutral"},
                    "MACD": {"value": 0, "signal": "Neutral"},
                    "Moving_Averages": {
                        "SMA_20": 0,
                        "SMA_50": 0,
                        "SMA_200": 0,
                        "trend": "Neutral"
                    },
                    "Bollinger_Bands": {
                        "upper": 0,
                        "middle": 0,
                        "lower": 0,
                        "position": "Middle"
                    }
                },
                "analysis": ["Technical analysis currently unavailable"]
            }
        
        print(f"Generating insight for {ticker}...")
        insight = generate_stock_insight(ticker, sector)
        
        print(f"Fetching news for {ticker}...")
        try:
            related_news = get_multiple_news(f"{ticker} stock", count=3)
        except Exception as e:
            print(f"Error fetching news: {str(e)}")
            related_news = []

        # Prepare the response data
        response_data = {
            "ticker": str(ticker),
            "name": str(info.get('shortName', ticker)),
            "sector": str(sector),
            "price": float(hist['Close'].iloc[-1]),
            "marketCap": int(info.get('marketCap', 0)),
            "peRatio": float(info.get('trailingPE', 0)) if info.get('trailingPE') else None,
            "dividend": float(info.get('dividendYield', 0)) if info.get('dividendYield') else None,
            "chartData": {
                'dates': [d.strftime('%Y-%m-%d') for d in hist.index],
                'prices': [float(p) for p in hist['Close'].values],
                'volume': [float(v) for v in hist['Volume'].values],
                'technical_indicators': {
                    'rsi': [float(r) if not pd.isna(r) else None for r in tech_data['RSI'].values] if 'RSI' in tech_data else [],
                    'macd': [float(m) if not pd.isna(m) else None for m in tech_data['MACD'].values] if 'MACD' in tech_data else [],
                    'signal_line': [float(s) if not pd.isna(s) else None for s in tech_data['Signal_Line'].values] if 'Signal_Line' in tech_data else [],
                    'sma_20': [float(s) if not pd.isna(s) else None for s in tech_data['SMA_20'].values] if 'SMA_20' in tech_data else [],
                    'sma_50': [float(s) if not pd.isna(s) else None for s in tech_data['SMA_50'].values] if 'SMA_50' in tech_data else [],
                    'sma_200': [float(s) if not pd.isna(s) else None for s in tech_data['SMA_200'].values] if 'SMA_200' in tech_data else [],
                    'bb_upper': [float(b) if not pd.isna(b) else None for b in tech_data['BB_upper'].values] if 'BB_upper' in tech_data else [],
                    'bb_middle': [float(b) if not pd.isna(b) else None for b in tech_data['BB_middle'].values] if 'BB_middle' in tech_data else [],
                    'bb_lower': [float(b) if not pd.isna(b) else None for b in tech_data['BB_lower'].values] if 'BB_lower' in tech_data else []
                }
            },
            "technical_analysis": tech_summary,
            "insight": insight,
            "news": related_news
        }

        print(f"Successfully prepared response for {ticker}")
        return jsonify({"status": "success", "data": response_data})
        
    except Exception as e:
        print(f"Error in api_stock_detail for {ticker}: {str(e)}")
        import traceback
        traceback.print_exc()
        return jsonify({
            "status": "error",
            "message": f"An error occurred while fetching stock details: {str(e)}"
        })

@app.route('/api/pattern_analysis/<ticker>')
def api_pattern_analysis(ticker):
    try:
        # Get stock data
        stock_data = get_stock_data(ticker, period="6mo")
        if not stock_data or stock_data['history'].empty:
            return jsonify({"status": "error", "message": "No data available"})
            
        df = stock_data['history']
        
        # Simple pattern detection based on price action
        patterns = {}
        
        # Detect basic patterns
        for i in range(2, len(df)):
            # Bullish patterns
            if (df['Close'].iloc[i] > df['Close'].iloc[i-1] > df['Close'].iloc[i-2] and
                df['Volume'].iloc[i] > df['Volume'].iloc[i-1]):
                patterns[df.index[i].strftime('%Y-%m-%d')] = {
                    'name': 'Bullish Trend',
                    'signal': 1
                }
            # Bearish patterns
            elif (df['Close'].iloc[i] < df['Close'].iloc[i-1] < df['Close'].iloc[i-2] and
                  df['Volume'].iloc[i] > df['Volume'].iloc[i-1]):
                patterns[df.index[i].strftime('%Y-%m-%d')] = {
                    'name': 'Bearish Trend',
                    'signal': -1
                }
        
        # Find support and resistance levels
        def find_support_resistance(data, window=20):
            highs = data['High'].rolling(window=window, center=True).max()
            lows = data['Low'].rolling(window=window, center=True).min()
            
            resistance_levels = highs[highs == data['High']].unique()[-3:]
            support_levels = lows[lows == data['Low']].unique()[:3]
            
            return {
                'support': support_levels.tolist(),
                'resistance': resistance_levels.tolist()
            }
        
        # Calculate volume analysis
        volume_mean = df['Volume'].mean()
        volume_std = df['Volume'].std()
        unusual_volume = df[df['Volume'] > volume_mean + 2*volume_std]
        
        # Calculate risk metrics
        returns = df['Close'].pct_change()
        risk_metrics = {
            'volatility': returns.std() * np.sqrt(252),  # Annualized volatility
            'sharpe_ratio': (returns.mean() * 252) / (returns.std() * np.sqrt(252)),  # Assuming risk-free rate of 0
            'max_drawdown': (df['Close'] / df['Close'].expanding().max() - 1).min()
        }
        
        response = {
            'status': 'success',
            'data': {
                'patterns': patterns,
                'support_resistance': find_support_resistance(df),
                'volume_analysis': {
                    'average_volume': int(volume_mean),
                    'unusual_volume_dates': unusual_volume.index.strftime('%Y-%m-%d').tolist(),
                    'unusual_volume_values': unusual_volume['Volume'].tolist()
                },
                'risk_metrics': {k: float(v) for k, v in risk_metrics.items()}
            }
        }
        return jsonify(response)
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)})

@app.route('/api/correlation_analysis')
def api_correlation_analysis():
    try:
        sectors = request.args.get('sectors', 'Technology').split(',')
        tickers = []
        for sector in sectors:
            if sector in SECTORS:
                tickers.extend(SECTORS[sector][:5])  # Take top 5 from each sector
        
        # Get closing prices for all tickers
        prices_dict = {}
        for ticker in tickers:
            stock_data = get_stock_data(ticker, period="1mo")
            if stock_data and not stock_data['history'].empty:
                prices_dict[ticker] = stock_data['history']['Close']
        
        # Create correlation matrix
        df = pd.DataFrame(prices_dict)
        corr_matrix = df.corr()
        
        # Convert to list of correlations
        correlations = []
        for i in range(len(corr_matrix.columns)):
            for j in range(i+1, len(corr_matrix.columns)):
                correlations.append({
                    'stock1': corr_matrix.columns[i],
                    'stock2': corr_matrix.columns[j],
                    'correlation': float(corr_matrix.iloc[i, j])
                })
        
        return jsonify({
            'status': 'success',
            'data': {
                'correlations': correlations,
                'matrix': corr_matrix.to_dict()
            }
        })
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)})

@app.route('/api/sentiment_analysis/<ticker>')
def api_sentiment_analysis(ticker):
    try:
        # Get news articles
        news = get_multiple_news(f"{ticker} stock", count=10)
        
        # Analyze sentiment for each article
        sentiments = []
        for article in news:
            blob = TextBlob(article['content'])
            sentiment = blob.sentiment
            sentiments.append({
                'title': article['title'],
                'polarity': float(sentiment.polarity),
                'subjectivity': float(sentiment.subjectivity),
                'date': article['publishedAt']
            })
        
        # Calculate average sentiment
        avg_sentiment = sum(s['polarity'] for s in sentiments) / len(sentiments) if sentiments else 0
        
        return jsonify({
            'status': 'success',
            'data': {
                'articles': sentiments,
                'average_sentiment': avg_sentiment,
                'sentiment_label': 'Positive' if avg_sentiment > 0.1 else 'Negative' if avg_sentiment < -0.1 else 'Neutral'
            }
        })
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)})

if __name__ == '__main__':
    port = int(os.environ.get('PORT', 5000))
    app.run(host='0.0.0.0', port=port, debug=True)