update config

config.py

@@ -1,511 +1,69 @@
-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
+# 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"
+}
 
-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"
+# 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 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
+# 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 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
+# Chart styling
+CHART_CONFIG = {
+    'template': 'plotly_white',
+    'color_scheme': {
+        'bullish': '#10b981',
+        'bearish': '#ef4444',
+        'neutral': '#6b7280',
+        'accent': '#3b82f6'
     }
-    
-    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
+}