app.py

import gradio as gr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import norm, lognorm
import seaborn as sns

# Set matplotlib style for professional plots
plt.style.use('default')
sns.set_palette("husl")

class TVOGAnalysis:
    def __init__(self):
        self.reset_parameters()
    
    def reset_parameters(self):
        """Reset to default parameters"""
        self.scenarios = 10000
        self.risk_free_rate = 0.02
        self.volatility = 0.03
        self.maturity = 10
        self.sum_assured = 500000
        self.policy_count = 100
        
    def generate_random_numbers(self, scenarios, time_steps):
        """Generate standard normal random numbers"""
        np.random.seed(42)  # For reproducibility
        return np.random.standard_normal((scenarios, time_steps))
    
    def simulate_account_values(self, initial_av, scenarios, time_steps):
        """Simulate account value paths using geometric Brownian motion"""
        dt = 1/12  # Monthly time steps
        rand_nums = self.generate_random_numbers(scenarios, time_steps)
        
        # Initialize account value matrix
        av_paths = np.zeros((scenarios, time_steps + 1))
        av_paths[:, 0] = initial_av
        
        # Simulate paths
        for t in range(time_steps):
            drift = (self.risk_free_rate - 0.5 * self.volatility**2) * dt
            diffusion = self.volatility * np.sqrt(dt) * rand_nums[:, t]
            av_paths[:, t+1] = av_paths[:, t] * np.exp(drift + diffusion)
        
        return av_paths
    
    def calculate_gmab_payouts(self, av_paths):
        """Calculate GMAB payouts at maturity"""
        final_av = av_paths[:, -1]
        guarantee = self.sum_assured * self.policy_count
        payouts = np.maximum(guarantee - final_av, 0)
        
        # Present value of payouts
        discount_factor = np.exp(-self.risk_free_rate * self.maturity)
        pv_payouts = payouts * discount_factor
        
        return pv_payouts, payouts
    
    def black_scholes_put(self, S0, K, T, r, sigma):
        """Black-Scholes-Merton formula for European put option"""
        d1 = (np.log(S0/K) + (r + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
        d2 = d1 - sigma*np.sqrt(T)
        
        put_price = K*np.exp(-r*T)*norm.cdf(-d2) - S0*norm.cdf(-d1)
        return put_price

def create_dashboard():
    tvog = TVOGAnalysis()
    
    def update_analysis(scenarios, risk_free_rate, volatility, maturity, 
                       sum_assured, policy_count, min_premium, max_premium, num_points):
        
        # Update parameters
        tvog.scenarios = int(scenarios)
        tvog.risk_free_rate = risk_free_rate
        tvog.volatility = volatility
        tvog.maturity = maturity
        tvog.sum_assured = sum_assured
        tvog.policy_count = policy_count
        
        # Create model points with varying initial account values
        premiums = np.linspace(min_premium, max_premium, int(num_points))
        initial_avs = premiums * policy_count
        
        monte_carlo_results = []
        black_scholes_results = []
        
        time_steps = int(maturity * 12)  # Monthly steps
        
        for initial_av in initial_avs:
            # Monte Carlo simulation
            av_paths = tvog.simulate_account_values(initial_av, tvog.scenarios, time_steps)
            pv_payouts, _ = tvog.calculate_gmab_payouts(av_paths)
            mc_tvog = np.mean(pv_payouts)
            monte_carlo_results.append(mc_tvog)
            
            # Black-Scholes-Merton
            guarantee = sum_assured * policy_count
            bs_tvog = tvog.black_scholes_put(initial_av, guarantee, maturity, 
                                           risk_free_rate, volatility)
            black_scholes_results.append(bs_tvog)
        
        # Create results DataFrame
        results_df = pd.DataFrame({
            'Premium_per_Policy': premiums,
            'Initial_Account_Value': initial_avs,
            'Monte_Carlo_TVOG': monte_carlo_results,
            'Black_Scholes_TVOG': black_scholes_results,
            'Ratio_MC_BS': np.array(monte_carlo_results) / np.array(black_scholes_results),
            'Difference': np.array(monte_carlo_results) - np.array(black_scholes_results)
        })
        
        # Create plots
        fig1 = create_tvog_comparison_plot(results_df)
        fig2 = create_sample_paths_plot(tvog, initial_avs[len(initial_avs)//2], time_steps)
        fig3 = create_distribution_plots(tvog, initial_avs[len(initial_avs)//2], time_steps)
        fig4 = create_convergence_plot(tvog, initial_avs[len(initial_avs)//2], time_steps)
        
        return results_df, fig1, fig2, fig3, fig4
    
    def create_tvog_comparison_plot(results_df):
        """Create TVOG comparison plot"""
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
        
        # TVOG Comparison
        ax1.scatter(results_df['Initial_Account_Value'], results_df['Monte_Carlo_TVOG'], 
                   s=50, alpha=0.7, label='Monte Carlo', color='blue')
        ax1.scatter(results_df['Initial_Account_Value'], results_df['Black_Scholes_TVOG'], 
                   s=50, alpha=0.7, label='Black-Scholes-Merton', color='red')
        ax1.set_xlabel('Initial Account Value')
        ax1.set_ylabel('TVOG Value')
        ax1.set_title('TVOG: Monte Carlo vs Black-Scholes-Merton')
        ax1.legend()
        ax1.grid(True, alpha=0.3)
        
        # Ratio Plot
        ax2.plot(results_df['Initial_Account_Value'], results_df['Ratio_MC_BS'], 
                'o-', color='green', markersize=6)
        ax2.axhline(y=1, color='red', linestyle='--', alpha=0.7)
        ax2.set_xlabel('Initial Account Value')
        ax2.set_ylabel('Monte Carlo / Black-Scholes Ratio')
        ax2.set_title('Convergence Ratio (MC/BS)')
        ax2.grid(True, alpha=0.3)
        
        plt.tight_layout()
        return fig
    
    def create_sample_paths_plot(tvog, initial_av, time_steps):
        """Create sample simulation paths plot"""
        sample_scenarios = min(100, tvog.scenarios)
        av_paths = tvog.simulate_account_values(initial_av, sample_scenarios, time_steps)
        
        fig, ax = plt.subplots(1, 1, figsize=(12, 6))
        
        time_axis = np.arange(time_steps + 1) / 12  # Convert to years
        
        for i in range(sample_scenarios):
            ax.plot(time_axis, av_paths[i, :], alpha=0.3, linewidth=0.8)
        
        # Add mean path
        mean_path = np.mean(av_paths, axis=0)
        ax.plot(time_axis, mean_path, color='red', linewidth=3, label='Mean Path')
        
        # Add guarantee line
        guarantee = tvog.sum_assured * tvog.policy_count
        ax.axhline(y=guarantee, color='black', linestyle='--', linewidth=2, 
                  label=f'Guarantee Level ({guarantee:,.0f})')
        
        ax.set_xlabel('Time (Years)')
        ax.set_ylabel('Account Value')
        ax.set_title(f'Sample Account Value Simulation Paths (n={sample_scenarios})')
        ax.legend()
        ax.grid(True, alpha=0.3)
        
        return fig
    
    def create_distribution_plots(tvog, initial_av, time_steps):
        """Create distribution analysis plots"""
        av_paths = tvog.simulate_account_values(initial_av, tvog.scenarios, time_steps)
        final_av = av_paths[:, -1]
        
        # Present value of final account values
        pv_final_av = final_av * np.exp(-tvog.risk_free_rate * tvog.maturity)
        
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
        
        # Histogram of final account values
        ax1.hist(pv_final_av, bins=50, density=True, alpha=0.7, color='skyblue')
        
        # Theoretical lognormal distribution
        S0 = initial_av
        sigma = tvog.volatility
        T = tvog.maturity
        
        x_range = np.linspace(pv_final_av.min(), pv_final_av.max(), 1000)
        theoretical_pdf = lognorm.pdf(x_range, sigma * np.sqrt(T), scale=S0)
        ax1.plot(x_range, theoretical_pdf, 'r-', linewidth=2, label='Theoretical Lognormal')
        ax1.axvline(x=S0, color='green', linestyle='--', label=f'Initial Value: {S0:,.0f}')
        ax1.axvline(x=np.mean(pv_final_av), color='orange', linestyle='--', 
                   label=f'Simulated Mean: {np.mean(pv_final_av):,.0f}')
        
        ax1.set_xlabel('Present Value of Final Account Value')
        ax1.set_ylabel('Density')
        ax1.set_title('Distribution of Final Account Values')
        ax1.legend()
        ax1.grid(True, alpha=0.3)
        
        # GMAB Payouts
        pv_payouts, _ = tvog.calculate_gmab_payouts(av_paths)
        non_zero_payouts = pv_payouts[pv_payouts > 0]
        
        ax2.hist(pv_payouts, bins=50, alpha=0.7, color='lightcoral')
        ax2.set_xlabel('GMAB Payout (Present Value)')
        ax2.set_ylabel('Frequency')
        ax2.set_title(f'GMAB Payout Distribution\n({len(non_zero_payouts)} non-zero payouts)')
        ax2.grid(True, alpha=0.3)
        
        # Add statistics text
        stats_text = f'Mean Payout: {np.mean(pv_payouts):,.0f}\n'
        stats_text += f'Max Payout: {np.max(pv_payouts):,.0f}\n'
        stats_text += f'Payout Probability: {len(non_zero_payouts)/len(pv_payouts):.1%}'
        ax2.text(0.95, 0.95, stats_text, transform=ax2.transAxes, 
                verticalalignment='top', horizontalalignment='right',
                bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
        
        plt.tight_layout()
        return fig
    
    def create_convergence_plot(tvog, initial_av, time_steps):
        """Create convergence analysis plot"""
        # Test different numbers of scenarios
        scenario_counts = [100, 500, 1000, 2000, 5000, 10000]
        if tvog.scenarios not in scenario_counts:
            scenario_counts.append(tvog.scenarios)
            scenario_counts.sort()
        
        mc_results = []
        
        guarantee = tvog.sum_assured * tvog.policy_count
        bs_result = tvog.black_scholes_put(initial_av, guarantee, tvog.maturity, 
                                         tvog.risk_free_rate, tvog.volatility)
        
        np.random.seed(42)  # For reproducible convergence
        
        for n_scenarios in scenario_counts:
            av_paths = tvog.simulate_account_values(initial_av, n_scenarios, time_steps)
            pv_payouts, _ = tvog.calculate_gmab_payouts(av_paths)
            mc_tvog = np.mean(pv_payouts)
            mc_results.append(mc_tvog)
        
        fig, ax = plt.subplots(1, 1, figsize=(10, 6))
        
        ax.plot(scenario_counts, mc_results, 'bo-', markersize=8, linewidth=2, 
               label='Monte Carlo Results')
        ax.axhline(y=bs_result, color='red', linestyle='--', linewidth=2, 
                  label=f'Black-Scholes Result: {bs_result:.0f}')
        
        ax.set_xlabel('Number of Scenarios')
        ax.set_ylabel('TVOG Value')
        ax.set_title('Monte Carlo Convergence Analysis')
        ax.legend()
        ax.grid(True, alpha=0.3)
        ax.set_xscale('log')
        
        # Add convergence statistics
        final_error = abs(mc_results[-1] - bs_result) / bs_result * 100
        ax.text(0.02, 0.98, f'Final Error: {final_error:.2f}%', 
               transform=ax.transAxes, verticalalignment='top',
               bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
        
        return fig
    
    # Create Gradio interface
    with gr.Blocks(title="TVOG Analysis Dashboard") as app:
        gr.Markdown("""
        # Time Value of Options and Guarantees (TVOG) Analysis Dashboard
        
        This dashboard compares Monte Carlo simulation results with Black-Scholes-Merton analytical solutions 
        for Variable Annuity products with Guaranteed Minimum Accumulation Benefits (GMAB).
        
        **Target Users:** Actuaries, Finance Professionals, Economists, and Academics
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### Model Parameters")
                
                scenarios = gr.Slider(
                    minimum=1000, maximum=50000, step=1000, value=10000,
                    label="Number of Monte Carlo Scenarios"
                )
                
                risk_free_rate = gr.Slider(
                    minimum=0.001, maximum=0.1, step=0.001, value=0.02,
                    label="Risk-Free Rate (continuous)"
                )
                
                volatility = gr.Slider(
                    minimum=0.01, maximum=0.5, step=0.01, value=0.03,
                    label="Volatility (σ)"
                )
                
                maturity = gr.Slider(
                    minimum=1, maximum=30, step=1, value=10,
                    label="Maturity (years)"
                )
                
                with gr.Row():
                    sum_assured = gr.Number(
                        value=500000, label="Sum Assured per Policy"
                    )
                    policy_count = gr.Number(
                        value=100, label="Number of Policies"
                    )
                
                gr.Markdown("### Model Point Range")
                
                with gr.Row():
                    min_premium = gr.Number(
                        value=300000, label="Min Premium per Policy"
                    )
                    max_premium = gr.Number(
                        value=500000, label="Max Premium per Policy"
                    )
                
                num_points = gr.Slider(
                    minimum=3, maximum=20, step=1, value=9,
                    label="Number of Model Points"
                )
                
                calculate_btn = gr.Button("Run Analysis", variant="primary")
            
            with gr.Column(scale=2):
                gr.Markdown("### Results Summary")
                results_table = gr.Dataframe(
                    headers=["Premium per Policy", "Initial Account Value", "Monte Carlo TVOG", 
                            "Black-Scholes TVOG", "MC/BS Ratio", "Difference"],
                    label="TVOG Comparison Results"
                )
        
        with gr.Tabs():
            with gr.Tab("TVOG Comparison"):
                tvog_plot = gr.Plot(label="Monte Carlo vs Black-Scholes Analysis")
            
            with gr.Tab("Simulation Paths"):
                paths_plot = gr.Plot(label="Sample Account Value Trajectories")
            
            with gr.Tab("Distribution Analysis"):
                dist_plot = gr.Plot(label="Final Values & Payout Distributions")
            
            with gr.Tab("Convergence Analysis"):
                conv_plot = gr.Plot(label="Monte Carlo Convergence to Analytical Solution")
        
        # Event handlers
        calculate_btn.click(
            fn=update_analysis,
            inputs=[scenarios, risk_free_rate, volatility, maturity, 
                   sum_assured, policy_count, min_premium, max_premium, num_points],
            outputs=[results_table, tvog_plot, paths_plot, dist_plot, conv_plot]
        )
        
        # Initial calculation
        app.load(
            fn=update_analysis,
            inputs=[scenarios, risk_free_rate, volatility, maturity, 
                   sum_assured, policy_count, min_premium, max_premium, num_points],
            outputs=[results_table, tvog_plot, paths_plot, dist_plot, conv_plot]
        )
    
    return app

if __name__ == "__main__":
    app = create_dashboard()
    app.launch()