Update app.py

app.py

@@ -6,91 +6,104 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import timesfm
 
-# --- UPDATED INITIALIZATION (v2.5 API) ---
-# We use the new torch-specific class and ForecastConfig object
+# --- SDK v2.5 INITIALIZATION ---
+# Load the 200M parameter model specifically for PyTorch
 model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
     "google/timesfm-2.5-200m-pytorch"
 )
 
-# Configure the model using the new ForecastConfig
+# FIXED: Parameters are now moved into ForecastConfig
 model.compile(timesfm.ForecastConfig(
-    max_context=512,
-    max_horizon=128,
+    max_context=1024,      # v2.5 now supports up to 16k
+    max_horizon=256,
     normalize_inputs=True,
     infer_is_positive=True
 ))
 
-def get_financial_plot(df, forecast_df, ticker, is_backtest=False):
+def generate_professional_chart(df, forecast_data, ticker, is_backtest=False):
     fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
-                        vertical_spacing=0.05, row_heights=[0.75, 0.25])
+                        vertical_spacing=0.03, row_heights=[0.8, 0.2])
 
-    display_df = df[:-30] if is_backtest else df
+    hist_df = df[:-30] if is_backtest else df
     
-    # 1. Historical Price
-    fig.add_trace(go.Scatter(x=display_df.index, y=display_df['Close'], name='History', line=dict(color='#2962FF')), row=1, col=1)
+    # Historical Trend
+    fig.add_trace(go.Scatter(x=hist_df.index, y=hist_df['Close'], name='Market Price',
+                             line=dict(color='#2962FF', width=2)), row=1, col=1)
 
-    # 2. Backtest Truth
+    # Reality Check (Dotted)
     if is_backtest:
-        fig.add_trace(go.Scatter(x=df.index[-30:], y=df['Close'][-30:], name='Actual', line=dict(color='#787b86', dash='dot')), row=1, col=1)
+        fig.add_trace(go.Scatter(x=df.index[-30:], y=df['Close'][-30:], name='Actual Path',
+                                 line=dict(color='#787b86', dash='dot')), row=1, col=1)
 
-    # 3. AI Forecast
-    fc_dates = pd.date_range(start=display_df.index[-1], periods=len(forecast_df)+1, freq='D')[1:]
-    fig.add_trace(go.Scatter(x=fc_dates, y=forecast_df, name='AI Forecast', line=dict(color='#F23645', width=3)), row=1, col=1)
+    # AI Forecast Line
+    fc_dates = pd.date_range(start=hist_df.index[-1], periods=len(forecast_data)+1, freq='B')[1:]
+    fig.add_trace(go.Scatter(x=fc_dates, y=forecast_data, name='AI Forecast',
+                             line=dict(color='#F23645', width=3)), row=1, col=1)
 
-    # 4. Volume
-    fig.add_trace(go.Bar(x=df.index, y=df['Volume'], name='Volume', marker_color='rgba(38, 166, 154, 0.5)'), row=2, col=1)
+    # Volume Bars
+    fig.add_trace(go.Bar(x=df.index, y=df['Volume'], name='Volume',
+                         marker_color='rgba(120, 123, 134, 0.3)'), row=2, col=1)
 
-    fig.update_layout(template='plotly_dark', paper_bgcolor='#131722', plot_bgcolor='#131722', margin=dict(l=10, r=10, t=40, b=10))
+    fig.update_layout(template='plotly_dark', paper_bgcolor='#131722', plot_bgcolor='#131722',
+                      margin=dict(l=50, r=50, t=30, b=30), hovermode="x unified")
+    fig.update_yaxes(side='right', gridcolor='#2a2e39')
+    fig.update_xaxes(gridcolor='#2a2e39')
     return fig
 
-def run_analysis(ticker, horizon, mode):
+def run_terminal(ticker, horizon, mode):
     try:
-        df = yf.download(ticker, period="2y")
-        if df.empty: return None, "⚠️ Ticker Not Found", None
+        # 1. Data Ingestion
+        data = yf.download(ticker, period="2y")
+        if data.empty: return None, "⚠️ SYMBOL NOT FOUND", None
         
-        train_df = df[:-30] if mode == "Backtest (Reality Check)" else df
+        train_data = data[:-30] if mode == "Backtest Mode" else data
         
-        # New API uses simple array inputs
-        inputs = [train_df['Close'].values]
-        
-        # Run inference using the v2.5 forecast method
+        # 2. SDK Inference (Array-based input for v2.5)
+        inputs = [train_data['Close'].values]
         point_forecast, _ = model.forecast(
-            inputs=inputs,
-            horizon=horizon if mode != "Backtest (Reality Check)" else 30
+            inputs=inputs, 
+            horizon=30 if mode == "Backtest Mode" else horizon
         )
+        prediction = point_forecast[0]
+
+        # 3. Financial Metrics
+        pct_change = ((prediction[-1] - train_data['Close'].iloc[-1]) / train_data['Close'].iloc[-1]) * 100
         
-        forecast_values = point_forecast[0] # Get results for the first (only) input
-        
-        # Save CSV
-        csv_path = f"{ticker}_forecast.csv"
-        pd.DataFrame({'Forecast': forecast_values}).to_csv(csv_path, index=False)
-        
-        # Signals
-        if mode != "Backtest (Reality Check)":
-            pct = ((forecast_values[-1] - train_df['Close'].iloc[-1]) / train_df['Close'].iloc[-1]) * 100
-            signal = f"<h3 style='color: {'#00ff88' if pct > 0 else '#ff4444'};'>{ 'BULLISH' if pct > 0 else 'BEARISH' } ({pct:+.2f}%)</h3>"
+        if mode == "Future Forecast":
+            signal = f"<h2 style='color: {'#00ff88' if pct_change > 0 else '#ff4444'}; text-align: center;'>" \
+                     f"{'BULLISH' if pct_change > 0 else 'BEARISH'} ({pct_change:+.2f}%)</h2>"
         else:
-            acc = 100 - (abs(df['Close'].iloc[-1] - forecast_values[-1]) / df['Close'].iloc[-1] * 100)
-            signal = f"<h3 style='color: #FFD700;'>Accuracy: {acc:.1f}%</h3>"
+            # Calculate Backtest Accuracy
+            accuracy = 100 - abs((data['Close'].iloc[-1] - prediction[-1]) / data['Close'].iloc[-1] * 100)
+            signal = f"<h2 style='color: #FFD700; text-align: center;'>AI ACCURACY: {accuracy:.1f}%</h2>"
 
-        return get_financial_plot(df, forecast_values, ticker, (mode == "Backtest (Reality Check)")), signal, csv_path
+        # 4. Generate Report
+        report_path = f"{ticker}_AI_Report.csv"
+        pd.DataFrame({'Forecast_Price': prediction}).to_csv(report_path, index=False)
         
+        chart = generate_professional_chart(data, prediction, ticker, (mode == "Backtest Mode"))
+        return chart, signal, report_path
+
     except Exception as e:
-        return None, f"Runtime Error: {str(e)}", None
+        return None, f"<div style='color:red;'>API Error: {str(e)}</div>", None
 
-# --- UI ---
-with gr.Blocks(theme=gr.themes.Default(), css=".gradio-container {background-color: #000000}") as demo:
-    gr.HTML("<h2 style='color: #2962FF; text-align: center;'>G-TIMES 2.5 QUANT TERMINAL</h2>")
+# --- TERMINAL UI ---
+with gr.Blocks(title="G-TIMES QUANT 2.5", theme=gr.themes.Base()) as demo:
+    gr.HTML("<div style='background-color:#131722; padding:20px; border-bottom:3px solid #2962FF; text-align:center;'>"
+            "<h1 style='color:white; letter-spacing:3px;'>G-TIMES <span style='color:#2962FF;'>QUANT 2.5</span></h1></div>")
+    
     with gr.Row():
         with gr.Column(scale=1):
-            ticker_in = gr.Textbox(label="SYMBOL", value="NVDA")
-            mode_in = gr.Radio(["Future Forecast", "Backtest (Reality Check)"], label="MODE", value="Future Forecast")
-            days_in = gr.Slider(5, 128, value=30, label="Days")
-            btn = gr.Button("RUN ANALYSIS", variant="primary")
-            result_box = gr.HTML()
-            file_output = gr.File(label="Download Forecast")
+            ticker_in = gr.Textbox(label="TICKER", value="TSLA")
+            mode_in = gr.Radio(["Future Forecast", "Backtest Mode"], label="STRATEGY", value="Future Forecast")
+            days_in = gr.Slider(7, 128, value=30, label="HORIZON")
+            btn = gr.Button("RUN QUANT ANALYSIS", variant="primary")
+            status_out = gr.HTML()
+            file_out = gr.File(label="CSV EXPORT")
+            
         with gr.Column(scale=4):
             plot_out = gr.Plot()
-    btn.click(run_analysis, [ticker_in, days_in, mode_in], [plot_out, result_box, file_output])
+
+    btn.click(run_terminal, [ticker_in, days_in, mode_in], [plot_out, status_out, file_out])
 
 demo.launch()