luciablancar/BackTestingVaRLuci

1import gradio as gr2import numpy as np3import matplotlib.pyplot as plt4import pandas as pd5 6# Set style for light purple theme7plt.style.use('default')8 9def backtest_var(portfolio_value, num_test_days, seed_value):10    # Set seed for reproducibility11    np.random.seed(seed_value)12    13    # 1. GENERATE HISTORICAL RETURN DATA (100 days)14    returns = np.random.normal(0.001, 0.02, 100)15    16    # 2. CALCULATE VaR 95%17    var_95 = np.percentile(returns, 5)18    var_rp = var_95 * portfolio_value19    20    # 3. BACKTESTING21    future_returns = np.random.normal(0.001, 0.02, num_test_days)22    exceptions = future_returns < var_9523    num_exceptions = np.sum(exceptions)24    expected_exceptions = 0.05 * num_test_days25    26    # 4. CREATE VISUALIZATION WITH LIGHT PURPLE COLORS27    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))28    29    # Plot 1: Backtesting Results30    colors = ['#E6E6FA' if not exc else '#9370DB' for exc in exceptions]31    ax1.bar(range(num_test_days), future_returns * 100, color=colors, edgecolor='#8A2BE2')32    ax1.axhline(y=var_95 * 100, color='#8B008B', linestyle='--', linewidth=2, 33                label=f'VaR 95% ({var_95*100:.2f}%)')34    ax1.axhline(y=0, color='black', linestyle='-', alpha=0.3)35    ax1.set_title('Backtesting VaR 95%', fontsize=16, color='#4B0082', pad=20)36    ax1.set_ylabel('Return (%)', color='#4B0082', fontsize=12)37    ax1.set_xlabel('Day', color='#4B0082', fontsize=12)38    ax1.legend(fontsize=11)39    ax1.grid(True, alpha=0.3)40    ax1.tick_params(axis='both', which='major', labelsize=10)41    42    # Plot 2: Exception Analysis43    categories = ['Actual Exceptions', 'Expected Exceptions']44    values = [num_exceptions, expected_exceptions]45    colors_bar = ['#9370DB', '#D8BFD8']46    ax2.bar(categories, values, color=colors_bar, edgecolor='#8A2BE2')47    ax2.set_title('Exception Analysis', fontsize=16, color='#4B0082', pad=20)48    ax2.set_ylabel('Number of Days', color='#4B0082', fontsize=12)49    ax2.tick_params(axis='x', rotation=0, labelsize=11)50    51    # Add values on top of bars52    for i, v in enumerate(values):53        ax2.text(i, v + 0.1, f'{v:.1f}', ha='center', va='bottom', fontweight='bold', fontsize=12)54    55    plt.tight_layout()56    57    # 5. GENERATE RESULTS REPORT58    result_text = f"""59    📊 **BACKTESTING VaR RESULTS**60    61    **VaR 95% Model:**62    - **1-day VaR:** {var_95*100:.2f}%63    - **In Rupiah:** Rp {var_rp:,.0f}64    65    **Backtesting Period:** {num_test_days} days66    **Number of Exceptions:** {num_exceptions} days67    **Expected Exceptions:** {expected_exceptions:.1f} days68    69    **📈 Interpretation:**70    """71    72    if abs(num_exceptions - expected_exceptions) <= 1:73        result_text += "✅ **Model ACCURATE** - Exception rate as expected"74    elif num_exceptions > expected_exceptions:75        result_text += "⚠️ **Model TOO OPTIMISTIC** - Too many exceptions"76    else:77        result_text += "🔰 **Model CONSERVATIVE** - Lower exception rate than expected"78    79    return fig, result_text80 81# CUSTOM CSS FOR LIGHT PURPLE THEME82custom_css = """83.gr-box {84    border: 1px solid #D8BFD8 !important;85}86.gr-button {87    background: linear-gradient(45deg, #9370DB, #8A2BE2) !important;88    color: white !important;89    border: none !important;90    font-weight: bold !important;91}92.gr-button:hover {93    background: linear-gradient(45deg, #8A2BE2, #9370DB) !important;94    transform: scale(1.05) !important;95    transition: 0.3s !important;96}97h1 {98    color: #4B0082 !important;99    text-align: center !important;100}101.container {102    max-width: 100% !important;103}104"""105 106# CREATE GRADIO INTERFACE107with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple"), css=custom_css) as demo:108    gr.Markdown(109        """110        # 💜 VaR Backtesting Simulator111        112        **Interactive simulator to test the accuracy of Value at Risk (VaR) models**113        """)114    115    # SIMULATOR CONTROLS SECTION116    with gr.Row():117        with gr.Column(scale=1):118            gr.Markdown("### ⚙️ Simulation Parameters")119            portfolio = gr.Number(120                label="Portfolio Value (Rp)",121                value=1000000000,122                minimum=100000000,123                info="Enter your portfolio value in Rupiah"124            )125            test_days = gr.Slider(126                label="Backtesting Days",127                minimum=10,128                maximum=100,129                value=25,130                step=5,131                info="Number of days to test the VaR model"132            )133            seed = gr.Number(134                label="Random Seed",135                value=42,136                info="For reproducible results"137            )138            btn = gr.Button("🚀 Run Backtesting", variant="primary", size="lg")139            140        with gr.Column(scale=1):141            gr.Markdown("### 📋 Quick Results")142            result = gr.Markdown(143                label="Analysis Results",144                value="*Configure parameters and click 'Run Backtesting' to see results here*"145            )146    147    # GUIDANCE SECTION148    gr.Markdown("---")149    with gr.Row():150        with gr.Column():151            gr.Markdown(152                """153                ### 💡 How to Read the Results:154                - **Dark purple bars**: Days where loss exceeded VaR (exceptions)155                - **Dashed line**: VaR 95% level  156                - **Actual vs Expected**: Compare actual vs theoretical exception counts157                - **Accurate model**: Actual exceptions ≈ Expected exceptions158                - **Optimistic model**: Actual exceptions > Expected exceptions159                - **Conservative model**: Actual exceptions < Expected exceptions160                """161            )162    163    # BACKTESTING RESULTS SECTION - FULL WIDTH BELOW164    gr.Markdown("---")165    gr.Markdown("## 📊 Backtesting Results Visualization")166    plot = gr.Plot(label="Backtesting Analysis", show_label=True)167    168    btn.click(169        fn=backtest_var,170        inputs=[portfolio, test_days, seed],171        outputs=[plot, result]172    )173 174# LAUNCH APPLICATION175if __name__ == "__main__":176    demo.launch()