Delete app.py

app.py

@@ -1,330 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import mplfinance as mpf
-from data_processor import DataProcessor
-from sentiment_analyzer import SentimentAnalyzer
-from model_handler import ModelHandler
-from trading_logic import TradingLogic
-import io
-import base64
-import plotly.graph_objects as go
-
-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):
-    try:
-        ticker = asset_map[asset_name]
-        df = data_processor.get_asset_data(ticker, interval)
-        if df.empty:
-            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', 
-                   ha='center', va='center', transform=ax.transAxes, fontsize=14, color='red')
-            ax.set_title('Data Error', color='black')
-            ax.axis('off')
-            pred_fig = plt.figure(figsize=(10, 4), facecolor='white')
-            pred_fig.patch.set_facecolor('white')
-            return fig, {}, pred_fig
-        
-        df = data_processor.calculate_indicators(df)
-        
-        ap = []
-        
-        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'))
-        
-        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'))
-        
-        try:
-            fig, axes = mpf.plot(
-                df[-100:],
-                type='candle',
-                style='yahoo',
-                title=f'{asset_name} - {interval}',
-                ylabel='Price (USD)',
-                volume=True,
-                addplot=ap,
-                figsize=(15, 9),
-                returnfig=True,
-                warn_too_much_data=200,
-                # MENGGUNAKAN scale_padding UNTUK MEMBERI RUANG PADA SUMBU Y KANAN
-                scale_padding={'right': 1.0, 'left': 0.1}
-            )
-            
-            fig.patch.set_facecolor('white')
-            if axes:
-                axes[0].set_facecolor('white')
-                axes[0].grid(True, alpha=0.3)
-        except Exception as plot_error:
-            print(f"Mplfinance plot error: {plot_error}")
-            fig, axes = plt.subplots(figsize=(12, 8), facecolor='white')
-            fig.patch.set_facecolor('white')
-            axes.text(0.5, 0.5, f'Chart Plot Error: {str(plot_error)}', ha='center', va='center', 
-                     transform=axes.transAxes, fontsize=14, color='red')
-            axes.set_title('Plot Generation Error', color='black')
-            axes.axis('off')
-        
-        prepared_data = data_processor.prepare_for_chronos(df)
-        
-        predictions = model_handler.predict(prepared_data, horizon=10)
-        current_price = df['Close'].iloc[-1]
-        
-        signal, confidence = trading_logic.generate_signal(
-            predictions, current_price, df
-        )
-        
-        tp, sl = trading_logic.calculate_tp_sl(
-            current_price, df['ATR'].iloc[-1] if 'ATR' in df.columns else 10, signal
-        )
-        
-        metrics = {
-            "Current Price": f"${current_price:,.2f}",
-            "Signal": signal.upper(),
-            "Confidence": f"{confidence:.1%}",
-            "Take Profit": f"${tp:,.2f}" if tp else "N/A",
-            "Stop Loss": f"${sl:,.2f}" if sl else "N/A",
-            "RSI": f"{df['RSI'].iloc[-1]:.1f}" if 'RSI' in df.columns else "N/A",
-            "MACD": f"{df['MACD'].iloc[-1]:.4f}" if 'MACD' in df.columns else "N/A",
-            "Volume": f"{df['Volume'].iloc[-1]:,.0f}" if 'Volume' in df.columns else "N/A"
-        }
-        
-        pred_fig, ax = plt.subplots(figsize=(10, 4), facecolor='white')
-        pred_fig.patch.set_facecolor('white')
-        
-        hist_data = df['Close'].iloc[-30:]
-        hist_dates = df.index[-30:]
-        ax.plot(hist_dates, hist_data, color='#4169E1', linewidth=2, label='Historical')
-        
-        if predictions.any() and len(predictions) > 0:
-            future_dates = pd.date_range(
-                start=df.index[-1], periods=len(predictions), freq='D'
-            )
-            ax.plot(future_dates, predictions, color='#FF6600', linewidth=2, 
-                   marker='o', markersize=4, label='Predictions')
-            
-            ax.plot([hist_dates[-1], future_dates[0]], 
-                   [hist_data.iloc[-1], predictions[0]], 
-                   color='#FF6600', linewidth=1, linestyle='--')
-        
-        ax.set_title('Price Prediction (Next 10 Periods)', fontsize=12, color='black')
-        ax.set_xlabel('Date', color='black')
-        ax.set_ylabel('Price (USD)', color='black')
-        ax.legend()
-        ax.grid(True, alpha=0.3)
-        ax.tick_params(colors='black')
-        
-        return fig, metrics, pred_fig
-        
-    except Exception as e:
-        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', 
-               transform=ax.transAxes, fontsize=14, color='red')
-        ax.set_title('Chart Generation Error', color='black')
-        ax.axis('off')
-        
-        pred_fig = plt.figure(figsize=(10, 4), facecolor='white')
-        pred_fig.patch.set_facecolor('white')
-        return fig, {}, pred_fig
-
-def analyze_sentiment(asset_name):
-    try:
-        ticker = asset_map[asset_name]
-        sentiment_score, news_summary = sentiment_analyzer.analyze_market_sentiment(ticker)
-        
-        fig = go.Figure(go.Indicator(
-            mode="gauge+number+delta",
-            value=sentiment_score,
-            domain={'x': [0, 1], 'y': [0, 1]},
-            title={'text': f"{ticker} Market Sentiment (Simulated)"},
-            delta={'reference': 0},
-            gauge={
-                'axis': {'range': [-1, 1]},
-                'bar': {'color': "#FFD700"},
-                'steps': [
-                    {'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},
-                    'thickness': 0.75,
-                    'value': 0
-                }
-            }
-        ))
-        
-        fig.update_layout(
-            template='plotly_white',
-            height=300,
-            paper_bgcolor='white', 
-            plot_bgcolor='white',
-            font=dict(color='black')
-        )
-        
-        return fig, news_summary
-        
-    except Exception as e:
-        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', 
-               transform=ax.transAxes, fontsize=12, color='red')
-        ax.axis('off')
-        return fig, f"<p>Error analyzing sentiment: {str(e)}</p>"
-
-def get_fundamentals(asset_name):
-    try:
-        ticker = asset_map[asset_name]
-        fundamentals = data_processor.get_fundamental_data(ticker)
-        
-        table_data = []
-        for key, value in fundamentals.items():
-            table_data.append([key, value])
-        
-        df = pd.DataFrame(table_data, columns=['Metric', 'Value'])
-        
-        fig, ax = plt.subplots(figsize=(6, 4), facecolor='white')
-        fig.patch.set_facecolor('white')
-        
-        strength_index = fundamentals.get('Strength Index', 50)
-        
-        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)
-        ax.set_title(f'{asset_name} Strength Index', color='black')
-        ax.set_xlabel('Index Value', color='black')
-        ax.text(strength_index, 0, f'{strength_index:.1f}', 
-               ha='left', va='center', fontsize=12, color='black', weight='bold')
-        ax.grid(True, alpha=0.3)
-        ax.tick_params(colors='black')
-        
-        return fig, df
-        
-    except Exception as e:
-        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', 
-               transform=ax.transAxes, fontsize=12, color='red')
-        ax.axis('off')
-        return fig, pd.DataFrame()
-
-with gr.Blocks(
-    theme=gr.themes.Default(primary_hue="blue", secondary_hue="blue"),
-    title="Trading Analysis & Prediction",
-    css="""
-        .gradio-container {background-color: #FFFFFF !important; color: #000000 !important}
-        .gr-button-primary {background-color: #4169E1 !important; color: #FFFFFF !important}
-        .gr-button-secondary {border-color: #4169E1 !important; color: #4169E1 !important}
-        .gr-tab button {color: #000000 !important}
-        .gr-tab button.selected {background-color: #4169E1 !important; color: #FFFFFF !important}
-        .gr-highlighted {background-color: #F0F0F0 !important}
-        .anycoder-link {color: #4169E1 !important; text-decoration: none; font-weight: bold}
-        .gr-json {background-color: #FFFFFF !important; color: #000000 !important}
-        .gr-json label {color: #000000 !important}
-        .gr-textbox, .gr-dropdown, .gr-number {background-color: #FFFFFF !important; color: #000000 !important}
-    """
-) as demo:
-    
-    gr.HTML("""
-        <div style="text-align: center; padding: 20px;">
-            <h1 style="color: #4169E1;">Trading Analysis & Prediction</h1>
-            <p>Advanced AI-powered analysis for Gold and Bitcoin</p>
-            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" class="anycoder-link">Built with anycoder</a>
-        </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            asset_dropdown = gr.Dropdown(
-                choices=list(asset_map.keys()),
-                value="Gold Futures (GC=F)",
-                label="Select Asset",
-                info="Choose trading pair"
-            )
-        with gr.Column(scale=1):
-            interval_dropdown = gr.Dropdown(
-                choices=[
-                    "5m", "15m", "30m", "1h", "1d", "1wk", "1mo", "3mo"
-                ],
-                value="1d",
-                label="Time Interval",
-                info="Select analysis timeframe"
-            )
-        with gr.Column(scale=1):
-            refresh_btn = gr.Button("Refresh Data", variant="primary")
-    
-    with gr.Tabs():
-        with gr.TabItem("Chart Analysis"):
-            
-            chart_plot = gr.Plot(label="Price Chart")
-            
-            with gr.Row():
-                pred_plot = gr.Plot(label="Price Predictions")
-            
-            with gr.Row():
-                metrics_output = gr.JSON(label="Trading Metrics")
-        
-        with gr.TabItem("Sentiment Analysis"):
-            with gr.Row():
-                sentiment_gauge = gr.Plot(label="Sentiment Score")
-                news_display = gr.HTML(label="Market News")
-        
-        with gr.TabItem("Fundamentals"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    fundamentals_gauge = gr.Plot(label="Strength Index")
-                with gr.Column(scale=1):
-                    fundamentals_table = gr.Dataframe(
-                        headers=["Metric", "Value"],
-                        label="Key Fundamentals",
-                        interactive=False
-                    )
-    
-    def update_all(interval, asset):
-        chart, metrics, pred = create_chart_analysis(interval, asset)
-        sentiment, news = analyze_sentiment(asset)
-        fund_gauge, fund_table = get_fundamentals(asset)
-        
-        return chart, metrics, pred, sentiment, news, fund_gauge, fund_table
-    
-    refresh_btn.click(
-        fn=update_all,
-        inputs=[interval_dropdown, asset_dropdown],
-        outputs=[
-            chart_plot, metrics_output, pred_plot,
-            sentiment_gauge, news_display,
-            fundamentals_gauge, fundamentals_table
-        ]
-    )
-    
-    demo.load(
-        fn=update_all,
-        inputs=[interval_dropdown, asset_dropdown],
-        outputs=[
-            chart_plot, metrics_output, pred_plot,
-            sentiment_gauge, news_display,
-            fundamentals_gauge, fundamentals_table
-        ]
-    )
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_api=True
-    )