Update app.py

app.py

@@ -25,77 +25,107 @@ def get_pro_chart(df, forecast_df, ticker):
     # Professional Subplots: Price + Indicators
     fig = make_subplots(rows=3, cols=1, shared_xaxes=True, 
                         vertical_spacing=0.05, row_heights=[0.5, 0.25, 0.25],
-                        subplot_titles=(f"{ticker} Forecast", "RSI Momentum", "MACD Trend"))
+                        subplot_titles=(f"{ticker} Forecast & Confidence Zone", "RSI Momentum", "MACD Trend"))
 
-    # 1. Price + Forecast
-    fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Price', line=dict(color='#2962FF')), row=1, col=1)
-    fig.add_trace(go.Scatter(x=forecast_df['ds'], y=forecast_df['AutoARIMA'], name='AI Forecast', line=dict(color='#F23645')), row=1, col=1)
+    # --- 1. Price + Forecast + CONFIDENCE ZONE ---
+    # Shaded Confidence Area (AutoARIMA-hi-80 and AutoARIMA-lo-80)
+    fig.add_trace(go.Scatter(
+        x=forecast_df['ds'].tolist() + forecast_df['ds'].tolist()[::-1],
+        y=forecast_df['AutoARIMA-hi-80'].tolist() + forecast_df['AutoARIMA-lo-80'].tolist()[::-1],
+        fill='toself',
+        fillcolor='rgba(0, 210, 255, 0.2)',
+        line=dict(color='rgba(255,255,255,0)'),
+        hoverinfo="skip",
+        showlegend=True,
+        name='80% Confidence'
+    ), row=1, col=1)
 
-    # 2. RSI
+    # Historical Price
+    fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Historical Price', line=dict(color='#00d2ff', width=2)), row=1, col=1)
+    
+    # AI Mean Forecast Line
+    fig.add_trace(go.Scatter(x=forecast_df['ds'], y=forecast_df['AutoARIMA'], name='AI Target', line=dict(color='#F23645', dash='dot')), row=1, col=1)
+
+    # --- 2. RSI ---
     fig.add_trace(go.Scatter(x=df.index, y=df['RSI'], name='RSI', line=dict(color='#FF9800')), row=2, col=1)
     fig.add_hline(y=70, line_dash="dot", line_color="red", row=2, col=1)
     fig.add_hline(y=30, line_dash="dot", line_color="green", row=2, col=1)
 
-    # 3. MACD
-    fig.add_trace(go.Bar(x=df.index, y=df['MACD'] - df['Signal'], name='Momentum'), row=3, col=1)
+    # --- 3. MACD ---
+    fig.add_trace(go.Bar(x=df.index, y=df['MACD'] - df['Signal'], name='Momentum', marker_color='#9d50bb'), row=3, col=1)
+    
+    # Styling to match your Aether UI
+    fig.update_layout(
+        template='plotly_dark', 
+        height=800, 
+        showlegend=True, 
+        paper_bgcolor='rgba(0,0,0,0)', # Transparent to show your terminal background
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(family="Lato", size=12)
+    )
+    fig.update_xaxes(showgrid=False, zeroline=False)
+    fig.update_yaxes(showgrid=False, zeroline=False)
     
-    fig.update_layout(template='plotly_dark', height=800, showlegend=False, paper_bgcolor='#131722', plot_bgcolor='#131722')
     return fig
 
 def analyze(ticker, horizon):
     try:
-        # Step 1: Data + Analyst Targets
+        # Step 1: Data
         stock = yf.Ticker(ticker)
         df = stock.history(period="1y")
-        if df.empty: return None, "Symbol Error", ""
+        if df.empty: return None, "Symbol Error"
         
         info = stock.info
         target_high = info.get('targetHighPrice', 'N/A')
         current_price = df['Close'].iloc[-1]
         
-        # Step 2: Accuracy Boost via Technicals
+        # Step 2: Indicators
         df = calculate_indicators(df)
         
-        # Step 3: Fast StatsForecast
+        # Step 3: StatsForecast with Levels
         data = df.reset_index()[['Date', 'Close']]
         data.columns = ['ds', 'y']
         data['unique_id'] = ticker
         
-        # Season length 5 = Weekly trading cycle
         sf = StatsForecast(models=[AutoARIMA(season_length=5)], freq='B')
         sf.fit(data)
+        
+        # We request 80% level to get the 'lo-80' and 'hi-80' columns
         forecast = sf.predict(h=horizon, level=[80])
+        forecast = forecast.reset_index()
         
         # Step 4: Signal Analysis
         rsi_val = df['RSI'].iloc[-1]
-        rsi_stat = "OVERSOLD (BUY)" if rsi_val < 30 else "OVERBOUGHT (SELL)" if rsi_val > 70 else "NEUTRAL"
+        rsi_stat = "OVERSOLD" if rsi_val < 30 else "OVERBOUGHT" if rsi_val > 70 else "NEUTRAL"
         
         signal_html = f"""
-        <div style='background: #1e222d; padding: 15px; border-radius: 10px; color: white;'>
-            <p>Current Price: <b>${current_price:.2f}</b></p>
-            <p>Wall St Target: <b style='color:#00ff88;'>${target_high}</b></p>
-            <hr>
-            <p>RSI (14d): <b>{rsi_val:.1f} ({rsi_stat})</b></p>
-            <p>AI 30d Target: <b>${forecast['AutoARIMA'].iloc[-1]:.2f}</b></p>
+        <div style='background: rgba(255,255,255,0.05); padding: 20px; border-radius: 15px; border: 1px solid rgba(255,255,255,0.1); font-family: "Lato", sans-serif;'>
+            <p style='margin:0; color:#888; font-size:12px;'>CURRENT VALUE</p>
+            <h2 style='margin:0; color:#00d2ff;'>${current_price:.2f}</h2>
+            <hr style='border:0; border-top:1px solid #333; margin:15px 0;'>
+            <p>RSI Analysis: <b style='color:#FF9800;'>{rsi_stat} ({rsi_val:.1f})</b></p>
+            <p>AI Forecast ({horizon}d): <b style='color:#F23645;'>${forecast['AutoARIMA'].iloc[-1]:.2f}</b></p>
+            <p style='font-size:11px; color:#555;'>Confidence Zone: ${forecast['AutoARIMA-lo-80'].iloc[-1]:.2f} - ${forecast['AutoARIMA-hi-80'].iloc[-1]:.2f}</p>
         </div>
         """
         
         return get_pro_chart(df, forecast, ticker), signal_html
     except Exception as e:
-        return None, f"Error: {str(e)}"
+        return None, f"<p style='color:red;'>Error: {str(e)}</p>"
 
 # --- UI Layout ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🏛️ INSTITUTIONAL QUANT TERMINAL")
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="slate")) as demo:
+    gr.Markdown("<h1 style='text-align: center; letter-spacing: 5px;'>AETHER BACKEND ENGINE</h1>")
     with gr.Row():
         with gr.Column(scale=1):
-            t_in = gr.Textbox(label="Ticker Symbol", value="NVDA")
+            t_in = gr.Textbox(label="Ticker", value="NVDA")
             h_in = gr.Slider(7, 90, value=30, label="Forecast Days")
-            btn = gr.Button("RUN ANALYSIS", variant="primary")
+            btn = gr.Button("UPDATE ENGINE", variant="primary")
             info_out = gr.HTML()
         with gr.Column(scale=4):
             plot_out = gr.Plot()
 
     btn.click(analyze, [t_in, h_in], [plot_out, info_out])
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()