Update app.py

app.py

@@ -6,49 +6,42 @@ from statsforecast import StatsForecast
 from statsforecast.models import AutoARIMA
 import nltk
 from nltk.sentiment.vader import SentimentIntensityAnalyzer
-import requests
 
-# Setup AI Sentiment
+# Initialize Sentiment Engine
 try:
     nltk.data.find('vader_lexicon')
 except LookupError:
     nltk.download('vader_lexicon')
 sia = SentimentIntensityAnalyzer()
 
-def get_market_mood():
-    # Fetching the global 'Fear & Greed' sentiment
-    try:
-        # Note: In a real prod environment, you'd use a dedicated API key for CNN/FNG
-        # For this terminal, we simulate the current 2026 index reading
-        return 45, "Neutral" 
-    except:
-        return 50, "Unknown"
-
-def get_news_sentiment(ticker):
-    # Fetch latest headlines via yfinance
-    stock = yf.Ticker(ticker)
-    news = stock.news[:5] # Get top 5 stories
-    if not news: return 0, "No recent news found."
+def get_pro_analytics(df, ticker):
+    # 1. Volume Surge Alert (Compares current volume to 20-day average)
+    avg_vol = df['Volume'].tail(20).mean()
+    curr_vol = df['Volume'].iloc[-1]
+    surge_ratio = curr_vol / avg_vol
+    surge_alert = f"⚡ VOLUME SURGE: {surge_ratio:.1f}x" if surge_ratio > 2.0 else "Normal Volume"
     
+    # 2. News Sentiment
+    stock = yf.Ticker(ticker)
+    news = stock.news[:3]
     scores = []
-    headlines_text = ""
+    headlines_html = ""
     for article in news:
-        title = article['title']
-        score = sia.polarity_scores(title)['compound']
-        scores.append(score)
-        sentiment_label = "🟢" if score > 0.1 else "🔴" if score < -0.1 else "⚪"
-        headlines_text += f"{sentiment_label} {title}<br>"
+        s = sia.polarity_scores(article['title'])['compound']
+        scores.append(s)
+        icon = "🟢" if s > 0.05 else "🔴" if s < -0.05 else "⚪"
+        headlines_html += f"<div style='font-size:11px; margin-bottom:4px;'>{icon} {article['title'][:60]}...</div>"
     
-    avg_score = sum(scores) / len(scores)
-    return avg_score, headlines_text
+    avg_sent = sum(scores)/len(scores) if scores else 0
+    return surge_alert, avg_sent, headlines_html
 
-def analyze_pro(ticker, horizon):
+def analyze_full(ticker, horizon):
     try:
-        # 1. Market Data
+        # Data Acquisition
         df = yf.download(ticker, period="1y", multi_level_index=False)
-        if df.empty: return None, "⚠️ Symbol Error", ""
+        if df.empty: return None, "⚠️ Symbol Error"
         
-        # 2. AI Forecasting (Math)
+        # 1. Forecasting with StatsForecast
         data = df.reset_index()[['Date', 'Close']]
         data.columns = ['ds', 'y']
         data['unique_id'] = ticker
@@ -56,54 +49,64 @@ def analyze_pro(ticker, horizon):
         sf.fit(data)
         forecast = sf.predict(h=horizon, level=[80])
         
-        # 3. Sentiment Intelligence (Mood)
-        sent_score, headlines = get_news_sentiment(ticker)
-        mood_val, mood_label = get_market_mood()
+        # 2. Extract Analytics
+        surge_text, sent_score, news_html = get_pro_analytics(df, ticker)
         
-        # 4. Accuracy Correlation Logic
-        # If Math is Bullish but Sentiment is Negative, we lower the 'Confidence'
+        # 3. Confidence Calculation
         math_move = ((forecast['AutoARIMA'].iloc[-1] - df['Close'].iloc[-1]) / df['Close'].iloc[-1]) * 100
-        conf_score = 90 if (math_move > 0 and sent_score > 0) or (math_move < 0 and sent_score < 0) else 65
+        # Correlation check: Is news sentiment in the same direction as the math?
+        is_aligned = (math_move > 0 and sent_score > 0) or (math_move < 0 and sent_score < 0)
+        confidence = 85 if is_aligned else 60
+        
+        # 4. Charting with Confidence Zone
+        fig = go.Figure()
+        # History
+        fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Market History', line=dict(color='#2962FF', width=2)))
+        # Shaded Confidence Zone (80% Level)
+        fig.add_trace(go.Scatter(
+            x=pd.concat([forecast['ds'], forecast['ds'][::-1]]),
+            y=pd.concat([forecast['AutoARIMA-hi-80'], forecast['AutoARIMA-lo-80'][::-1]]),
+            fill='toself', fillcolor='rgba(242, 54, 69, 0.1)',
+            line=dict(color='rgba(255,255,255,0)'), name='AI Confidence Zone'
+        ))
+        # Median Forecast
+        fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['AutoARIMA'], name='AI Target', line=dict(color='#F23645', width=3)))
         
-        # UI Components
+        fig.update_layout(template='plotly_dark', paper_bgcolor='#131722', plot_bgcolor='#131722', 
+                          margin=dict(l=10, r=10, t=10, b=10), legend=dict(orientation="h", y=1.1))
+
+        # 5. UI Summary
         color = "#00ff88" if math_move > 0 else "#ff4444"
-        mood_color = "#FFD700" if mood_label == "Neutral" else "#ff4444"
+        surge_color = "#FFD700" if "SURGE" in surge_text else "#787b86"
         
-        summary_html = f"""
-        <div style='background: #1e222d; padding: 15px; border-radius: 10px; border-left: 5px solid {color};'>
-            <h3 style='margin:0; color:{color};'>PROJECTION: {'BULLISH' if math_move > 0 else 'BEARISH'}</h3>
-            <p style='font-size: 20px;'>Target: <b>${forecast['AutoARIMA'].iloc[-1]:.2f}</b> ({math_move:+.2f}%)</p>
-            <p>AI Confidence: <b>{conf_score}%</b></p>
-            <hr style='border: 0.5px solid #363c4e;'>
-            <p>🌍 Global Market Mood: <b style='color:{mood_color};'>{mood_label} ({mood_val}/100)</b></p>
-            <p style='font-size: 12px; color: #787b86;'>{headlines}</p>
+        summary = f"""
+        <div style='background: #1e222d; padding: 15px; border-radius: 8px; border-top: 4px solid {color};'>
+            <h3 style='margin:0; color:{color};'>{'BULLISH' if math_move > 0 else 'BEARISH'} ({math_move:+.1f}%)</h3>
+            <p style='color:{surge_color}; font-weight:bold; margin:5px 0;'>{surge_text}</p>
+            <p style='font-size: 13px;'>AI Confidence: <b>{confidence}%</b></p>
+            <hr style='border:0.1px solid #363c4e;'>
+            <p style='font-size: 12px; font-weight:bold; margin-bottom:5px;'>LIVE SENTIMENT FEED:</p>
+            {news_html}
         </div>
         """
-        
-        # Charting
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name='History', line=dict(color='#2962FF')))
-        fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['AutoARIMA'], name='AI Forecast', line=dict(color='#F23645')))
-        fig.update_layout(template='plotly_dark', paper_bgcolor='#131722', plot_bgcolor='#131722', margin=dict(l=0,r=0,t=0,b=0))
-        
-        return fig, summary_html
+        return fig, summary
 
     except Exception as e:
-        return None, f"<div style='color:red;'>Terminal Error: {str(e)}</div>"
+        return None, f"<div style='color:red;'>Error: {str(e)}</div>"
 
-# --- GRADIO INTERFACE ---
-with gr.Blocks(title="Quant-Node Pro 2026", theme=gr.themes.Default()) as demo:
-    gr.HTML("<div style='text-align:center; padding:20px; background:#131722;'><h1 style='color:#2962FF; margin:0;'>QUANT-NODE <span style='color:white;'>PRO</span></h1><p style='color:#787b86;'>Sentiment-Aware Forecasting Engine</p></div>")
+# --- UI INTERFACE ---
+with gr.Blocks(title="Quant-Node Pro Elite", theme=gr.themes.Default()) as demo:
+    gr.HTML("<div style='text-align:center; padding:15px; background:#131722;'><h1 style='color:#2962FF; margin:0;'>QUANT-NODE <span style='color:white;'>ELITE</span></h1></div>")
     
     with gr.Row():
         with gr.Column(scale=1):
-            ticker = gr.Textbox(label="Ticker Symbol", value="AAPL")
+            ticker = gr.Textbox(label="Market Symbol", value="TSLA")
             horizon = gr.Slider(7, 90, value=30, label="Days to Forecast")
-            btn = gr.Button("GENERATE INTELLIGENCE", variant="primary")
-            stats = gr.HTML()
+            btn = gr.Button("EXECUTE QUANT STRATEGY", variant="primary")
+            output_panel = gr.HTML(label="Intelligence Summary")
         with gr.Column(scale=3):
-            chart = gr.Plot()
+            plot = gr.Plot()
 
-    btn.click(analyze_pro, [ticker, horizon], [chart, stats])
+    btn.click(analyze_full, [ticker, horizon], [plot, output_panel])
 
 demo.launch()