Risk-Based Retirement Guardrails Simulator Beta

This simulator models a retirement plan that adjusts spending when risk crosses preset guardrails. It starts with your chosen withdrawal rate (or tunes itself to an 80% success rate) and runs thousands of Monte Carlo simulations using a 60/40 stock–bond portfolio in real, inflation-adjusted dollars. Spending can rise when your plan’s success improves—or tighten if markets fall—to keep your income sustainable through retirement.

How it works (quick summary)

Simulates 1,000+ random return paths based on historical 60/40 volatility.
Uses real (after-inflation) returns for clarity on long-term purchasing power.
Begins at your chosen or tuned withdrawal rate (≈80% success target).
Guardrails trigger when success rises above ~99% (raise spending) or falls below ~25% (cut spending).
Shows the probability your income plan stays viable across market conditions.

Inputs

Manual by % Manual by $ Tune to 80% success

Initial Portfolio ($)

Initial Withdrawal Rate (%)

Retirement Length (years)

Starting Withdrawal ($/yr, from portfolio)

Annual Pension ($)

Pension COLA (% nominal)

Social Security Amount

SS Start Year (0 = now)

SS COLA (% nominal)

CPI (% nominal)

One-Time Deposit ($)

Deposit Year (0 = now)

Monte Carlo Paths

Repeatability code (optional)

Apply Guardrails During Runs?

Guardrail policy (risk-based): Start at 80% success (or use your manual %). If success rises to ~100%, increase spending to an 80%-success plan. If success falls to 25%, cut to a 45%-success plan.

Results

StatusAwaiting run…

Ready.

Model: All figures in today’s dollars (real). Portfolio is a 60/40 stocks/bonds mix using correlated, lognormal annual returns (stocks 6%±18%, bonds 0%±6%, ρ=0.10) with annual rebalancing. Withdrawals are modeled in real (inflation-adjusted) dollars, meaning your income maintains purchasing power over time.; pension grows by real COLA (nominal COLA − CPI). Results are simulated and not guarantees; taxes, fees, and extreme tail events are not fully modeled.