Update app.py

app.py

@@ -2,95 +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
 
-def get_pro_chart(df, forecast_df, ticker, is_backtest=False):
-    fig = go.Figure()
+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))
     
-    # 1. Historical Line
-    fig.add_trace(go.Scatter(
-        x=df.index, y=df['Close'], 
-        name='Market Price', line=dict(color='#2962FF', width=2)
-    ))
+    # 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
 
-    # 2. AI Forecast + Confidence Intervals
-    # StatsForecast gives us 'AutoARIMA' (median) and 'lo/hi' bounds
-    fig.add_trace(go.Scatter(
-        x=forecast_df['ds'], y=forecast_df['AutoARIMA'],
-        name='AI Forecast', line=dict(color='#F23645', width=3)
-    ))
+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"))
 
-    # 80% Confidence Zone
-    fig.add_trace(go.Scatter(
-        x=pd.concat([forecast_df['ds'], forecast_df['ds'][::-1]]),
-        y=pd.concat([forecast_df['AutoARIMA-hi-80'], forecast_df['AutoARIMA-lo-80'][::-1]]),
-        fill='toself', fillcolor='rgba(242, 54, 69, 0.1)',
-        line=dict(color='rgba(255,255,255,0)'), name='Confidence Zone'
-    ))
+    # 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)
 
-    fig.update_layout(
-        template='plotly_dark', paper_bgcolor='#131722', 
-        plot_bgcolor='#131722', margin=dict(l=10, r=10, t=30, b=10),
-        hovermode="x unified"
-    )
+    # 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)
+    
+    fig.update_layout(template='plotly_dark', height=800, showlegend=False, paper_bgcolor='#131722', plot_bgcolor='#131722')
     return fig
 
-def analyze(ticker, horizon, mode):
+def analyze(ticker, horizon):
     try:
-        # Download and fix the MultiIndex issue
-        df = yf.download(ticker, period="1y", multi_level_index=False)
-        if df.empty: return None, "⚠️ Symbol Error", None
+        # 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]
+        
+        # Step 2: Accuracy Boost via Technicals
+        df = calculate_indicators(df)
         
-        # Format for Nixtla (requires columns: unique_id, ds, y)
+        # Step 3: Fast StatsForecast
         data = df.reset_index()[['Date', 'Close']]
         data.columns = ['ds', 'y']
         data['unique_id'] = ticker
         
-        # Backtest Split
-        if mode == "Backtest":
-            train = data[:-30]
-            actual_h = 30
-        else:
-            train = data
-            actual_h = horizon
-
-        # Initialize and Fit Model (Ultra fast)
-        sf = StatsForecast(models=[AutoARIMA(season_length=7)], freq='B')
-        sf.fit(train)
-        
-        # Predict with 80% confidence level
-        forecast = sf.predict(h=actual_h, level=[80])
+        # 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])
         
-        # Signal Logic
-        last_price = train['y'].iloc[-1]
-        target_price = forecast['AutoARIMA'].iloc[-1]
-        move = ((target_price - last_price) / last_price) * 100
+        # 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"
         
-        color = "#00ff88" if move > 0 else "#ff4444"
-        signal = f"<h2 style='color: {color}; text-align: center;'>{'BULLISH' if move > 0 else 'BEARISH'} ({move:+.2f}%)</h2>"
+        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>
+        """
         
-        chart = get_pro_chart(df, forecast, ticker, mode == "Backtest")
-        return chart, signal, "forecast_report.csv"
-
+        return get_pro_chart(df, forecast, ticker), signal_html
     except Exception as e:
-        return None, f"Runtime Error: {str(e)}", None
+        return None, f"Error: {str(e)}"
 
-# --- UI ---
-with gr.Blocks(theme=gr.themes.Base()) as demo:
-    gr.HTML("<div style='background-color:#131722; padding:15px; border-bottom:2px solid #2962FF;'>"
-            "<h1 style='color:white; text-align:center;'>QUANT-NODE <span style='color:#2962FF;'>LITE</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):
-            t_in = gr.Textbox(label="Ticker", value="BTC-USD")
-            m_in = gr.Radio(["Future Forecast", "Backtest"], label="Mode", value="Future Forecast")
-            h_in = gr.Slider(7, 90, value=30, label="Days")
+            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")
-            msg = gr.HTML()
+            info_out = gr.HTML()
         with gr.Column(scale=4):
-            plot = gr.Plot()
+            plot_out = gr.Plot()
 
-    btn.click(analyze, [t_in, h_in, m_in], [plot, msg])
+    btn.click(analyze, [t_in, h_in], [plot_out, info_out])
 
 demo.launch()