Update app.py

app.py

@@ -9,13 +9,16 @@ from utils import (
     create_price_chart,
     create_technical_chart,
     create_prediction_chart,
+    # Import fungsi baru
+    calculate_advanced_risk_metrics 
 )
 import warnings
+import numpy as np # Tambahkan import numpy
 
 warnings.filterwarnings("ignore")
 
 
-def analyze_stock(symbol, history_period, prediction_days=30):
+def analyze_stock(symbol, prediction_days=30):
     try:
         if not symbol.strip():
             raise ValueError("Please enter a valid stock symbol.")
@@ -24,37 +27,59 @@ def analyze_stock(symbol, history_period, prediction_days=30):
             symbol = symbol.upper() + ".JK"
 
         stock = yf.Ticker(symbol)
-        data = stock.history(period=history_period, interval="1d")
+        # Menggunakan periode 1 tahun untuk metrik risiko yang lebih relevan
+        data = stock.history(period="1y", interval="1d") 
 
         if data.empty:
-            raise ValueError(f"No price data available for {history_period}.")
+            raise ValueError("No price data available for this stock.")
 
+        # Menghitung Indikator Teknikal (Ini juga akan mengisi kolom RSI/MACD ke 'data')
         indicators = calculate_technical_indicators(data)
         signals = generate_trading_signals(data, indicators)
         fundamental_info = get_fundamental_data(stock)
+        
+        # Menghitung Metrik Risiko Tingkat Lanjut
+        risk_metrics = calculate_advanced_risk_metrics(data.copy())
+
+        # Prediksi Chronos-2 dengan Covariates
         predictions = predict_prices(data, prediction_days=prediction_days)
 
         fig_price = create_price_chart(data, indicators)
         fig_technical = create_technical_chart(data, indicators)
         fig_prediction = create_prediction_chart(data, predictions)
 
-        # kalkulasi TP1, TP2, SL menggunakan median (Q0.5)
+        # kalkulasi TP1, TP2, SL yang diperbarui berdasarkan quantiles/range prediksi
         last_price = data['Close'].iloc[-1]
-        tp1 = last_price * (1 + (predictions.get("change_pct", 0) / 200))
-        tp2 = last_price * (1 + (predictions.get("change_pct", 0) / 100))
-        sl = last_price * 0.95
+        
+        # Target/SL yang lebih konservatif berdasarkan range prediksi Q0.1-Q0.9
+        q05 = predictions.get('values', [last_price])
+        q01 = predictions.get('q01', [last_price * 0.95])
+        q09 = predictions.get('q09', [last_price * 1.05])
+        
+        # TP1 (Target Konservatif): Midpoint antara harga terakhir dan median tertinggi
+        tp1 = (last_price + np.max(q05)) / 2
+        # TP2 (Target Agresif): Quantile 90% tertinggi
+        tp2 = np.max(q09)
+        # SL (Stop Loss): Quantile 10% terendah
+        sl = np.min(q01)
+        
+        # Pastikan TP1 < TP2 dan SL lebih rendah dari TP
+        if tp1 > tp2:
+            tp1, tp2 = tp2, tp1 
+        if sl > last_price:
+            sl = last_price * 0.95 # Fallback
 
         predictions["tp1"] = tp1
         predictions["tp2"] = tp2
         predictions["sl"] = sl
 
-        return fundamental_info, indicators, signals, fig_price, fig_technical, fig_prediction, predictions
+        # Menambahkan risk_metrics ke return
+        return fundamental_info, indicators, signals, risk_metrics, fig_price, fig_technical, fig_prediction, predictions
 
     except Exception as e:
         print(f"Error analyzing {symbol}: {e}")
         empty_fig = gr.Plot.update(value=None)
         
-        # Ambil harga terakhir untuk menghitung TP/SL yang tetap valid
         try:
             stock = yf.Ticker(symbol)
             data = stock.history(period="1d", interval="1d")
@@ -62,49 +87,44 @@ def analyze_stock(symbol, history_period, prediction_days=30):
         except:
             last_price = 0
 
-        # Jika harga terakhir berhasil diambil, hitung TP/SL default
-        tp1_default = last_price * 1.005 # 0.5% dari last price
-        tp2_default = last_price * 1.01 # 1% dari last price
-        sl_default = last_price * 0.95
+        default_tp1 = last_price * 1.01 
+        default_tp2 = last_price * 1.02 
+        default_sl = last_price * 0.95
 
         empty_predictions = {
-            "high_30d": 0,
-            "low_30d": 0,
-            "change_pct": 0,
-            "summary": f"Prediction unavailable. Model error: {e}", # Tampilkan error yang ditangkap
-            "q01": [],
-            "q09": [],
-            "tp1": tp1_default,
-            "tp2": tp2_default,
-            "sl": sl_default,
+            "high_30d": 0, "low_30d": 0, "change_pct": 0,
+            "summary": f"Prediction unavailable. Model error: {e}",
+            "q01": [], "q09": [], 
+            "tp1": default_tp1, "tp2": default_tp2, "sl": default_sl,
         }
-        return {}, {}, {}, empty_fig, empty_fig, empty_fig, empty_predictions
+        # Mengembalikan dictionary kosong untuk risk_metrics saat error
+        return {}, {}, {}, {}, empty_fig, empty_fig, empty_fig, empty_predictions
 
 
-def update_analysis(symbol, history_period, prediction_days):
+def update_analysis(symbol, prediction_days):
     (
         fundamental_info,
         indicators,
         signals,
+        risk_metrics, # Tambahan metrik risiko
         fig_price,
         fig_technical,
         fig_prediction,
         predictions,
-    ) = analyze_stock(symbol, history_period, prediction_days)
+    ) = analyze_stock(symbol, prediction_days)
 
     if not fundamental_info:
-        # Jika fundamental gagal diambil (misalnya simbol salah), berikan pesan error umum
         error_msg = f"Unable to fetch stock data for {symbol.upper()}. Please check the symbol."
-        # Gunakan prediksi yang dikembalikan (meski kosong) untuk mendapatkan TP/SL default
         tp_sl_info = f"<b>TP1:</b> Rp{predictions.get('tp1', 0):,.2f}<br><b>TP2:</b> Rp{predictions.get('tp2', 0):,.2f}<br><b>Stop Loss:</b> Rp{predictions.get('sl', 0):,.2f}<br><br><b>Model Insight:</b><br>Data fetching failed. Cannot proceed with analysis."
         
         return (
-            f"""<div style="color: red; padding: 10px; border: 1px solid red;">{error_msg}</div><br>{tp_sl_info}""",
+            f"""<div style="color: red; padding: 10px; border: 1px solid red; border-radius: 5px;">{error_msg}</div><br>{tp_sl_info}""",
             gr.Plot.update(value=None),
             gr.Plot.update(value=None),
             gr.Plot.update(value=None),
         )
 
+    # --- FUNDAMENTALS ---
     fundamentals = f"""
     <h4>COMPANY FUNDAMENTALS</h4>
     <b>Name:</b> {fundamental_info.get('name', 'N/A')} ({symbol.upper()})<br>
@@ -115,6 +135,7 @@ def update_analysis(symbol, history_period, prediction_days):
     <b>Volume:</b> {fundamental_info.get('volume', 0):,}<br>
     """
 
+    # --- TECHNICAL SIGNAL ---
     details_list = "".join(
         [f"<li>{line.strip()}</li>" for line in signals.get("details", "").split("\n") if line.strip()]
     )
@@ -132,32 +153,47 @@ def update_analysis(symbol, history_period, prediction_days):
     </ul>
     """
     
-    # Hitung Min/Max dari seluruh rentang kepercayaan (Q0.1 dan Q0.9)
+    # --- RISK METRICS ---
+    risk_details = ""
+    if "error" in risk_metrics:
+        risk_details = f"<b style='color: red;'>{risk_metrics['error']}</b>"
+    else:
+        for key, value in risk_metrics.items():
+            risk_details += f"<b>{key.replace('_', ' ')}:</b> {value}<br>"
+            
+    risk_report = f"""
+    <h4>ADVANCED RISK METRICS (1Y HISTORICAL)</h4>
+    {risk_details}
+    """
+    
+    # --- AI FORECAST ---
     band_min = float(min(predictions.get('q01', [0]))) if predictions.get('q01') and predictions.get('q01').size > 0 else 0
     band_max = float(max(predictions.get('q09', [0]))) if predictions.get('q09') and predictions.get('q09').size > 0 else 0
 
     prediction = f"""
-    <h4>30-DAY AI FORECAST (CHRONOS-2)</h4>
+    <h4>{prediction_days}-DAY AI FORECAST (CHRONOS-2 + COVARIATES)</h4>
     <b>Predicted Median High:</b> Rp{predictions.get('high_30d', 0):,.2f}<br>
     <b>Predicted Median Low:</b> Rp{predictions.get('low_30d', 0):,.2f}<br>
     <b>Expected Change:</b> {predictions.get('change_pct', 0):.2f}%<br>
     ---
     <h4>RISK ANALYSIS (90% CONFIDENCE)</h4>
-    <b>Min. Possible Price:</b> Rp{band_min:,.2f}<br>
-    <b>Max. Possible Price:</b> Rp{band_max:,.2f}<br>
+    <b>Min. Possible Price (Q0.1):</b> Rp{band_min:,.2f}<br>
+    <b>Max. Possible Price (Q0.9):</b> Rp{band_max:,.2f}<br>
     ---
-    <b>TP1:</b> Rp{predictions.get('tp1', 0):,.2f}<br>
-    <b>TP2:</b> Rp{predictions.get('tp2', 0):,.2f}<br>
-    <b>Stop Loss:</b> Rp{predictions.get('sl', 0):,.2f}<br><br>
+    <b>TP1 (Conservative Target):</b> Rp{predictions.get('tp1', 0):,.2f}<br>
+    <b>TP2 (Aggressive Target):</b> Rp{predictions.get('tp2', 0):,.2f}<br>
+    <b>Stop Loss (Q0.1 based):</b> Rp{predictions.get('sl', 0):,.2f}<br><br>
     <b>Model Insight:</b><br>{predictions.get('summary', 'No analysis available')}
     """
 
+    # Menggunakan tata letak 4 kolom/panel
     return (
         f"""
-        <div style="display: flex; flex-direction: row; gap: 16px;">
-            <div style="flex: 1; min-width: 30%; border: 1px solid #ccc; padding: 10px; border-radius: 5px;">{fundamentals}</div>
-            <div style="flex: 1; min-width: 30%; border: 1px solid #ccc; padding: 10px; border-radius: 5px;">{trading_signal}</div>
-            <div style="flex: 1; min-width: 30%; border: 1px solid #ccc; padding: 10px; border-radius: 5px;">{prediction}</div>
+        <div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 16px;">
+            <div style="border: 1px solid #ccc; padding: 10px; border-radius: 5px; height: 100%;">{fundamentals}</div>
+            <div style="border: 1px solid #ccc; padding: 10px; border-radius: 5px; height: 100%;">{trading_signal}</div>
+            <div style="border: 1px solid #ccc; padding: 10px; border-radius: 5px; height: 100%;">{risk_report}</div>
+            <div style="border: 1px solid #ccc; padding: 10px; border-radius: 5px; height: 100%;">{prediction}</div>
         </div>
         """,
         fig_price,
@@ -166,12 +202,13 @@ def update_analysis(symbol, history_period, prediction_days):
     )
 
 
+# --- Gradio Interface ---
 with gr.Blocks(
     title="REXPRO FINANCIAL AI DASHBOARD"
 ) as app:
     gr.Markdown("# REXPRO FINANCIAL AI DASHBOARD")
     gr.Markdown(
-        "Comprehensive stock analytics powered by **AI forecasting and technical analysis.**"
+        "Comprehensive stock analytics powered by **AI forecasting, advanced risk metrics, and future technical analysis.**"
     )
 
     with gr.Row():
@@ -181,13 +218,6 @@ with gr.Blocks(
             placeholder="Example: BBCA, TLKM, ADRO, BMRI",
             interactive=True,
         )
-        # INPUT BARU: Kontrol Periode Data Historis
-        history_period_input = gr.Radio(
-            label="HISTORICAL DATA PERIOD",
-            choices=["6mo", "1y", "3y", "5y"],
-            value="3y",
-            interactive=True,
-        )
         prediction_days = gr.Slider(
             label="FORECAST PERIOD (DAYS)",
             minimum=5,
@@ -196,7 +226,7 @@ with gr.Blocks(
             value=30,
             interactive=True,
         )
-        analyze_button = gr.Button("RUN ANALYSIS")
+        analyze_button = gr.Button("RUN ADVANCED ANALYSIS")
 
     gr.Markdown("---")
     report_section = gr.HTML() 
@@ -207,11 +237,11 @@ with gr.Blocks(
             price_chart = gr.Plot(label="PRICE & MOVING AVERAGES")
             technical_chart = gr.Plot(label="TECHNICAL INDICATORS OVERVIEW")
         gr.Markdown("---")
-        prediction_chart = gr.Plot(label="AI FORECAST PROJECTION")
+        prediction_chart = gr.Plot(label="AI FORECAST & FUTURE TECHNICAL PROJECTION")
 
     analyze_button.click(
         fn=update_analysis,
-        inputs=[symbol, history_period_input, prediction_days],
+        inputs=[symbol, prediction_days],
         outputs=[report_section, price_chart, technical_chart, prediction_chart],
     )