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After running a simulation, Drift presents comprehensive results from 100,000 Monte Carlo scenarios. This guide explains how to interpret each metric and make informed decisions.

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Success Probability

The most important metric: percentage of scenarios where you reached your goal.

High Confidence

75-100%You’re on track. Most scenarios achieve the goal. Focus on staying consistent.

Moderate Risk

50-75%Uncertain outcome. Consider adjusting spending, income, or timeline to improve odds.

High Risk

0-50%Goal unlikely with current plan. Significant changes needed - see what-if scenarios.

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How It’s Calculated

// From simulationService.ts
const successCount = finalBalances.filter(
  (balance) => balance >= goal.targetAmount
).length

const successProbability = successCount / nSimulations
// Example: 65,000 successes / 100,000 simulations = 0.65 (65%)
Screenshot description: Large card showing “65%” in bold with subtitle “of reaching goal” and a circular progress indicator.

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Outcome Distribution

Drift shows five key percentiles representing the range of possible outcomes:
PercentileMeaningUse Case
10th (Pessimistic)Only 10% of scenarios worseWorst-case planning
25thLower quartileConservative estimate
50th (Median)Expected outcomeMost likely result
75thUpper quartileOptimistic estimate
90th (Optimistic)Only 10% of scenarios betterBest-case planning

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Results Table

Screenshot description: Table with three columns:
PercentileOutcomevs Goal
10th (pessimistic)$38,250🔴 -$11,750
25th$42,100🔴 -$7,900
50th (expected)$47,500🔴 -$2,500
75th$53,200🟢 +$3,200
90th (optimistic)$61,800🟢 +$11,800

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Interpreting the Distribution

Example: 10th = 45K,90th=45K, 90th = 55K (goal: $50K)Interpretation:
  • Predictable outcome
  • Low spending volatility
  • Stable income
  • Short timeline or low investment exposure
Action: Stick to the plan, outcome is fairly certain.
Example: 10th = 25K,90th=25K, 90th = 85K (goal: $50K)Interpretation:
  • High uncertainty
  • Variable spending or irregular income
  • Long timeline with market volatility
  • Aggressive investment allocation
Action: Consider reducing risk if the goal is time-sensitive, or embrace volatility for long-term goals.
Example: 10th = 42K,50th=42K, 50th = 48K, 90th = 72K(goal:72K (goal: 50K)Interpretation:
  • Asymmetric risk (upside potential > downside risk)
  • Investment growth creates right-tail outcomes
  • Normal for equity-heavy portfolios
Action: Stay the course - you have limited downside but significant upside.

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Outcome Chart

The area chart visualizes how percentiles evolve over time: Screenshot description:
  • X-axis: Time in months (0 to 36)
  • Y-axis: Dollar amount (0to0 to 70K)
  • Five colored bands representing p10, p25, p50, p75, p90
  • Median line (p50) is highlighted in blue
  • Red dashed horizontal line at goal amount ($50K)
  • Bands widen over time (increasing uncertainty)
// From ResultsChart.tsx
<AreaChart data={data}>
  <Area dataKey="p90" fill="var(--border-primary)" />
  <Area dataKey="p75" fill="var(--border-primary)" />
  <Area dataKey="p50" stroke="var(--accent)" fill="url(#fillGradient)" strokeWidth={2} />
  <Area dataKey="p25" fill="var(--border-primary)" />
  <Area dataKey="p10" fill="var(--border-primary)" />
  
  <ReferenceLine y={goalAmount} stroke="var(--error)" strokeDasharray="4 4" />
</AreaChart>

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Reading the Chart

1

Current Position (Month 0)

The leftmost point shows your starting balance (liquid assets - debt).
2

Growth Trajectory

The median line (p50) represents the most likely path. If it crosses above the goal line, you’re on track.
3

Uncertainty Cone

The widening bands show increasing uncertainty over time. Longer timelines = more variability.
4

Goal Reference

The horizontal dashed line marks your target. Count how many percentile bands cross above it.

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Summary Metrics

Four key cards summarize the simulation: 
successProbability: 0.65  // 65%
Percentage of simulations that reached the goal amount.Display: 65% with subtitle “of reaching goal”
Screenshot description: Four cards in a row, each showing a large number with icon and subtitle. Gap card is highlighted in red indicating a shortfall.

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Account Analysis

The results page shows what data was used:

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Account Snapshot

// From results/page.tsx - financialProfile
{
  monthlyIncome: 6500,       // Salary + deposits from transactions
  monthlySpending: 4200,     // Average expenses (last 90 days)
  liquidAssets: 12500,       // Checking + savings balances
  creditDebt: 3250,          // Credit card balances
  loanDebt: 28500,           // Auto + student loans
  monthlyLoanPayments: 650   // Required debt payments
}
Screenshot description: Grid showing four bordered sections:
  1. Monthly Income: $6,500 (blue left border)
  2. Monthly Spending: $4,200 ± 12% volatility (orange left border)
  3. Liquid Assets: $12,500 (green left border)
  4. Debt: $31,750 total, $650/mo payments (red left border)

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Model Assumptions

Drift uses these parameters in every simulation run: 
// From results/page.tsx - HARDCODED_ASSUMPTIONS
{
  annualReturnMean: 0.07,              // 7% average investment returns
  annualReturnStd: 0.15,               // 15% volatility
  inflationRate: 0.025,                // 2.5% annual inflation
  inflationVolatility: 0.01,           // 1% inflation variance
  annualRaiseMean: 0.03,               // 3% annual raise
  annualRaiseFrequency: 'annual',      // Once per year
  promotionProbabilitySemiAnnual: 0.08,// 8% chance of promotion every 6mo
  promotionRaiseMean: 0.06,            // 6% raise from promotion
  emergencyProbabilityMonthly: 0.08,   // 8% monthly chance of emergency
  emergencyAmountRange: '$500 - $2,000', // Random emergency costs
  incomeVolatility: 0.05,              // 5% income variation
  expenseVolatility: 0.12              // 12% expense variation
}
Screenshot description: Grid showing six bordered sections with assumption details:
  1. Investment Returns: 7.0% ± 15.0% annualized
  2. Inflation: 2.5% volatility 1.0%
  3. Income Growth: Raises 3.0% annual, Promo 8.0% semi-annual +6.0%
  4. Emergency Events: 8.0% monthly, Amount 500−500-2,000
  5. Volatility: Income 5.0%, Expenses 12.0%
  6. Simulation Scale: 100,000 Monte Carlo runs

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Understanding Monte Carlo Simulation

Each of the 100,000 simulations runs independently:

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Single Simulation Flow

1

Initialize Balance

Start with: liquidAssets - creditDebt = $12,500 - $3,250 = $9,250
2

Loop Through Each Month

For month 1 to 36:
  1. Generate random income (mean ± volatility)
  2. Generate random spending (mean ± volatility)
  3. Random emergency event (8% chance → 500−500-2,000)
  4. Investment returns (7% annual ÷ 12 months ± variance)
  5. Update balance: balance += income - spending - emergency + returns
3

Record Final Balance

After month 36, store the ending balance: $47,234
4

Repeat 99,999 More Times

Run 100,000 independent simulations with different random variations
5

Analyze Distribution

Sort all 100,000 final balances and calculate percentiles, success rate, mean, std dev

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Example Simulation Variations

// Simulation #1: Everything goes well
Month 1: +$6,700 income, -$4,050 spending, no emergency, +$55 returns = +$2,705
Month 2: +$6,450 income, -$4,150 spending, no emergency, +$62 returns = +$2,362
... → Final: $58,400 ✓ (Above goal)

// Simulation #2: Bad luck
Month 1: +$6,200 income, -$4,500 spending, -$1,200 emergency, +$48 returns = +$548
Month 2: +$6,300 income, -$4,600 spending, no emergency, +$51 returns = +$1,751
... → Final: $41,200 ✗ (Below goal)

// Simulation #3: Average outcome
Month 1: +$6,500 income, -$4,200 spending, no emergency, +$58 returns = +$2,358
Month 2: +$6,550 income, -$4,180 spending, no emergency, +$61 returns = +$2,431
... → Final: $47,500 ~ (Close to median)

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Spending Breakdown

Results show your top spending categories for targeted optimization: Screenshot description: Spending by Category section showing horizontal bars:
  • Food & Dining: $450/mo (35% of discretionary)
  • Shopping: $320/mo (25%)
  • Transportation: $280/mo (22%)
  • Entertainment: $180/mo (14%)
  • Utilities: $210/mo (fixed)
Drift automatically identifies non-essential categories (dining, entertainment, shopping) for what-if scenario analysis.

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Statistical Measures

For advanced users, Drift also calculates: 
{
  mean: 48100,           // Average outcome across all simulations
  std: 8750,             // Standard deviation (measure of spread)
  worstCase: 18500,      // Minimum outcome (0th percentile)
  bestCase: 89200        // Maximum outcome (100th percentile)
}

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Standard Deviation Interpretation

Example: Goal 50K,std=50K, std = 4KVery predictable. Outcome will be close to the mean in nearly all scenarios.

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Red Flags to Watch

Concerning Patterns:
  • Success probability < 50% → Goal needs significant adjustment
  • Median outcome < 75% of goal → Shortfall likely
  • Wide percentile spread (p90/p10 > 2.5x) → Too much uncertainty
  • Negative 10th percentile → Risk of losing money
  • Gap widening over time → Current strategy unsustainable

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Next Steps After Viewing Results

1

If Success Probability ≥ 75%

You’re on track! Maintain current habits and rerun simulations quarterly to verify.
2

If Success Probability 50-75%

Review what-if scenarios to find highest-impact adjustments (spending cuts, income increases, timeline extension).
3

If Success Probability < 50%

Major changes needed. Consider:
  • Reducing goal amount
  • Extending timeline by 6-12 months
  • Cutting discretionary spending by 20-30%
  • Increasing income through side work or raises

What-If Scenarios

Explore how changes to your plan affect success probability

Setting Goals

Refine your goal with better parameters

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