import yfinance as yf
import pandas as pd
import numpy as np
import torch
from datetime import datetime, timedelta
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots

def get_indonesian_stocks():
    """Get list of major Indonesian stocks"""
    return {
        "BBCA.JK": "Bank Central Asia",
        "BBRI.JK": "Bank BRI",
        "BBNI.JK": "Bank BNI",
        "BMRI.JK": "Bank Mandiri",
        "TLKM.JK": "Telkom Indonesia",
        "UNVR.JK": "Unilever Indonesia",
        "ASII.JK": "Astra International",
        "INDF.JK": "Indofood Sukses Makmur",
        "KLBF.JK": "Kalbe Farma",
        "HMSP.JK": "HM Sampoerna",
        "GGRM.JK": "Gudang Garam",
        "ADRO.JK": "Adaro Energy",
        "PGAS.JK": "Perusahaan Gas Negara",
        "JSMR.JK": "Jasa Marga",
        "WIKA.JK": "Wijaya Karya",
        "PTBA.JK": "Tambang Batubara Bukit Asam",
        "ANTM.JK": "Aneka Tambang",
        "SMGR.JK": "Semen Indonesia",
        "INTP.JK": "Indocement Tunggal Prakasa",
        "ITMG.JK": "Indo Tambangraya Megah"
    }

def calculate_technical_indicators(data):
    """Calculate various technical indicators"""
    indicators = {}
    
    # RSI (Relative Strength Index)
    def calculate_rsi(prices, period=14):
        delta = prices.diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
        rs = gain / loss
        rsi = 100 - (100 / (1 + rs))
        return rsi
    
    indicators['rsi'] = {
        'current': calculate_rsi(data['Close']).iloc[-1],
        'values': calculate_rsi(data['Close'])
    }
    
    # MACD
    def calculate_macd(prices, fast=12, slow=26, signal=9):
        exp1 = prices.ewm(span=fast).mean()
        exp2 = prices.ewm(span=slow).mean()
        macd = exp1 - exp2
        signal_line = macd.ewm(span=signal).mean()
        histogram = macd - signal_line
        return macd, signal_line, histogram
    
    macd, signal_line, histogram = calculate_macd(data['Close'])
    indicators['macd'] = {
        'macd': macd.iloc[-1],
        'signal': signal_line.iloc[-1],
        'histogram': histogram.iloc[-1],
        'signal_text': 'BUY' if histogram.iloc[-1] > 0 else 'SELL'
    }
    
    # Bollinger Bands
    def calculate_bollinger_bands(prices, period=20, std_dev=2):
        sma = prices.rolling(window=period).mean()
        std = prices.rolling(window=period).std()
        upper_band = sma + (std * std_dev)
        lower_band = sma - (std * std_dev)
        return upper_band, sma, lower_band
    
    upper, middle, lower = calculate_bollinger_bands(data['Close'])
    current_price = data['Close'].iloc[-1]
    bb_position = (current_price - lower.iloc[-1]) / (upper.iloc[-1] - lower.iloc[-1])
    
    indicators['bollinger'] = {
        'upper': upper.iloc[-1],
        'middle': middle.iloc[-1],
        'lower': lower.iloc[-1],
        'position': 'UPPER' if bb_position > 0.8 else 'LOWER' if bb_position < 0.2 else 'MIDDLE'
    }
    
    # Moving Averages
    indicators['moving_averages'] = {
        'sma_20': data['Close'].rolling(20).mean().iloc[-1],
        'sma_50': data['Close'].rolling(50).mean().iloc[-1],
        'sma_200': data['Close'].rolling(200).mean().iloc[-1],
        'ema_12': data['Close'].ewm(span=12).mean().iloc[-1],
        'ema_26': data['Close'].ewm(span=26).mean().iloc[-1]
    }
    
    # Volume indicators
    indicators['volume'] = {
        'current': data['Volume'].iloc[-1],
        'avg_20': data['Volume'].rolling(20).mean().iloc[-1],
        'ratio': data['Volume'].iloc[-1] / data['Volume'].rolling(20).mean().iloc[-1]
    }
    
    return indicators

def generate_trading_signals(data, indicators):
    """Generate trading signals based on technical indicators"""
    signals = {}
    
    current_price = data['Close'].iloc[-1]
    
    # Initialize scores
    buy_signals = 0
    sell_signals = 0
    
    signal_details = []
    
    # RSI Signal
    rsi = indicators['rsi']['current']
    if rsi < 30:
        buy_signals += 1
        signal_details.append(f"✅ RSI ({rsi:.1f}) - Oversold - BUY signal")
    elif rsi > 70:
        sell_signals += 1
        signal_details.append(f"❌ RSI ({rsi:.1f}) - Overbought - SELL signal")
    else:
        signal_details.append(f"⚪ RSI ({rsi:.1f}) - Neutral")
    
    # MACD Signal
    macd_hist = indicators['macd']['histogram']
    if macd_hist > 0:
        buy_signals += 1
        signal_details.append(f"✅ MACD Histogram ({macd_hist:.4f}) - Positive - BUY signal")
    else:
        sell_signals += 1
        signal_details.append(f"❌ MACD Histogram ({macd_hist:.4f}) - Negative - SELL signal")
    
    # Bollinger Bands Signal
    bb_position = indicators['bollinger']['position']
    if bb_position == 'LOWER':
        buy_signals += 1
        signal_details.append(f"✅ Bollinger Bands - Near lower band - BUY signal")
    elif bb_position == 'UPPER':
        sell_signals += 1
        signal_details.append(f"❌ Bollinger Bands - Near upper band - SELL signal")
    else:
        signal_details.append("⚪ Bollinger Bands - Middle position")
    
    # Moving Averages Signal
    sma_20 = indicators['moving_averages']['sma_20']
    sma_50 = indicators['moving_averages']['sma_50']
    
    if current_price > sma_20 > sma_50:
        buy_signals += 1
        signal_details.append(f"✅ Price above MA(20,50) - Bullish - BUY signal")
    elif current_price < sma_20 < sma_50:
        sell_signals += 1
        signal_details.append(f"❌ Price below MA(20,50) - Bearish - SELL signal")
    else:
        signal_details.append("⚪ Moving Averages - Mixed signals")
    
    # Volume Signal
    volume_ratio = indicators['volume']['ratio']
    if volume_ratio > 1.5:
        buy_signals += 0.5
        signal_details.append(f"✅ High volume ({volume_ratio:.1f}x avg) - Strengthens BUY signal")
    elif volume_ratio < 0.5:
        sell_signals += 0.5
        signal_details.append(f"❌ Low volume ({volume_ratio:.1f}x avg) - Weakens SELL signal")
    else:
        signal_details.append(f"⚪ Normal volume ({volume_ratio:.1f}x avg)")
    
    # Determine overall signal
    total_signals = buy_signals + sell_signals
    signal_strength = (buy_signals / max(total_signals, 1)) * 100
    
    if buy_signals > sell_signals:
        overall_signal = "BUY"
    elif sell_signals > buy_signals:
        overall_signal = "SELL"
    else:
        overall_signal = "HOLD"
    
    # Calculate support and resistance
    recent_high = data['High'].tail(20).max()
    recent_low = data['Low'].tail(20).min()
    
    signals = {
        'overall': overall_signal,
        'strength': signal_strength,
        'details': '\n'.join(signal_details),
        'support': recent_low,
        'resistance': recent_high,
        'stop_loss': recent_low * 0.95 if overall_signal == "BUY" else recent_high * 1.05
    }
    
    return signals

def get_fundamental_data(stock):
    """Get fundamental data for the stock"""
    try:
        info = stock.info
        history = stock.history(period="1d")
        
        fundamental_info = {
            'name': info.get('longName', 'N/A'),
            'current_price': history['Close'].iloc[-1] if not history.empty else 0,
            'market_cap': info.get('marketCap', 0),
            'pe_ratio': info.get('forwardPE', 0),
            'dividend_yield': info.get('dividendYield', 0) * 100 if info.get('dividendYield') else 0,
            'volume': history['Volume'].iloc[-1] if not history.empty else 0,
            'info': f"""
Sector: {info.get('sector', 'N/A')}
Industry: {info.get('industry', 'N/A')}
Market Cap: {format_large_number(info.get('marketCap', 0))}
52 Week High: {info.get('fiftyTwoWeekHigh', 'N/A')}
52 Week Low: {info.get('fiftyTwoWeekLow', 'N/A')}
Beta: {info.get('beta', 'N/A')}
EPS: {info.get('forwardEps', 'N/A')}
Book Value: {info.get('bookValue', 'N/A')}
Price to Book: {info.get('priceToBook', 'N/A')}
            """.strip()
        }
        
        return fundamental_info
    except Exception as e:
        print(f"Error getting fundamental data: {e}")
        return {
            'name': 'N/A',
            'current_price': 0,
            'market_cap': 0,
            'pe_ratio': 0,
            'dividend_yield': 0,
            'volume': 0,
            'info': 'Unable to fetch fundamental data'
        }

def format_large_number(num):
    """Format large numbers to readable format"""
    if num >= 1e12:
        return f"{num/1e12:.2f}T"
    elif num >= 1e9:
        return f"{num/1e9:.2f}B"
    elif num >= 1e6:
        return f"{num/1e6:.2f}M"
    elif num >= 1e3:
        return f"{num/1e3:.2f}K"
    else:
        return f"{num:.2f}"

@spaces.GPU(duration=120)
def predict_prices(data, model, tokenizer, prediction_days=30):
    """Predict future prices using Chronos-Bolt model"""
    try:
        # Prepare data for prediction
        prices = data['Close'].values
        context_length = min(len(prices), 512)
        
        # Tokenize the input
        input_sequence = prices[-context_length:]
        
        # Create prediction input
        prediction_input = torch.tensor(input_sequence).unsqueeze(0).float()
        
        # Generate predictions
        with torch.no_grad():
            forecast = model.generate(
                prediction_input,
                prediction_length=prediction_days,
                temperature=1.0,
                top_k=50,
                top_p=0.9
            )
        
        predictions = forecast[0].numpy()
        
        # Calculate prediction statistics
        last_price = prices[-1]
        predicted_high = np.max(predictions)
        predicted_low = np.min(predictions)
        predicted_mean = np.mean(predictions)
        change_pct = ((predicted_mean - last_price) / last_price) * 100
        
        return {
            'values': predictions,
            'dates': pd.date_range(
                start=data.index[-1] + timedelta(days=1),
                periods=prediction_days,
                freq='D'
            ),
            'high_30d': predicted_high,
            'low_30d': predicted_low,
            'mean_30d': predicted_mean,
            'change_pct': change_pct,
            'summary': f"""
AI Model: Amazon Chronos-Bolt
Prediction Period: {prediction_days} days
Expected Change: {change_pct:.2f}%
Confidence: Medium (based on historical patterns)
Note: AI predictions are for reference only and not financial advice
            """.strip()
        }
    except Exception as e:
        print(f"Error in prediction: {e}")
        return {
            'values': [],
            'dates': [],
            'high_30d': 0,
            'low_30d': 0,
            'mean_30d': 0,
            'change_pct': 0,
            'summary': 'Prediction unavailable due to model error'
        }

def create_price_chart(data, indicators):
    """Create price chart with technical indicators"""
    fig = make_subplots(
        rows=3, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.05,
        subplot_titles=('Price & Moving Averages', 'RSI', 'MACD'),
        row_width=[0.2, 0.2, 0.7]
    )
    
    # Price and Moving Averages
    fig.add_trace(
        go.Candlestick(
            x=data.index,
            open=data['Open'],
            high=data['High'],
            low=data['Low'],
            close=data['Close'],
            name='Price'
        ),
        row=1, col=1
    )
    
    # Add moving averages
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=indicators['moving_averages']['sma_20'],
            name='SMA 20',
            line=dict(color='orange', width=1)
        ),
        row=1, col=1
    )
    
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=indicators['moving_averages']['sma_50'],
            name='SMA 50',
            line=dict(color='blue', width=1)
        ),
        row=1, col=1
    )
    
    # RSI
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=indicators['rsi']['values'],
            name='RSI',
            line=dict(color='purple')
        ),
        row=2, col=1
    )
    
    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
    
    # MACD
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=indicators['macd']['macd'],
            name='MACD',
            line=dict(color='blue')
        ),
        row=3, col=1
    )
    
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=indicators['macd']['signal'],
            name='Signal',
            line=dict(color='red')
        ),
        row=3, col=1
    )
    
    fig.update_layout(
        title='Technical Analysis Dashboard',
        height=900,
        showlegend=True,
        xaxis_rangeslider_visible=False
    )
    
    return fig

def create_technical_chart(data, indicators):
    """Create technical indicators dashboard"""
    fig = make_subplots(
        rows=2, cols=2,
        subplot_titles=('Bollinger Bands', 'Volume', 'Price vs MA', 'RSI Analysis'),
        specs=[[{"secondary_y": False}, {"secondary_y": False}],
               [{"secondary_y": False}, {"secondary_y": False}]]
    )
    
    # Bollinger Bands
    fig.add_trace(
        go.Scatter(x=data.index, y=data['Close'], name='Price', line=dict(color='black')),
        row=1, col=1
    )
    
    # Volume
    fig.add_trace(
        go.Bar(x=data.index, y=data['Volume'], name='Volume', marker_color='lightblue'),
        row=1, col=2
    )
    
    # Price vs Moving Averages
    fig.add_trace(
        go.Scatter(x=data.index, y=data['Close'], name='Price', line=dict(color='black')),
        row=2, col=1
    )
    
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=[indicators['moving_averages']['sma_20']] * len(data),
            name='SMA 20',
            line=dict(color='orange', dash='dash')
        ),
        row=2, col=1
    )
    
    fig.update_layout(
        title='Technical Indicators Overview',
        height=600,
        showlegend=False
    )
    
    return fig

def create_prediction_chart(data, predictions):
    """Create prediction visualization"""
    if not predictions['values'].size:
        return go.Figure()
    
    fig = go.Figure()
    
    # Historical prices
    fig.add_trace(
        go.Scatter(
            x=data.index[-60:],
            y=data['Close'].values[-60:],
            name='Historical Price',
            line=dict(color='blue', width=2)
        )
    )
    
    # Predictions
    fig.add_trace(
        go.Scatter(
            x=predictions['dates'],
            y=predictions['values'],
            name='AI Prediction',
            line=dict(color='red', width=2, dash='dash')
        )
    )
    
    # Confidence interval (simple)
    pred_std = np.std(predictions['values'])
    upper_band = predictions['values'] + (pred_std * 1.96)
    lower_band = predictions['values'] - (pred_std * 1.96)
    
    fig.add_trace(
        go.Scatter(
            x=predictions['dates'],
            y=upper_band,
            name='Upper Band',
            line=dict(color='lightcoral', width=1),
            fill=None
        )
    )
    
    fig.add_trace(
        go.Scatter(
            x=predictions['dates'],
            y=lower_band,
            name='Lower Band',
            line=dict(color='lightcoral', width=1),
            fill='tonexty',
            fillcolor='rgba(255,182,193,0.2)'
        )
    )
    
    fig.update_layout(
        title=f'Price Prediction - Next {len(predictions["dates"])} Days',
        xaxis_title='Date',
        yaxis_title='Price (IDR)',
        hovermode='x unified',
        height=500
    )
    
    return fig