Sensitivity Analysis & What-If Scenarios

def run_sensitivity_analysis(request: SimulationRequest) -> SensitivityAnalysis:
    """Run sensitivity analysis by varying key parameters."""
    
    # Run base simulation
    base_results = run_monte_carlo(request, n_workers=2)
    base_probability = base_results.success_probability
    
    # Define scenarios as (attribute_path, field, multiplier_or_delta) tuples
    scenarios = {
        "income_plus_10": ("user_inputs", "monthly_income", 1.1, "multiply"),
        "income_minus_10": ("user_inputs", "monthly_income", 0.9, "multiply"),
        "spending_minus_10": ("financial_profile", "monthly_spending", 0.9, "multiply"),
        "spending_minus_20": ("financial_profile", "monthly_spending", 0.8, "multiply"),
        "spending_plus_10": ("financial_profile", "monthly_spending", 1.1, "multiply"),
        "timeline_plus_6mo": ("goal", "timeline_months", 6, "add"),
        "timeline_plus_12mo": ("goal", "timeline_months", 12, "add"),
    }

interface SensitivityAnalysis {
  baseProbability: number  // Original success probability
  sensitivities: {
    [scenarioName: string]: {
      delta: number                // Change in probability (-1 to +1)
      newProbability: number       // Updated success probability
      impact: number               // Absolute value of delta
    }
  }
  mostImpactful: string          // Scenario name with largest impact
  recommendations: string[]      // Actionable advice
}

{
  "monthlyIncome": 6500,
  "monthlySpending": 4800,
  "targetAmount": 500000,
  "timelineMonths": 180,
  "successProbability": 0.58
}

{
  "baseProbability": 0.58,
  "sensitivities": {
    "income_plus_10": {
      "delta": 0.12,
      "newProbability": 0.70,
      "impact": 0.12
    },
    "income_minus_10": {
      "delta": -0.11,
      "newProbability": 0.47,
      "impact": 0.11
    },
    "spending_minus_10": {
      "delta": 0.15,
      "newProbability": 0.73,
      "impact": 0.15
    },
    "spending_minus_20": {
      "delta": 0.28,
      "newProbability": 0.86,
      "impact": 0.28
    },
    "spending_plus_10": {
      "delta": -0.14,
      "newProbability": 0.44,
      "impact": 0.14
    },
    "timeline_plus_6mo": {
      "delta": 0.08,
      "newProbability": 0.66,
      "impact": 0.08
    },
    "timeline_plus_12mo": {
      "delta": 0.16,
      "newProbability": 0.74,
      "impact": 0.16
    }
  },
  "mostImpactful": "spending_minus_20",
  "recommendations": [
    "Reducing spending by 10% could improve your success probability by 15% (to 73%).",
    "Increasing income by 10% (raise, side gig) could boost your odds by 12%.",
    "Extending your timeline by 6 months improves probability to 66%."
  ]
}

def generate_recommendations(
    sensitivities: Dict[str, SensitivityResult],
    base_probability: float
) -> List[str]:
    """Generate actionable recommendations based on sensitivity analysis."""
    recommendations = []
    
    # Check spending impact
    spending_impact = sensitivities.get("spending_minus_10", SensitivityResult(delta=0, new_probability=0, impact=0))
    if spending_impact.impact > 0.05:
        recommendations.append(
            f"Reducing spending by 10% could improve your success probability by "
            f"{spending_impact.impact:.0%} (to {spending_impact.new_probability:.0%})."
        )
    
    # Check income impact
    income_impact = sensitivities.get("income_plus_10", SensitivityResult(delta=0, new_probability=0, impact=0))
    if income_impact.impact > 0.05:
        recommendations.append(
            f"Increasing income by 10% (raise, side gig) could boost your odds by "
            f"{income_impact.impact:.0%}."
        )
    
    # Check timeline impact
    timeline_impact = sensitivities.get("timeline_plus_6mo", SensitivityResult(delta=0, new_probability=0, impact=0))
    if timeline_impact.impact > 0.05:
        recommendations.append(
            f"Extending your timeline by 6 months improves probability to "
            f"{timeline_impact.new_probability:.0%}."
        )
    
    # Low probability warning
    if base_probability < 0.5:
        recommendations.append(
            "Your current plan has less than 50% success probability. "
            "Consider adjusting your goal, timeline, or savings rate."
        )
    
    # High probability encouragement
    if base_probability > 0.8:
        recommendations.append(
            "You're on track! Your current plan has strong odds of success. "
            "Stay consistent with your savings."
        )
    
    return recommendations

interface WhatIfScenario {
  name: string
  description: string
  savingsPerMonth: number
  projectedSuccessProbability: number
  implementationTips: string[]
}

const spending = baseRequest.financialProfile.spendingByCategory
const monthlySpending = Object.fromEntries(
  Object.entries(spending).map(([k, v]) => [k, v / 12])
)

const categories = Object.entries(monthlySpending).sort((a, b) => b[1] - a[1])
const nonEssential = categories.filter(([name]) => {
  const lower = name.toLowerCase()
  return ['dining', 'restaurant', 'entertainment', 'shopping', 'travel', 
          'subscription', 'coffee', 'bar', 'alcohol'].some(k => lower.includes(k))
})

// Scenario 1: Cut largest discretionary category
if (nonEssential.length > 0) {
  const [name, amount] = nonEssential[0]
  const cutPercent = Math.min(0.5, gap / amount)
  const savingsPerMonth = amount * cutPercent
  
  const modifiedRequest = JSON.parse(JSON.stringify(baseRequest))
  modifiedRequest.financialProfile.spendingByCategory[name] = (amount * (1 - cutPercent)) * 12
  modifiedRequest.financialProfile.monthlySpending -= savingsPerMonth
  
  const results = await this.runSimulation(modifiedRequest)
  scenarios.push({
    name: `Reduce ${name}`,
    description: `Cut ${name} by ${Math.round(cutPercent * 100)}% (save $${Math.round(savingsPerMonth)}/mo from $${Math.round(amount)}/mo)`,
    savingsPerMonth,
    projectedSuccessProbability: results.successProbability,
    implementationTips: this.getTips(name),
  })
}

private getTips(category: string): string[] {
  const lower = category.toLowerCase()
  
  if (lower.includes('dining') || lower.includes('restaurant') || lower.includes('food')) {
    return [
      'Cook at home 4-5 times per week',
      'Pack lunch for work instead of buying',
      'Meal prep on Sundays for the week',
      'Use grocery delivery instead of restaurants',
    ]
  }
  
  if (lower.includes('entertainment') || lower.includes('shopping')) {
    return [
      'Unsubscribe from unused streaming services',
      'Cancel unnecessary subscriptions',
      'Set a weekly shopping budget',
      'Use 30-day rule for non-essentials',
    ]
  }
  
  if (lower.includes('travel')) {
    return [
      'Plan trips during off-season',
      'Use rewards points and miles',
      'Travel locally more often',
      'Split accommodations with friends',
    ]
  }
  
  return [
    'Track spending in this category weekly',
    'Set a specific budget limit',
    'Find alternatives or substitutes',
    'Gradually reduce over time',
  ]
}

{
  "currentSuccessProbability": 0.58,
  "gap": 800,  // Need to save $800/mo more to reach 75% probability
  "spendingByCategory": {
    "Dining": 7200,      // $600/mo
    "Shopping": 4800,    // $400/mo
    "Entertainment": 3600 // $300/mo
  }
}

{
  "scenarios": [
    {
      "name": "Reduce Dining",
      "description": "Cut Dining by 50% (save $300/mo from $600/mo)",
      "savingsPerMonth": 300,
      "projectedSuccessProbability": 0.68,
      "implementationTips": [
        "Cook at home 4-5 times per week",
        "Pack lunch for work instead of buying",
        "Meal prep on Sundays for the week",
        "Use grocery delivery instead of restaurants"
      ]
    },
    {
      "name": "Balance cuts across categories",
      "description": "Reduce Dining, Shopping, Entertainment to save $800/mo total",
      "savingsPerMonth": 800,
      "projectedSuccessProbability": 0.76,
      "implementationTips": [
        "Identify your most flexible categories",
        "Set specific spending limits",
        "Track weekly spending",
        "Build in occasional treats to stay motivated"
      ]
    },
    {
      "name": "Increase income",
      "description": "Earn $80/mo more (1.2% raise or side income)",
      "savingsPerMonth": 80,
      "projectedSuccessProbability": 0.61,
      "implementationTips": [
        "Ask for a raise or promotion",
        "Develop side hustle (5-10 hrs/week)",
        "Freelance in your skill area",
        "Negotiate better salary at next job"
      ]
    }
  ]
}

POST /api/simulation/sensitivity
{
  // Same structure as simulation request
  "financialProfile": { ... },
  "userInputs": { ... },
  "goal": { ... }
}

POST /api/whatif/scenarios
{
  "request": { /* SimulationRequest */ },
  "currentSuccessProbability": 0.58,
  "gap": 800  // Monthly savings gap to reach target probability
}

router.post('/scenarios', async (req, res) => {
  try {
    const { request, currentSuccessProbability, gap } = req.body
    
    if (!request || typeof currentSuccessProbability !== 'number' || typeof gap !== 'number') {
      return res.status(400).json({
        error: 'Missing required parameters: request, currentSuccessProbability, gap'
      })
    }
    
    const scenarios = await whatIfService.generateScenarios(
      request as SimulationRequest,
      currentSuccessProbability,
      gap
    )
    
    res.json(scenarios)
  } catch (error) {
    console.error('Error generating what-if scenarios:', error)
    res.status(500).json({ error: 'Failed to generate scenarios' })
  }
})