Create app.py

app.py

@@ -0,0 +1,322 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import yfinance as yf
+from datetime import datetime, timedelta
+import pickle
+import json
+import warnings
+warnings.filterwarnings('ignore')
+
+# Load trained model
+def load_model():
+    try:
+        with open('portfolio_risk_model.pkl', 'rb') as f:
+            model = pickle.load(f)
+        return model
+    except Exception as e:
+        print(f"Model loading error: {str(e)}")
+        return None
+
+class GradioRiskApp:
+    def __init__(self):
+        self.model = load_model()
+        self.available_stocks = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 
+                                'META', 'NVDA', 'NFLX', 'JPM', 'JNJ',
+                                'V', 'PG', 'DIS', 'BAC', 'XOM']
+    
+    def get_current_data(self, symbols, period='1y'):
+        """Get current stock data"""
+        try:
+            data = yf.download(symbols, period=period)['Adj Close']
+            return data
+        except Exception as e:
+            print(f"Data fetch error: {str(e)}")
+            return None
+    
+    def run_monte_carlo(self, weights, days=252, simulations=5000):
+        """Run Monte Carlo simulation with given weights"""
+        if self.model is None:
+            return None
+            
+        mean_returns = np.array(self.model['mean_returns'])
+        cov_matrix = np.array(self.model['covariance_matrix'])
+        
+        # Generate simulation
+        np.random.seed(42)
+        try:
+            L = np.linalg.cholesky(cov_matrix)
+        except:
+            eigvals, eigvecs = np.linalg.eigh(cov_matrix)
+            L = eigvecs @ np.diag(np.sqrt(eigvals))
+        
+        simulation_results = []
+        for _ in range(simulations):
+            random_numbers = np.random.normal(0, 1, size=(days, len(mean_returns)))
+            correlated_randoms = random_numbers @ L.T
+            daily_returns = correlated_randoms + mean_returns
+            
+            portfolio_value = 100
+            portfolio_path = [portfolio_value]
+            
+            for day in range(days):
+                daily_portfolio_return = np.dot(daily_returns[day], weights)
+                portfolio_value *= (1 + daily_portfolio_return)
+                portfolio_path.append(portfolio_value)
+            
+            simulation_results.append(portfolio_path)
+        
+        return np.array(simulation_results)
+    
+    def calculate_metrics(self, simulation_results):
+        """Calculate risk metrics from simulation"""
+        final_values = simulation_results[:, -1]
+        
+        metrics = {
+            'expected_value': np.mean(final_values),
+            'median_value': np.median(final_values),
+            'std_value': np.std(final_values),
+            'prob_10_loss': np.mean(final_values < 90),
+            'prob_20_loss': np.mean(final_values < 80),
+            'prob_30_loss': np.mean(final_values < 70),
+            'var_95': np.percentile(final_values, 5),
+            'best_case': np.percentile(final_values, 95),
+            'worst_case': np.percentile(final_values, 5)
+        }
+        
+        return metrics
+    
+    def create_simulation_plot(self, simulation_results):
+        """Create Monte Carlo simulation plot"""
+        fig = go.Figure()
+        
+        # Sample paths
+        for i in range(min(50, len(simulation_results))):
+            fig.add_trace(go.Scatter(
+                y=simulation_results[i],
+                mode='lines',
+                line=dict(width=1, color='blue'),
+                opacity=0.1,
+                showlegend=False
+            ))
+        
+        # Mean path
+        mean_path = simulation_results.mean(axis=0)
+        fig.add_trace(go.Scatter(
+            y=mean_path,
+            mode='lines',
+            line=dict(width=3, color='red'),
+            name='Average Path'
+        ))
+        
+        fig.update_layout(
+            title="Monte Carlo Simulation Paths",
+            xaxis_title="Trading Days",
+            yaxis_title="Portfolio Value ($)",
+            height=400
+        )
+        
+        return fig
+    
+    def create_distribution_plot(self, simulation_results, metrics):
+        """Create final value distribution plot"""
+        final_values = simulation_results[:, -1]
+        
+        fig = px.histogram(
+            x=final_values,
+            nbins=50,
+            title="Distribution of Final Portfolio Values",
+            color_discrete_sequence=['#FF6B6B']
+        )
+        
+        fig.add_vline(x=metrics['expected_value'], line_dash="dash", line_color="red",
+                     annotation_text=f"Mean: ${metrics['expected_value']:.2f}")
+        fig.add_vline(x=metrics['var_95'], line_dash="dash", line_color="orange",
+                     annotation_text=f"5% VaR: ${metrics['var_95']:.2f}")
+        
+        fig.update_layout(height=400, showlegend=False)
+        
+        return fig
+    
+    def create_risk_gauge(self, metrics):
+        """Create risk probability gauge"""
+        risk_prob = metrics['prob_10_loss'] * 100
+        
+        fig = go.Figure(go.Indicator(
+            mode = "gauge+number+delta",
+            value = risk_prob,
+            domain = {'x': [0, 1], 'y': [0, 1]},
+            title = {'text': "Probability of >10% Loss"},
+            gauge = {
+                'axis': {'range': [None, 50]},
+                'bar': {'color': "darkblue"},
+                'steps': [
+                    {'range': [0, 10], 'color': "lightgreen"},
+                    {'range': [10, 25], 'color': "yellow"},
+                    {'range': [25, 50], 'color': "red"}],
+                'threshold': {
+                    'line': {'color': "red", 'width': 4},
+                    'thickness': 0.75,
+                    'value': 30}}
+        ))
+        fig.update_layout(height=300)
+        
+        return fig
+    
+    def analyze_portfolio(self, selected_stocks, simulation_days, num_simulations):
+        """Main analysis function for Gradio"""
+        if not selected_stocks:
+            return "Please select at least one stock", None, None, None, None
+        
+        try:
+            # Equal weights for selected stocks
+            n_stocks = len(selected_stocks)
+            weights = [1/n_stocks] * n_stocks
+            
+            # Run simulation
+            simulation_results = self.run_monte_carlo(weights, simulation_days, num_simulations)
+            
+            if simulation_results is None:
+                return "Error in simulation", None, None, None, None
+            
+            # Calculate metrics
+            metrics = self.calculate_metrics(simulation_results)
+            
+            # Create plots
+            sim_plot = self.create_simulation_plot(simulation_results)
+            dist_plot = self.create_distribution_plot(simulation_results, metrics)
+            risk_gauge = self.create_risk_gauge(metrics)
+            
+            # Create metrics summary
+            summary_html = f"""
+            <div style="font-family: Arial, sans-serif; padding: 20px; background: #f5f5f5; border-radius: 10px;">
+                <h2 style="color: #333; text-align: center;">📊 Portfolio Risk Analysis Report</h2>
+                
+                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px; margin-top: 20px;">
+                    <div style="background: white; padding: 15px; border-radius: 8px; border-left: 4px solid #4CAF50;">
+                        <h3 style="margin: 0; color: #666;">Expected Value</h3>
+                        <p style="font-size: 24px; font-weight: bold; color: #333; margin: 5px 0;">${metrics['expected_value']:.2f}</p>
+                    </div>
+                    
+                    <div style="background: white; padding: 15px; border-radius: 8px; border-left: 4px solid #FF9800;">
+                        <h3 style="margin: 0; color: #666;">Value at Risk (95%)</h3>
+                        <p style="font-size: 24px; font-weight: bold; color: #333; margin: 5px 0;">${metrics['var_95']:.2f}</p>
+                    </div>
+                    
+                    <div style="background: white; padding: 15px; border-radius: 8px; border-left: 4px solid #F44336;">
+                        <h3 style="margin: 0; color: #666;">10% Loss Probability</h3>
+                        <p style="font-size: 24px; font-weight: bold; color: #333; margin: 5px 0;">{metrics['prob_10_loss']*100:.1f}%</p>
+                    </div>
+                    
+                    <div style="background: white; padding: 15px; border-radius: 8px; border-left: 4px solid #2196F3;">
+                        <h3 style="margin: 0; color: #666;">Best Case (95%)</h3>
+                        <p style="font-size: 24px; font-weight: bold; color: #333; margin: 5px 0;">${metrics['best_case']:.2f}</p>
+                    </div>
+                </div>
+                
+                <div style="margin-top: 20px;">
+                    <h3 style="color: #333;">Portfolio Composition:</h3>
+                    <p style="color: #666;">{', '.join(selected_stocks)}</p>
+                    <p style="color: #666;">Equal weights: {', '.join([f'{w*100:.1f}%' for w in weights])}</p>
+                </div>
+            </div>
+            """
+            
+            return summary_html, sim_plot, dist_plot, risk_gauge, None
+            
+        except Exception as e:
+            return f"Error in analysis: {str(e)}", None, None, None, None
+
+# Initialize the app
+app = GradioRiskApp()
+
+# Custom CSS for better styling
+custom_css = """
+.gradio-container {
+    font-family: 'Arial', sans-serif;
+}
+.container {
+    max-width: 1200px;
+    margin: 0 auto;
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 📊 QuantRisk Pro - Portfolio Risk Analyzer
+        **AI-Powered Monte Carlo Simulations for Portfolio Risk Assessment**
+        
+        *Analyze your stock portfolio risk using advanced probabilistic models*
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🔧 Portfolio Configuration")
+            
+            stock_checkboxes = gr.CheckboxGroup(
+                choices=app.available_stocks,
+                value=['AAPL', 'MSFT', 'GOOGL'],
+                label="Select Stocks for Portfolio",
+                info="Choose up to 10 stocks"
+            )
+            
+            simulation_days = gr.Slider(
+                minimum=30,
+                maximum=500,
+                value=252,
+                step=30,
+                label="Time Horizon (Days)",
+                info="Simulation period in trading days"
+            )
+            
+            num_simulations = gr.Radio(
+                choices=[1000, 2500, 5000, 10000],
+                value=5000,
+                label="Number of Simulations",
+                info="More simulations = more accuracy"
+            )
+            
+            analyze_btn = gr.Button("🚀 Run Risk Analysis", variant="primary")
+        
+        with gr.Column(scale=2):
+            gr.Markdown("### 📈 Analysis Results")
+            
+            summary_html = gr.HTML(
+                label="Risk Summary",
+                value="<div style='text-align: center; color: #666; padding: 50px;'>Configure your portfolio and click 'Run Risk Analysis' to see results</div>"
+            )
+            
+            with gr.Row():
+                risk_gauge = gr.Plot(label="Risk Probability Gauge")
+                simulation_plot = gr.Plot(label="Monte Carlo Simulation")
+            
+            with gr.Row():
+                distribution_plot = gr.Plot(label="Value Distribution")
+    
+    # Footer
+    gr.Markdown(
+        """
+        ---
+        <div style="text-align: center; color: #666;">
+            <p>Built with ❤️ using Gradio | QuantRisk Pro v1.0</p>
+            <p>Uses Monte Carlo simulations and historical data for risk assessment</p>
+        </div>
+        """
+    )
+    
+    # Set up the analysis function
+    analyze_btn.click(
+        fn=app.analyze_portfolio,
+        inputs=[stock_checkboxes, simulation_days, num_simulations],
+        outputs=[summary_html, simulation_plot, distribution_plot, risk_gauge]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(share=True)