Retirement Planner

• Test whether a portfolio can fund a target spending plan over a fixed retirement horizon.
• Compare spending rules and see how much stability each one trades for a higher or lower success rate.
• Stress-test a plan against poor historical sequences and Monte Carlo variability before changing allocation or spending.
• Add Social Security, pensions, annuities, and one-time or recurring spending changes that materially affect withdrawal needs.
• Compare several named plans side by side when deciding between different retirement ages, spending levels, or allocations.

The planner simulates retirement one year at a time. In each year, spending is determined by the selected spending rule, income streams offset withdrawals, spending adjustments are applied, and the remaining portfolio grows by that year's real return. Values are shown in constant purchasing-power dollars.

The most effective workflow is:

• Start with a realistic allocation and horizon.
• Add recurring income and any known spending changes.
• Run Both analysis modes for a fast view of historical and Monte Carlo results side by side.
• Use the failure diagnostics, sensitivity sweeps, and plan comparison table to identify the smallest change that improves the plan.

Two analysis modes are available and can be run independently or together:

• Initial portfolio value: starting balance in today's dollars.
• Asset allocation: percentage weights across supported asset classes. You can start from a template or import a compatible workspace portfolio. Weights must sum to 100%.
• Time horizon: retirement start age and end age. This defines the full decumulation window.
• Annual spending: either a fixed real dollar target or, for percent-of-portfolio mode, an initial withdrawal rate.
• Spending rule: how spending adjusts over time.
• Income streams: recurring income that reduces portfolio withdrawals during the years it is active.
• Spending adjustments: one-time or recurring spending changes such as travel, health care, or a mortgage payoff.

Income streams (Social Security, pensions, annuities, or custom recurring income) offset portfolio withdrawals during the years they are active. Each stream has a start age, optional end age, and optional cost-of-living adjustment (COLA).

Spending adjustments model one-time or recurring changes to baseline spending (e.g., a travel budget from age 65 to 75, or a one-time home repair at age 70). Positive amounts increase spending; negative amounts reduce it.

• Start with a clean base case. Use your current planned allocation, retirement age, and spending target before trying optimizations.
• Enter material cashflow offsets. A plan without Social Security, pensions, or large recurring expenses is usually not decision-useful.
• Run Both modes first. If historical and Monte Carlo disagree, treat that as a sign to examine assumptions more closely.
• Use the sensitivity analysis to change one lever at a time. This is the fastest way to see whether spending, retirement age, or allocation matters most.
• Save candidate plans and use plan comparison instead of relying on memory. This is especially useful when comparing a spending cut against a later retirement date.

• Verdict: pass (90%+ success), caution (75-90%), or fail (below 75%).
• Success rate: fraction of scenarios where the portfolio lasted the full plan duration.
• Median terminal value: 50th percentile portfolio balance at plan end.
• Worst-case terminal value: 5th percentile portfolio balance at plan end.
• Spending variability: coefficient of variation of annual spending (shown for the guardrails rule only).
• Failure analysis: table of historical cohorts that failed, showing which start years led to depletion and how many years the portfolio lasted.
• Portfolio fan chart: percentile bands of portfolio value over the plan duration.
• Spending path chart: percentile bands of annual spending over the plan duration.
• Terminal value distribution: histogram of ending portfolio values across all scenarios.
• Failure diagnostics: for weaker plans, the tool estimates a lower spending target, a safer withdrawal rate, and other levers that may improve success.
• Sensitivity analysis: shows how success rate changes when you sweep spending, retirement age, or stock allocation.
• Scenario comparison: compares the current result against saved plans so you can see which change improved the plan most.

• Treating a high success rate as a guarantee. The tool is a stress test, not a forecast.
• Comparing plans with missing income or missing expense changes. Those omissions usually matter more than small allocation changes.
• Overreacting to one metric. Use success rate, terminal wealth, spending variability, and failure timing together.
• Ignoring the shape of the path. Two plans can have similar success rates but very different worst cases and spending volatility.

• Returns are annual real (after-inflation) total returns compiled from standard academic datasets (1926-2024).
• Asset classes beyond US Stocks and US Bonds use simplified approximations based on the primary series.
• Beginning-of-period withdrawal convention: spending is taken at the start of each year, then the portfolio grows by that year's return.
• Monte Carlo draws random years with replacement (bootstrap), which preserves the empirical return distribution but not serial correlation.
• When tax-aware mode is off, taxes, fees, and transaction costs are not modeled. When enabled, simplified flat federal rates are applied to traditional and taxable withdrawals. State tax, NIIT, and progressive brackets are not modeled.