Stress Testing Engine

🎯 Key Features of This Stress Testing Engine

📊 Multi-Asset Support

• Stocks: Market risk, beta exposure, sector sensitivity
• Bonds: Interest rate risk, credit spread risk, duration/convexity effects
• Options: Greek exposures (delta, gamma, vega, theta), volatility risk
• Gold: Safe-haven asset, USD correlation, inflation hedge

🔗 Correlated Shocks

• Cholesky Decomposition: Generate correlated random shocks using matrix factorization
• Risk Factor Correlation Matrix: Pre-defined correlations between equity, rates, volatility, spreads
• Positive Definite Enforcement: Ensures correlation matrix stability
• Monte Carlo Integration: Correlated scenario generation for comprehensive risk assessment

📈 Historical Scenarios

• 2008 Financial Crisis: Market crash, volatility spike, credit spread widening
• 2020 COVID Crisis: Rapid equity decline, rate cuts, safe-haven flows
• Inflation Shock Scenario: Rate hikes, equity pressure, commodity impacts
• Custom Scenarios: User-defined stress events with adjustable parameters

📉 VaR Calculation (Value at Risk)

• Monte Carlo Simulation: 10,000+ correlated scenarios
• 95% and 99% Confidence Levels: Standard regulatory thresholds
• Expected Shortfall (CVaR): Average loss beyond VaR threshold
• Absolute & Relative VaR: Both dollar amounts and percentage losses

🔍 Sensitivity Analysis

• Risk Factor Attribution: Isolate impact of individual factors (equity, rates, vol, etc.)
• Marginal Contribution: Understand each asset's contribution to total risk
• Stress Testing Matrix: Systematic exploration of factor combinations
• What-If Analysis: Test custom shock magnitudes and directions

📊 Visualization

• P&L Distribution Plots: Histograms with VaR thresholds
• Q-Q Plots: Normality assessment of returns
• Correlation Heatmaps: Visualize risk factor relationships
• Scenario Analysis Charts: Compare historical stress impacts
• Tail Risk Profiles: Extreme loss probability analysis

⚡ Advanced Features

• Marginal VaR: Contribution of each position to portfolio VaR
• Liquidity Stress Testing: Incorporate bid-ask spreads and market depth
• Portfolio Decomposition: Asset-level performance under stress
• Worst-Case Scenario Analysis: Extreme but plausible events

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🚀 Enhancement Opportunities

For Real Market Data Integration

# Using yfinance for real-time data (for example -- Extract the data and implement)
import yfinance as yf

# Get historical data for calibration
spy = yf.download('SPY', start='2020-01-01', end='2023-12-31')
tlt = yf.download('TLT', start='2020-01-01', end='2023-12-31')
gld = yf.download('GLD', start='2020-01-01', end='2023-12-31')

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🏗️ Framework Architecture

Portfolio Stress Testing Engine
├── Core Engine
│   ├── Portfolio Manager
│   ├── Risk Factor Model
│   ├── Correlation Generator
│   └── Scenario Builder
├── Calculation Modules
│   ├── VaR Calculator
│   ├── Stress Test Runner
│   ├── Sensitivity Analyzer
│   └── Liquidity Adjuster
├── Data Layer
│   ├── Market Data Interface
│   ├── Historical Database
│   ├── Scenario Repository
│   └── Results Storage
└── Presentation Layer
    ├── Report Generator
    ├── Visualization Engine
    ├── API Endpoints
    └── GUI Interface