Update app.py

app.py

@@ -2,111 +2,100 @@ import gradio as gr
 import pandas as pd
 import yfinance as yf
 import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 from statsforecast import StatsForecast
 from statsforecast.models import AutoARIMA
-import nltk
-from nltk.sentiment.vader import SentimentIntensityAnalyzer
 
-# Initialize Sentiment Engine
-try:
-    nltk.data.find('vader_lexicon')
-except LookupError:
-    nltk.download('vader_lexicon')
-sia = SentimentIntensityAnalyzer()
-
-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"
+def calculate_indicators(df):
+    # RSI Calculation
+    delta = df['Close'].diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    df['RSI'] = 100 - (100 / (1 + rs))
     
-    # 2. News Sentiment
-    stock = yf.Ticker(ticker)
-    news = stock.news[:3]
-    scores = []
-    headlines_html = ""
-    for article in news:
-        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>"
+    # MACD Calculation
+    df['EMA12'] = df['Close'].ewm(span=12, adjust=False).mean()
+    df['EMA26'] = df['Close'].ewm(span=26, adjust=False).mean()
+    df['MACD'] = df['EMA12'] - df['EMA26']
+    df['Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
+    return df
+
+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"))
+
+    # 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)
+
+    # 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)
     
-    avg_sent = sum(scores)/len(scores) if scores else 0
-    return surge_alert, avg_sent, headlines_html
+    fig.update_layout(template='plotly_dark', height=800, showlegend=False, paper_bgcolor='#131722', plot_bgcolor='#131722')
+    return fig
 
-def analyze_full(ticker, horizon):
+def analyze(ticker, horizon):
     try:
-        # Data Acquisition
-        df = yf.download(ticker, period="1y", multi_level_index=False)
-        if df.empty: return None, "⚠️ Symbol Error"
+        # Step 1: Data + Analyst Targets
+        stock = yf.Ticker(ticker)
+        df = stock.history(period="1y")
+        if df.empty: return None, "Symbol Error", ""
+        
+        info = stock.info
+        target_high = info.get('targetHighPrice', 'N/A')
+        current_price = df['Close'].iloc[-1]
         
-        # 1. Forecasting with StatsForecast
+        # Step 2: Accuracy Boost via Technicals
+        df = calculate_indicators(df)
+        
+        # Step 3: Fast StatsForecast
         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)
         forecast = sf.predict(h=horizon, level=[80])
         
-        # 2. Extract Analytics
-        surge_text, sent_score, news_html = get_pro_analytics(df, ticker)
+        # 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"
         
-        # 3. Confidence Calculation
-        math_move = ((forecast['AutoARIMA'].iloc[-1] - df['Close'].iloc[-1]) / df['Close'].iloc[-1]) * 100
-        # 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)))
-        
-        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"
-        surge_color = "#FFD700" if "SURGE" in surge_text else "#787b86"
-        
-        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}
+        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>
         """
-        return fig, summary
-
+        
+        return get_pro_chart(df, forecast, ticker), signal_html
     except Exception as e:
-        return None, f"<div style='color:red;'>Error: {str(e)}</div>"
+        return None, f"Error: {str(e)}"
 
-# --- 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>")
-    
+# --- UI Layout ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🏛️ INSTITUTIONAL QUANT TERMINAL")
     with gr.Row():
         with gr.Column(scale=1):
-            ticker = gr.Textbox(label="Market Symbol", value="TSLA")
-            horizon = gr.Slider(7, 90, value=30, label="Days to Forecast")
-            btn = gr.Button("EXECUTE QUANT STRATEGY", variant="primary")
-            output_panel = gr.HTML(label="Intelligence Summary")
-        with gr.Column(scale=3):
-            plot = gr.Plot()
+            t_in = gr.Textbox(label="Ticker Symbol", value="NVDA")
+            h_in = gr.Slider(7, 90, value=30, label="Forecast Days")
+            btn = gr.Button("RUN ANALYSIS", variant="primary")
+            info_out = gr.HTML()
+        with gr.Column(scale=4):
+            plot_out = gr.Plot()
 
-    btn.click(analyze_full, [ticker, horizon], [plot, output_panel])
+    btn.click(analyze, [t_in, h_in], [plot_out, info_out])
 
 demo.launch()