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
from plotly.subplots import make_subplots
import spaces
import gc
import time
import random
from chronos import ChronosPipeline
from scipy.stats import skew, kurtosis
from typing import Dict, Union, List

# Global variable for model pipeline
pipeline = None 

# --- ADVANCED UTILITIES & CONFIG ---

# Sumber data Covariate eksternal
COVARIATE_SOURCES = {
    'market_indices': ['^GSPC', '^DJI', '^IXIC', '^VIX'],
    'commodities': ['GC=F', 'CL=F'],
}

def clear_gpu_memory():
    """Membersihkan cache memori GPU"""
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        gc.collect()

@spaces.GPU()
def load_pipeline():
    """
    Memuat model Chronos-2 dengan konfigurasi GPU canggih.
    Menggunakan device_map="cuda" dan torch_dtype=torch.float16.
    """
    global pipeline
    try:
        model_name = "amazon/chronos-2" 
        
        if pipeline is None:
            clear_gpu_memory()
            print(f"Loading Chronos model: {model_name}...")
            
            # FIX 1: Menyederhanakan argumen untuk menghindari error 'input_patch_size'
            pipeline = ChronosPipeline.from_pretrained(
                model_name,
                device_map="cuda",  
                torch_dtype=torch.float16, 
                # Menghapus argumen yang mungkin memicu error konfigurasi
            )
            
            pipeline.model = pipeline.model.eval()
            for param in pipeline.model.parameters():
                param.requires_grad = False
            print(f"Chronos model {model_name} loaded successfully on CUDA")
            
        return pipeline
        
    except Exception as e:
        # Menampilkan error yang lebih spesifik
        print(f"Error loading pipeline on CUDA, trying CPU: {str(e)}")
        try:
            # Fallback ke CPU
            pipeline = ChronosPipeline.from_pretrained(model_name, device_map="cpu")
            pipeline.model = pipeline.model.eval()
            print(f"Chronos model {model_name} loaded successfully on CPU (performance degraded)")
            return pipeline
        except Exception as cpu_e:
            raise RuntimeError(f"Failed to load model {model_name} on both CUDA and CPU: {str(cpu_e)}")

# ... (Fungsi-fungsi lain: retry_yfinance_request, fetch_enhanced_covariates, calculate_advanced_risk_metrics)

def retry_yfinance_request(func, max_retries=3, initial_delay=1):
    """Mekanisme retry untuk permintaan yfinance dengan backoff eksponensial."""
    for attempt in range(max_retries):
        try:
            result = func()
            if result is not None and not result.empty:
                return result
            if attempt == max_retries - 1:
                return None
            
            delay = min(8.0, initial_delay * (2 ** attempt) + random.uniform(0, 1))
            time.sleep(delay)
        except Exception:
            if attempt == max_retries - 1:
                return None
            delay = min(8.0, initial_delay * (2 ** attempt) + random.uniform(0, 1))
            time.sleep(delay)

def fetch_enhanced_covariates(data: pd.DataFrame) -> pd.DataFrame:
    """Mengambil data covariate (Indeks Pasar) dan menggabungkannya."""
    start_date = data.index.min().strftime('%Y-%m-%d')
    end_date = data.index.max().strftime('%Y-%m-%d')
    date_range = pd.date_range(start=start_date, end=end_date, freq='D')
    
    # 1. Reindex data asli ke range hari yang kontinu
    data_full = data.reindex(date_range)
    data_full['Close'] = data_full['Close'].fillna(method='ffill')
    data_full['Volume'] = data_full['Volume'].fillna(0)
    
    covariate_df = pd.DataFrame(index=date_range)
    
    # 2. Ambil data dari semua sumber covariate eksternal
    for source_key, symbols in COVARIATE_SOURCES.items():
        for symbol in symbols:
            def fetch_covariate():
                return yf.download(symbol, start=start_date, end=end_date, interval="1d", progress=False)

            cov_data = retry_yfinance_request(fetch_covariate)
            
            if cov_data is not None and not cov_data.empty:
                cov_data = cov_data['Close'].rename(f'cov_{symbol.replace("^", "_").replace("=", "_")}')
                cov_data = cov_data.reindex(date_range)
                covariate_df = covariate_df.merge(cov_data, left_index=True, right_index=True, how='left')

    # 3. Gabungkan dan imputasi
    final_df = data_full.merge(covariate_df, left_index=True, right_index=True, how='left')
    cov_cols = [col for col in final_df.columns if col.startswith('cov_') or col == 'Volume']
    
    # Imputasi Covariates: Forward fill untuk harga/indeks, 0 untuk Volume
    final_df['Volume'] = final_df['Volume'].fillna(0)
    final_df[[col for col in cov_cols if col != 'Volume']] = final_df[[col for col in cov_cols if col != 'Volume']].fillna(method='ffill')
    
    final_df = final_df.dropna(subset=['Close'], how='all')
    
    # Ganti nama kolom sesuai format Chronos
    return final_df.rename(columns={'Close': 'target', 'Volume': 'cov_volume'})

def calculate_advanced_risk_metrics(df: pd.DataFrame, risk_free_rate: float = 0.05) -> Dict[str, Union[float, str]]:
    """Menghitung metrik risiko dan performa lanjutan (Sharpe Ratio, VaR, CVaR, Max Drawdown)."""
    if df.empty or 'Close' not in df.columns:
        return {"error": "Data historis tidak valid untuk perhitungan risiko."}

    try:
        df['Returns'] = df['Close'].pct_change()
        returns = df['Returns'].dropna()
            
        if returns.empty:
            return {"error": "Return historis tidak tersedia."}
            
        days_per_year = 252 
        
        annual_return = returns.mean() * days_per_year
        annual_vol = returns.std() * np.sqrt(days_per_year)
        
        sharpe_ratio = (annual_return - risk_free_rate) / annual_vol if annual_vol != 0 else 0
        
        var_95 = np.percentile(returns, 5) * -1
        cvar_95 = returns[returns < -var_95].mean() * -1
        
        cumulative_returns = (1 + returns).cumprod()
        peak = cumulative_returns.expanding(min_periods=1).max()
        drawdown = (cumulative_returns / peak) - 1
        max_drawdown = drawdown.min()
        
        skewness = skew(returns)
        kurtosis_val = kurtosis(returns)

        return {
            "Annual_Return": f"{annual_return*100:.2f}%",
            "Annual_Volatility": f"{annual_vol*100:.2f}%",
            "Sharpe_Ratio": f"{sharpe_ratio:.2f}",
            "Max_Drawdown": f"{max_drawdown*100:.2f}%",
            "VaR_95_Daily_Loss": f"{var_95*100:.2f}%",
            "CVaR_95_Avg_Loss": f"{cvar_95*100:.2f}%",
            "Skewness": f"{skewness:.2f}",
            "Kurtosis": f"{kurtosis_val:.2f}",
        }
    
    except Exception as e:
        return {"error": f"Risk calculation failed: {str(e)}"}

def predict_technical_indicators_future(data: pd.DataFrame, price_prediction: np.ndarray) -> Dict[str, np.ndarray]:
    """Memprediksi MACD dan Bollinger Bands di masa depan berdasarkan prediksi harga."""
    predictions = {}
    
    # Pastikan price_prediction tidak kosong sebelum diolah
    if price_prediction.size == 0:
        return {"MACD_Future": np.array([]), "MACD_Signal_Future": np.array([]), "BB_Upper_Future": np.array([]), "BB_Lower_Future": np.array([])}

    full_price_series = np.concatenate([data['Close'].values, price_prediction])
    full_price_series = pd.Series(full_price_series)
    
    # MACD dan Signal Line Future
    def calculate_ema(prices, span):
        return prices.ewm(span=span, adjust=False).mean()
        
    ema_12_full = calculate_ema(full_price_series, 12)
    ema_26_full = calculate_ema(full_price_series, 26)
    macd_full = ema_12_full - ema_26_full
    macd_signal_full = calculate_ema(macd_full, 9)
    
    predictions['MACD_Future'] = macd_full.iloc[-len(price_prediction):].values
    predictions['MACD_Signal_Future'] = macd_signal_full.iloc[-len(price_prediction):].values

    # Bollinger Bands Future
    period = 20
    std_dev = 2
    middle_band_full = full_price_series.rolling(window=period).mean()
    std_full = full_price_series.rolling(window=period).std()
    upper_band_full = middle_band_full + (std_full * std_dev)
    lower_band_full = middle_band_full - (std_full * std_dev)
    
    predictions['BB_Upper_Future'] = upper_band_full.iloc[-len(price_prediction):].values
    predictions['BB_Lower_Future'] = lower_band_full.iloc[-len(price_prediction):].values
    
    return predictions


@spaces.GPU(duration=120)
def predict_prices(data, prediction_days=30):
    """Fungsi prediksi utama menggunakan Chronos-2 dengan enhanced covariates."""
    
    # Default return structure for errors (Menggunakan np.array([]) yang aman)
    empty_result = {
        'values': np.array([]), 'dates': pd.Series([], dtype='datetime64[ns]'), 
        'high_30d': 0, 'low_30d': 0, 'mean_30d': 0, 'change_pct': 0, 
        'q01': np.array([]), 'q09': np.array([]),
        'future_macd': np.array([]), 'future_macd_signal': np.array([]), 
        'future_bb_upper': np.array([]), 'future_bb_lower': np.array([]),
        'summary': 'Prediction failed due to model or data error.'
    }
    
    try:
        # 1. Load Model (Akan memanggil load_pipeline yang sudah diperbaiki)
        pipeline = load_pipeline()
        
        data_original = data.copy()
        
        # 2. Enhanced Data Preprocessing & Covariate
        data_enhanced = fetch_enhanced_covariates(data_original)
        
        context_df = data_enhanced.reset_index()
        context_df.columns = ['timestamp'] + [col for col in context_df.columns[1:]]
        context_df['id'] = 'stock_price' 
        
        all_covariates = [col for col in context_df.columns if col not in ['timestamp', 'id', 'target']]
        
        # 3. Model Prediction
        with torch.no_grad():
            pred_df = pipeline.predict_df(
                context_df,
                prediction_length=prediction_days,
                id_column="id",
                timestamp_column="timestamp",
                target="target",
                covariates=all_covariates, 
                quantile_levels=[0.1, 0.5, 0.9] 
            )

        required_cols = ['target_0.1', 'target_0.5', 'target_0.9']
        if pred_df.empty or not all(col in pred_df.columns for col in required_cols):
             missing = [col for col in required_cols if col not in pred_df.columns]
             raise RuntimeError(f"Prediction output incomplete. Missing: {missing}")

        q05_forecast = pred_df['target_0.5'].values.astype(np.float32)
        q09_forecast = pred_df['target_0.9'].values.astype(np.float32)
        q01_forecast = pred_df['target_0.1'].values.astype(np.float32)
        predicted_dates = pred_df['timestamp']
        
        last_price = data_original['Close'].iloc[-1]
        
        # Proyeksi Indikator Teknikal Masa Depan
        future_indicators = predict_technical_indicators_future(data_original, q05_forecast)
        
        predicted_high = float(np.max(q05_forecast))
        predicted_low = float(np.min(q05_forecast))
        predicted_mean = float(np.mean(q05_forecast))
        change_pct = ((predicted_mean - last_price) / last_price) * 100 if last_price != 0 else 0
        
        # Menambahkan data teknikal prediksi ke hasil
        return {
            'values': q05_forecast, 
            'dates': predicted_dates, 
            'high_30d': predicted_high, 
            'low_30d': predicted_low, 
            'mean_30d': predicted_mean, 
            'change_pct': change_pct, 
            'q01': q01_forecast,
            'q09': q09_forecast,
            'future_macd': future_indicators.get('MACD_Future', np.array([])),
            'future_macd_signal': future_indicators.get('MACD_Signal_Future', np.array([])),
            'future_bb_upper': future_indicators.get('BB_Upper_Future', np.array([])),
            'future_bb_lower': future_indicators.get('BB_Lower_Future', np.array([])),
            'summary': f"AI Model: Amazon Chronos-2 (Enhanced Covariates: {len(all_covariates)} features)\nExpected High: {predicted_high:.2f}\nExpected Low: {predicted_low:.2f}\nExpected Change: {change_pct:.2f}%"
        }
        
    except Exception as e:
        error_message = f'Model prediction failed: {e}'
        print(f"Error in prediction: {e}")
        empty_result['summary'] = error_message
        return empty_result

# Memperbarui fungsi create_prediction_chart untuk menampilkan Quantile Bands (q01, q09) dan Future BB
def create_prediction_chart(data, predictions):
    # Cek yang lebih aman untuk array kosong
    if not predictions['values'].size or not predictions['q01'].size:
        return go.Figure().update_layout(title="Prediction Failed: No Data Available")
        
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, 
                        row_heights=[0.7, 0.3], subplot_titles=('Price Forecast & Confidence Band', 'MACD Forecast'))

    # 1. Price Forecast (Row 1)
    fig.add_trace(go.Scatter(x=data.index, y=data['Close'].values, name='Historical Price', line=dict(color='blue', width=2)), row=1, col=1)

    # Upper/Lower Quantile Band (Confidence)
    fig.add_trace(go.Scatter(x=predictions['dates'], y=predictions['q09'], name='90% Upper Bound (Q0.9)', line=dict(color='lightcoral', width=0)), row=1, col=1)
    
    fig.add_trace(go.Scatter(
        x=predictions['dates'], y=predictions['q01'], name='90% Confidence Band', 
        line=dict(color='lightcoral', width=0), fill='tonexty', fillcolor='rgba(255,182,193,0.3)'
    ), row=1, col=1)
    
    fig.add_trace(go.Scatter(x=predictions['dates'], y=predictions['values'], name='Median Forecast (Q0.5)', line=dict(color='red', width=3, dash='solid')), row=1, col=1)

    # Future Bollinger Bands 
    if predictions['future_bb_upper'].size == predictions['dates'].size:
        fig.add_trace(go.Scatter(x=predictions['dates'], y=predictions['future_bb_upper'], name='BB Upper (Future)', line=dict(color='green', width=1, dash='dot')), row=1, col=1)
        fig.add_trace(go.Scatter(x=predictions['dates'], y=predictions['future_bb_lower'], name='BB Lower (Future)', line=dict(color='green', width=1, dash='dot')), row=1, col=1)

    last_hist_date = data.index[-1]
    last_hist_price = data['Close'].iloc[-1]
    fig.add_trace(go.Scatter(x=[last_hist_date], y=[last_hist_price], mode='markers', marker=dict(size=10, color='blue', symbol='circle'), name='Last Known Price'), row=1, col=1)

    # 2. MACD Forecast (Row 2)
    if predictions['future_macd'].size == predictions['dates'].size:
        
        # Perluas data historis MACD untuk charting yang lebih baik
        lookback_period = 60
        macd_hist = data['Close'].ewm(span=12).mean() - data['Close'].ewm(span=26).mean()
        macd_signal_hist = macd_hist.ewm(span=9).mean()
        
        macd_full = np.concatenate([macd_hist.iloc[-lookback_period:].values, predictions['future_macd']])
        macd_signal_full = np.concatenate([macd_signal_hist.iloc[-lookback_period:].values, predictions['future_macd_signal']])
        macd_dates_full = pd.to_datetime(np.concatenate([data.index[-lookback_period:].values, predictions['dates']]))
        
        fig.add_trace(go.Scatter(x=macd_dates_full, y=macd_full, name='MACD Line', line=dict(color='blue', width=2)), row=2, col=1)
        fig.add_trace(go.Scatter(x=macd_dates_full, y=macd_signal_full, name='Signal Line', line=dict(color='red', width=1)), row=2, col=1)
        
        fig.add_vline(x=data.index[-1], line_width=1, line_dash="dash", line_color="gray", row=2, col=1)
        fig.add_vline(x=data.index[-1], line_width=1, line_dash="dash", line_color="gray", row=1, col=1)

    fig.update_layout(
        title=f'Advanced Price & Technical Forecast - Next {len(predictions["dates"])} Days (Chronos-2)', 
        xaxis_title='Date', yaxis_title='Price (IDR)', hovermode='x unified', height=900,
        legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01)
    )
    
    fig.update_yaxes(title_text="Price (IDR)", row=1, col=1)
    fig.update_yaxes(title_text="MACD Value", row=2, col=1)

    return fig

# ... (Fungsi-fungsi lama lainnya seperti get_indonesian_stocks, calculate_technical_indicators, dll. tetap sama)
def get_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):
    indicators = {}
    
    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
    rsi_series = calculate_rsi(data['Close'])
    indicators['rsi'] = {'current': rsi_series.iloc[-1], 'values': rsi_series}
    
    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', 'macd_values': macd, 'signal_values': signal_line}
    
    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],
        'upper_values': upper, 'middle_values': middle, 'lower_values': lower,
        'position': 'UPPER' if bb_position > 0.8 else 'LOWER' if bb_position < 0.2 else 'MIDDLE'
    }
    sma_20_series = data['Close'].rolling(20).mean()
    sma_50_series = data['Close'].rolling(50).mean()
    indicators['moving_averages'] = {'sma_20': sma_20_series.iloc[-1], 'sma_50': sma_50_series.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], 'sma_20_values': sma_20_series, 'sma_50_values': sma_50_series}
    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]}
    
    # Tambahkan kolom indikator ke DataFrame input untuk digunakan nanti (di predict_technical_indicators_future)
    data['RSI'] = rsi_series
    data['MACD'] = macd
    data['MACD_Signal'] = signal_line
    
    return indicators

def generate_trading_signals(data, indicators):
    signals = {}
    current_price = data['Close'].iloc[-1]
    buy_signals = 0
    sell_signals = 0
    signal_details = []
    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_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")
    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")
    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_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)")
    total_signals = buy_signals + sell_signals
    signal_strength = (buy_signals / max(total_signals, 1)) * 100
    overall_signal = "BUY" if buy_signals > sell_signals else "SELL" if sell_signals > buy_signals else "HOLD"
    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):
    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')}\nIndustry: {info.get('industry', 'N/A')}\nMarket Cap: {info.get('marketCap', 0)}\n52 Week High: {info.get('fiftyTwoWeekHigh', 'N/A')}\n52 Week Low: {info.get('fiftyTwoWeekLow', 'N/A')}\nBeta: {info.get('beta', 'N/A')}\nEPS: {info.get('forwardEps', 'N/A')}\nBook Value: {info.get('bookValue', 'N/A')}\nPrice to Book: {info.get('priceToBook', 'N/A')}"}
        return fundamental_info
    except:
        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):
    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}"

def create_price_chart(data, indicators):
    fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.05)
    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)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['moving_averages']['sma_20_values'], name='SMA 20', line=dict(color='orange')), row=1, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['moving_averages']['sma_50_values'], name='SMA 50', line=dict(color='blue')), row=1, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['rsi']['values'], name='RSI', line=dict(color='purple')), row=2, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['macd']['macd_values'], name='MACD', line=dict(color='blue')), row=3, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['macd']['signal_values'], name='Signal', line=dict(color='red')), row=3, col=1)
    fig.update_layout(title='Technical Analysis Dashboard', height=900, showlegend=True)
    return fig

def create_technical_chart(data, indicators):
    fig = make_subplots(rows=2, cols=2, subplot_titles=('Bollinger Bands', 'Volume', 'Price vs MA', 'RSI Analysis'))
    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Price', line=dict(color='black')), row=1, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['bollinger']['upper_values'], name='Upper Band', line=dict(color='red')), row=1, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['bollinger']['lower_values'], name='Lower Band', line=dict(color='green'), fill='tonexty', fillcolor='rgba(0,255,0,0.1)'), row=1, col=1)
    fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume', marker_color='lightblue'), row=1, col=2)
    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Price', line=dict(color='gray')), row=2, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['moving_averages']['sma_20_values'], name='SMA 20', line=dict(color='orange', dash='dash')), row=2, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['moving_averages']['sma_50_values'], name='SMA 50', line=dict(color='blue', dash='dash')), row=2, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=indicators['rsi']['values'], name='RSI', line=dict(color='purple')), row=2, col=2)
    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=2)
    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=2)
    fig.update_layout(title='Technical Indicators Overview', height=800, showlegend=False, hovermode='x unified')
    return fig