Update app.py

app.py

@@ -11,25 +11,21 @@ import io
 import base64
 import plotly.graph_objects as go
 
-# Global instances
-data_processor = DataProcessor()
-sentiment_analyzer = SentimentAnalyzer()
-model_handler = ModelHandler()
-trading_logic = TradingLogic()
-
-# Asset mapping
 asset_map = {
     "Gold Futures (GC=F)": "GC=F",
     "Bitcoin USD (BTC-USD)": "BTC-USD"
 }
 
+data_processor = DataProcessor()
+sentiment_analyzer = SentimentAnalyzer()
+model_handler = ModelHandler()
+trading_logic = TradingLogic()
+
 def create_chart_analysis(interval, asset_name):
-    """Create chart with technical indicators using mplfinance"""
     try:
         ticker = asset_map[asset_name]
         df = data_processor.get_asset_data(ticker, interval)
         if df.empty:
-            # Return error plot instead of string
             fig, ax = plt.subplots(figsize=(12, 8), facecolor='white')
             fig.patch.set_facecolor('white')
             ax.text(0.5, 0.5, f'No data available for {asset_name}\nPlease try a different interval', 
@@ -40,45 +36,35 @@ def create_chart_analysis(interval, asset_name):
             pred_fig.patch.set_facecolor('white')
             return fig, {}, pred_fig
         
-        # Calculate indicators
         df = data_processor.calculate_indicators(df)
         
-        # Create main candlestick chart with mplfinance
-        # Prepare additional plots for indicators
         ap = []
         
-        # Add moving averages (last 100 data points)
         if 'SMA_20' in df.columns:
             ap.append(mpf.make_addplot(df['SMA_20'].iloc[-100:], color='#FFA500', width=1.5, label='SMA 20'))
         if 'SMA_50' in df.columns:
             ap.append(mpf.make_addplot(df['SMA_50'].iloc[-100:], color='#FF4500', width=1.5, label='SMA 50'))
         
-        # Add Bollinger Bands
         if 'BB_upper' in df.columns and 'BB_lower' in df.columns:
             ap.append(mpf.make_addplot(df['BB_upper'].iloc[-100:], color='#4169E1', width=1, linestyle='dashed', label='BB Upper'))
             ap.append(mpf.make_addplot(df['BB_lower'].iloc[-100:], color='#4169E1', width=1, linestyle='dashed', label='BB Lower'))
         
-        # Create figure
         try:
             fig, axes = mpf.plot(
-                df[-100:],  # Show last 100 candles
+                df[-100:],
                 type='candle',
                 style='yahoo',
                 title=f'{asset_name} - {interval}',
                 ylabel='Price (USD)',
                 volume=True,
                 addplot=ap,
-                figsize=(15, 9), # DITINGKATKAN
+                figsize=(15, 9),
                 returnfig=True,
                 warn_too_much_data=200,
-                tight_layout=True
+                # MENGGUNAKAN scale_padding UNTUK MEMBERI RUANG PADA SUMBU Y KANAN
+                scale_padding={'right': 1.0, 'left': 0.1}
             )
             
-            # Perbaikan untuk mencegah pemotongan label sumbu Y di Gradio
-            if fig:
-                plt.subplots_adjust(right=0.95) # Tambahkan margin kanan
-            
-            # Adjust layout
             fig.patch.set_facecolor('white')
             if axes:
                 axes[0].set_facecolor('white')
@@ -92,24 +78,19 @@ def create_chart_analysis(interval, asset_name):
             axes.set_title('Plot Generation Error', color='black')
             axes.axis('off')
         
-        # Prepare data for Chronos
         prepared_data = data_processor.prepare_for_chronos(df)
         
-        # Generate predictions
         predictions = model_handler.predict(prepared_data, horizon=10)
         current_price = df['Close'].iloc[-1]
         
-        # Get signal
         signal, confidence = trading_logic.generate_signal(
             predictions, current_price, df
         )
         
-        # Calculate TP/SL
         tp, sl = trading_logic.calculate_tp_sl(
             current_price, df['ATR'].iloc[-1] if 'ATR' in df.columns else 10, signal
         )
         
-        # Create metrics display
         metrics = {
             "Current Price": f"${current_price:,.2f}",
             "Signal": signal.upper(),
@@ -121,16 +102,13 @@ def create_chart_analysis(interval, asset_name):
             "Volume": f"{df['Volume'].iloc[-1]:,.0f}" if 'Volume' in df.columns else "N/A"
         }
         
-        # Create prediction chart using matplotlib
         pred_fig, ax = plt.subplots(figsize=(10, 4), facecolor='white')
         pred_fig.patch.set_facecolor('white')
         
-        # Plot historical prices (last 30 points)
         hist_data = df['Close'].iloc[-30:]
         hist_dates = df.index[-30:]
         ax.plot(hist_dates, hist_data, color='#4169E1', linewidth=2, label='Historical')
         
-        # Plot predictions
         if predictions.any() and len(predictions) > 0:
             future_dates = pd.date_range(
                 start=df.index[-1], periods=len(predictions), freq='D'
@@ -138,7 +116,6 @@ def create_chart_analysis(interval, asset_name):
             ax.plot(future_dates, predictions, color='#FF6600', linewidth=2, 
                    marker='o', markersize=4, label='Predictions')
             
-            # Connect historical to prediction
             ax.plot([hist_dates[-1], future_dates[0]], 
                    [hist_data.iloc[-1], predictions[0]], 
                    color='#FF6600', linewidth=1, linestyle='--')
@@ -153,7 +130,6 @@ def create_chart_analysis(interval, asset_name):
         return fig, metrics, pred_fig
         
     except Exception as e:
-        # Return error plot instead of string
         fig, ax = plt.subplots(figsize=(12, 8), facecolor='white')
         fig.patch.set_facecolor('white')
         ax.text(0.5, 0.5, f'Error: {str(e)}', ha='center', va='center', 
@@ -166,14 +142,10 @@ def create_chart_analysis(interval, asset_name):
         return fig, {}, pred_fig
 
 def analyze_sentiment(asset_name):
-    """Analyze market sentiment for selected asset"""
     try:
         ticker = asset_map[asset_name]
-        # FIX: Menggunakan fungsi yang benar dari sentiment_analyzer.py
         sentiment_score, news_summary = sentiment_analyzer.analyze_market_sentiment(ticker)
         
-        # --- Implementasi Plotly Gauge (sesuai referensi pengguna) ---
-        
         fig = go.Figure(go.Indicator(
             mode="gauge+number+delta",
             value=sentiment_score,
@@ -184,9 +156,9 @@ def analyze_sentiment(asset_name):
                 'axis': {'range': [-1, 1]},
                 'bar': {'color': "#FFD700"},
                 'steps': [
-                    {'range': [-1, -0.5], 'color': "rgba(255,0,0,0.5)"}, # Merah (Bearish)
-                    {'range': [-0.5, 0.5], 'color': "rgba(100,100,100,0.3)"}, # Abu-abu (Neutral)
-                    {'range': [0.5, 1], 'color': "rgba(0,255,0,0.5)"} # Hijau (Bullish)
+                    {'range': [-1, -0.5], 'color': "rgba(255,0,0,0.5)"},
+                    {'range': [-0.5, 0.5], 'color': "rgba(100,100,100,0.3)"},
+                    {'range': [0.5, 1], 'color': "rgba(0,255,0,0.5)"}
                 ],
                 'threshold': {
                     'line': {'color': "black", 'width': 4},
@@ -207,7 +179,6 @@ def analyze_sentiment(asset_name):
         return fig, news_summary
         
     except Exception as e:
-        # Return error plot (menggunakan Matplotlib untuk error fallback)
         fig, ax = plt.subplots(figsize=(6, 4), facecolor='white')
         fig.patch.set_facecolor('white')
         ax.text(0.5, 0.5, f'Sentiment Error: {str(e)}', ha='center', va='center', 
@@ -216,25 +187,21 @@ def analyze_sentiment(asset_name):
         return fig, f"<p>Error analyzing sentiment: {str(e)}</p>"
 
 def get_fundamentals(asset_name):
-    """Get fundamental analysis data"""
     try:
         ticker = asset_map[asset_name]
         fundamentals = data_processor.get_fundamental_data(ticker)
         
-        # Create fundamentals table
         table_data = []
         for key, value in fundamentals.items():
             table_data.append([key, value])
         
         df = pd.DataFrame(table_data, columns=['Metric', 'Value'])
         
-        # Create fundamentals gauge chart
         fig, ax = plt.subplots(figsize=(6, 4), facecolor='white')
         fig.patch.set_facecolor('white')
         
         strength_index = fundamentals.get('Strength Index', 50)
         
-        # Create horizontal bar gauge
         ax.barh([0], [strength_index], height=0.3, color='gold', alpha=0.7)
         ax.set_xlim(0, 100)
         ax.set_ylim(-0.5, 0.5)
@@ -248,7 +215,6 @@ def get_fundamentals(asset_name):
         return fig, df
         
     except Exception as e:
-        # Return error plot
         fig, ax = plt.subplots(figsize=(6, 4), facecolor='white')
         fig.patch.set_facecolor('white')
         ax.text(0.5, 0.5, f'Fundamentals Error: {str(e)}', ha='center', va='center', 
@@ -256,7 +222,6 @@ def get_fundamentals(asset_name):
         ax.axis('off')
         return fig, pd.DataFrame()
 
-# Create Gradio interface
 with gr.Blocks(
     theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue"),
     title="Trading Analysis & Prediction",
@@ -274,7 +239,6 @@ with gr.Blocks(
     """
 ) as demo:
     
-    # Header with anycoder link
     gr.HTML("""
         <div style="text-align: center; padding: 20px;">
             <h1 style="color: #4169E1;">Trading Analysis & Prediction</h1>
@@ -306,14 +270,11 @@ with gr.Blocks(
     with gr.Tabs():
         with gr.TabItem("Chart Analysis"):
             
-            # Price Chart (Full Width)
             chart_plot = gr.Plot(label="Price Chart")
             
-            # Price Predictions (Full Width)
             with gr.Row():
                 pred_plot = gr.Plot(label="Price Predictions")
             
-            # Trading Metrics (Full Width, dipindahkan ke bawah)
             with gr.Row():
                 metrics_output = gr.JSON(label="Trading Metrics")
         
@@ -333,7 +294,6 @@ with gr.Blocks(
                         interactive=False
                     )
     
-    # Event handlers
     def update_all(interval, asset):
         chart, metrics, pred = create_chart_analysis(interval, asset)
         sentiment, news = analyze_sentiment(asset)