Monte Carlo Simulation Integration Analysis

Current State

Implementation: ✅ Advanced but Unused

Core Components:

MonteCarloEngine (/src/alpha_pulse/risk/monte_carlo_engine.py)
- Multiple stochastic processes (GBM, Jump Diffusion, Heston, Variance Gamma)
- Variance reduction techniques
- GPU acceleration support
- Greeks calculation
SimulationService (/src/alpha_pulse/services/simulation_service.py)
- Portfolio simulation orchestration
- Stress testing scenarios
- Model backtesting
- Result caching and parallelization

Integration: ❌ 0% Used in Decision Making

Complete Isolation:

Not used in risk reporting
Portfolio optimization ignores scenarios
No API endpoints
Stress testing disconnected
Zero impact on trading decisions

Critical Integration Gaps

1. Risk Reporting Gap

Current: Risk reports show basic VaR only Impact:

Incomplete risk picture
No tail risk visibility
Missing scenario analysis
Regulatory compliance issues

Required Integration:

# In risk_reporter.py generate_risk_report()
async def generate_risk_report(self, portfolio):
    # Add Monte Carlo simulation
    simulation_report = await self.simulation_service.generate_simulation_report(
        portfolio,
        horizons=[1, 5, 20],  # 1 day, 1 week, 1 month
        confidence_levels=[0.95, 0.99],
        scenarios=10000
    )
    
    report = {
        "traditional_metrics": self._calculate_traditional_metrics(portfolio),
        "monte_carlo_var": simulation_report.var_estimates,
        "monte_carlo_cvar": simulation_report.cvar_estimates,
        "tail_risk_analysis": simulation_report.tail_statistics,
        "scenario_analysis": simulation_report.scenario_results,
        "stress_test_results": simulation_report.stress_test_results
    }
    
    return RiskReport(**report)

2. Portfolio Optimization Gap

Current: Optimization uses historical data only Impact:

Fragile to future scenarios
No robustness testing
Overfits to past data
Surprises in new regimes

Required Integration:

# In portfolio_optimizer.py optimize()
async def optimize_with_scenarios(self, portfolio, constraints):
    # Generate future scenarios
    scenarios = await self.simulation_service.generate_portfolio_scenarios(
        portfolio,
        num_scenarios=1000,
        horizon=20,  # days
        process_type="regime_switching"
    )
    
    # Robust optimization across scenarios
    optimal_weights = self._solve_robust_optimization(
        scenarios,
        constraints,
        objective="worst_case_sharpe"  # or "cvar_minimization"
    )
    
    # Test robustness
    backtest_results = await self.simulation_service.backtest_strategy(
        optimal_weights,
        scenarios
    )
    
    return OptimizationResult(
        weights=optimal_weights,
        expected_performance=backtest_results.summary_stats,
        scenario_analysis=backtest_results.scenario_breakdown
    )

3. API Endpoint Gap

Current: No simulation endpoints Impact:

Cannot run what-if analysis
No stress testing interface
Hidden simulation capabilities
No user scenario exploration

Required Endpoints:

# In new /api/routers/simulation.py
@router.post("/portfolio-simulation")
async def run_portfolio_simulation(request: SimulationRequest):
    """Run Monte Carlo simulation for portfolio"""
    results = await simulation_service.simulate_portfolio(
        request.portfolio,
        request.horizons,
        request.num_scenarios,
        request.process_type
    )
    return results

@router.post("/stress-test")
async def run_stress_test(request: StressTestRequest):
    """Run stress test scenarios"""
    results = await simulation_service.run_stress_tests(
        request.portfolio,
        request.scenarios  # e.g., "2008_crisis", "covid_crash", "custom"
    )
    return results

@router.post("/option-pricing")
async def price_options(request: OptionRequest):
    """Price options using Monte Carlo"""
    greeks = await simulation_service.calculate_option_greeks(
        request.underlying,
        request.strike,
        request.expiry,
        request.option_type
    )
    return greeks

@router.post("/what-if")
async def what_if_analysis(request: WhatIfRequest):
    """Run custom what-if scenarios"""
    results = await simulation_service.run_custom_scenario(
        request.portfolio,
        request.market_shocks,
        request.correlation_changes
    )
    return results

4. Decision Integration Gap

Current: Decisions ignore simulation insights Impact:

Blind to tail risks
No scenario-based sizing
Reactive not proactive
Surprises cost money

Required Integration:

# In risk_manager.py evaluate_trade()
async def evaluate_trade_with_simulation(self, trade):
    # Simulate trade impact
    simulation_result = await self.simulation_service.simulate_trade_impact(
        self.portfolio,
        trade,
        scenarios=1000
    )
    
    # Check tail risk
    if simulation_result.tail_risk_increase > self.max_tail_risk:
        return RiskDecision(
            approved=False,
            reason=f"Trade increases tail risk by {simulation_result.tail_risk_increase:.1%}"
        )
    
    # Check scenario performance
    worst_case = simulation_result.get_percentile_outcome(5)  # 5th percentile
    if worst_case.portfolio_loss > self.max_scenario_loss:
        return RiskDecision(
            approved=False,
            reason=f"Worst case loss {worst_case.portfolio_loss:.1%} exceeds limit"
        )
    
    return RiskDecision(
        approved=True,
        simulation_metrics=simulation_result.summary
    )

5. Stress Testing Gap

Current: Basic stress test disconnected from Monte Carlo Impact:

Limited scenario coverage
No probabilistic outcomes
Cannot test complex scenarios
Regulatory inadequacy

Required Integration:

# Merge stress_tester.py with simulation
class UnifiedStressTester:
    def __init__(self, simulation_service):
        self.simulation = simulation_service
        
    async def run_comprehensive_stress_test(self, portfolio):
        # Historical scenarios
        historical = await self.simulation.run_stress_tests(
            portfolio,
            scenarios=["2008_crisis", "covid_crash", "dot_com_burst"]
        )
        
        # Hypothetical scenarios
        hypothetical = await self.simulation.run_custom_scenarios(
            portfolio,
            [
                {"name": "rates_spike", "shocks": {"rates": +0.03}},
                {"name": "liquidity_crisis", "shocks": {"liquidity": -0.5}},
                {"name": "correlation_breakdown", "correlation_override": 0.9}
            ]
        )
        
        # Reverse stress testing
        breaking_point = await self.simulation.find_breaking_point(
            portfolio,
            target_loss=0.25  # Find scenario causing 25% loss
        )
        
        return StressTestReport(
            historical_scenarios=historical,
            hypothetical_scenarios=hypothetical,
            breaking_points=breaking_point
        )

Business Impact

Current State (Unused)

Risk Visibility: Basic metrics only
Scenario Planning: None
Stress Testing: Limited
Decision Quality: Historical-based only

Potential State (Integrated)

Complete Risk Picture: Full distribution of outcomes
Proactive Planning: Test strategies across scenarios
Robust Decisions: Scenario-aware position sizing
Regulatory Compliance: Advanced risk metrics

Annual Value

Avoided Losses: $1-2M from better tail risk management
Improved Decisions: $500K-1M from scenario planning
Regulatory Compliance: Avoid fines/restrictions
Total: $1.5-3M annually

Integration Architecture

graph TD
    A[Monte Carlo Engine] --> B[Simulation Service]
    
    B --> C[Risk Reporting]
    B --> D[Portfolio Optimization]
    B --> E[Stress Testing]
    B --> F[Trade Evaluation]
    
    G[API Layer] --> B
    
    C --> H[Risk Dashboard]
    D --> I[Rebalancing]
    E --> J[Compliance Reports]
    F --> K[Position Sizing]

Implementation Roadmap

Phase 1: Service Activation (1 day)

Start SimulationService in API
Configure simulation parameters
Test basic functionality

Phase 2: Risk Integration (3 days)

Add Monte Carlo to risk reports
Integrate with trade evaluation
Create simulation-based alerts

Phase 3: API Development (2 days)

Create simulation endpoints
Add stress test interface
Build what-if analysis tools

Phase 4: Decision Integration (3 days)

Connect to portfolio optimization
Add scenario-based sizing
Integrate stress test results

Configuration Schema

monte_carlo:
  enabled: true
  default_scenarios: 10000
  default_horizon: 20  # days
  
  processes:
    default: "gbm"
    regime_aware: "regime_switching"
    fat_tails: "variance_gamma"
    
  variance_reduction:
    antithetic: true
    control_variates: true
    importance_sampling: false
    
  gpu:
    enabled: true
    batch_size: 100000
    
  caching:
    enabled: true
    ttl: 3600  # 1 hour
    
  risk_metrics:
    var_levels: [0.95, 0.99]
    cvar_levels: [0.95, 0.99]
    stress_scenarios: ["2008", "covid", "custom"]

Success Metrics

Simulation Usage: Daily simulation runs
Risk Accuracy: Predicted vs actual tail events
Decision Impact: % of trades using simulation
Stress Test Coverage: Scenarios tested monthly
User Engagement: What-if analyses performed

Conclusion

The Monte Carlo simulation system is a powerful analytical engine running in neutral. It’s like having a flight simulator that’s never used for pilot training. With 9 days of integration work, we can transform static risk analysis into dynamic, forward-looking risk management that could prevent millions in losses while enabling more confident decision-making.