update config

config.py

@@ -1,69 +1,511 @@
-# Indonesian Stock Exchange (IDX) major stocks
-IDX_STOCKS = {
-    "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"
-}
+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
+import spaces
 
-# Technical indicators configuration
-TECHNICAL_INDICATORS = {
-    'rsi': {
-        'period': 14,
-        'oversold': 30,
-        'overbought': 70
-    },
-    'macd': {
-        'fast': 12,
-        'slow': 26,
-        'signal': 9
-    },
-    'bollinger': {
-        'period': 20,
-        'std_dev': 2
-    },
-    'moving_averages': {
-        'sma_short': 20,
-        'sma_medium': 50,
-        'sma_long': 200,
-        'ema_short': 12,
-        'ema_long': 26
+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"
     }
-}
 
-# Prediction model configuration
-PREDICTION_CONFIG = {
-    'model_name': 'amazon/chronos-bolt-base',
-    'context_length': 512,
-    'prediction_length': 30,
-    'temperature': 1.0,
-    'top_k': 50,
-    'top_p': 0.9
-}
+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
 
-# Chart styling
-CHART_CONFIG = {
-    'template': 'plotly_white',
-    'color_scheme': {
-        'bullish': '#10b981',
-        'bearish': '#ef4444',
-        'neutral': '#6b7280',
-        'accent': '#3b82f6'
+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