Investment Simulator — User Guide

The simulator takes a starting balance, a monthly contribution amount, and a date range, then replays what would have happened to your money across up to six parallel investment scenarios using actual historical monthly returns from Yahoo Finance. All scenarios share the same inputs so differences in outcome are attributable entirely to strategy and cost.

The result is a chart of portfolio value over time for each scenario, a set of stat cards summarising final value, total contributions, total gain, annualised return (CAGR), fees paid, and after-tax wealth, and a plain-English fee-drag callout that quantifies the cost of advisory and fund expenses in dollar terms.

Four scenarios are always shown; two momentum scenarios can be toggled on.

Portfolio inputs

Portfolio configuration

Advisor cost settings

Display options

Crash annotations

Vertical dashed lines mark three major market events — the Dot-com peak (March 2001), the 2008 Financial Crisis (September 2008), and the COVID-19 crash (February 2020). They snap to the nearest available monthly date in the selected window; they disappear automatically if your date range doesn't include them.

Yield curve inversion shading

Red-tinted bands cover every period when the 10-year Treasury yield fell below the 2-year yield (negative T10Y2Y spread, sourced from FRED). An inverted yield curve has historically preceded recessions by 6–24 months — so inversions visible in the chart provide context for subsequent drawdowns. The momentum scenarios also use this signal: if the curve was inverted at any point in the prior 12 months, the advisor shifts a portion of equity allocation to bonds.

Interactive legend & tooltips

Hovering a legend entry shows a popup table of the specific fund tickers and asset class for that scenario. Clicking a legend entry toggles that line's visibility. The stat-card checkboxes in the bottom panel also toggle individual lines directly. Hovering anywhere on the chart shows a crosshair tooltip with all scenario values for that month, sorted highest-to-lowest.

• Cost of advice on an index portfolio Compare Scenario 1 (Low-Cost Index) with Scenario 2 (Fee-Adjusted Index). The gap is the pure dollar cost of paying an advisor to manage an otherwise identical index portfolio. The fee-drag callout at the bottom of the page states this gap explicitly. Try lowering the AUM fee for Scenario 2 to reflect the lower rates that index-focused advisors often charge.
• Active vs. passive management Compare Scenario 1 (Low-Cost Index) with Scenario 3 (Actively Managed, no advisor). This isolates the performance difference between index funds and the selected active fund family without conflating it with advisory fees.
• Full cost of active management plus advice Compare Scenario 1 (Low-Cost Index) with Scenario 4 (Fee-Adjusted Active). This is the most common real-world comparison: an investor going it alone with index funds versus paying a wealth manager who uses active funds.
• Does momentum-based rotation beat buy-and-hold? Toggle on momentum (Scenarios 5 & 6) and compare them to their respective buy-and-hold equivalents (Scenarios 1 & 3). Both momentum scenarios carry the same AUM fee as Scenario 2/4, so any outperformance represents skill above cost.
• Era sensitivity Switch between Modern (2011+) and Pre-ETF (1987+) eras to see whether the conclusions hold across different market regimes — including the 1990s bull run, the dot-com bust, the 2008 crisis, and the post-GFC recovery.
• Date-range sensitivity Use the start/end date pickers to zoom into specific periods — e.g., a bear market only, or a single bull cycle — and see whether the relative ranking of strategies is stable or depends heavily on the window chosen.
• Stock/bond allocation effect Drag the stock/bond slider across the full range to see how allocation alone explains performance differences and whether advisor costs matter more at higher or lower equity exposures.
• Tax drag by strategy In Taxable mode, compare the "After-Tax Value" rows across stat cards. Strategies that rebalance more frequently (With Advisor / Momentum) realise gains annually and pay tax sooner, while buy-and-hold strategies defer the entire tax bill to liquidation — this difference can be material over long horizons. To isolate the tax impact of rebalancing frequency alone, see the Rebalancing Impact page.
• Real vs. nominal wealth Toggle inflation adjustment to see how much of apparent portfolio growth was offset by rising prices — particularly useful when spanning decades of data.

Below the chart, each scenario has a stat card showing six to nine fields depending on account type. The card's checkbox also controls that scenario's chart line.

The simulator works with a fixed, curated set of funds chosen to be representative of broad investment approaches — not to model any particular investor's portfolio or to identify top-performing funds. The goal is to illustrate structural differences between strategies (passive vs. active, self-directed vs. advisor-managed, buy-and-hold vs. momentum rotation) using funds that are well-known, widely held, and have long enough histories to support multi-decade backtests.

Results should be read as evidence about strategies, not as endorsements of the specific funds shown. Swapping in comparable funds from the same category would likely produce similar directional conclusions — but different numbers. The universe is deliberately small so that the simulations remain legible and the cost/return trade-offs stay visible, rather than getting lost in fund-selection noise.

Index funds — two eras

The three-fund DIY portfolio changes with the selected era. Modern ETFs begin in late 2011 (limited by VXUS inception); Pre-ETF mutual funds extend history to 1987.

Active fund families

Four families are available in Modern era; two families in Pre-ETF era (plus T. Rowe Price, which spans both). Each family contributes one US equity, one international equity, and one bond fund — a structure chosen to mirror the three-fund index portfolio and keep the stock/bond split parameter meaningful across all scenarios. Families were selected for long operating histories, broad name recognition, and coverage of different management styles (growth, value, international).

Momentum rotation universe

When momentum scenarios are enabled, the advisor selects each year from a wider pool of funds than the three used in the buy-and-hold scenarios. This pool is era-specific. Sector funds are included to give the momentum strategy more opportunity to rotate into assets with different return drivers — real estate, energy, health care, and technology — while the bond selection shifts each year to whichever bond fund had the best trailing return.

The behavior gap

Every scenario here assumes the investor follows their chosen strategy faithfully — contributing regularly, rebalancing on schedule, and never panic-selling. In practice, this is one of the hardest things to do. Research consistently shows that individual investors underperform the very funds they invest in, because they tend to buy after gains and sell after losses.

This creates a wide real-world spectrum that the simulator doesn't capture. At one end, the undisciplined DIY investor — panic-selling in downturns, buying back near peaks, triggering unnecessary capital gains — is likely to underperform every scenario shown here, often significantly. At the other end, an advisor with conflicts of interest — non-fiduciary, churning accounts, chasing trading commissions, recommending products for personal benefit rather than client outcomes — is also likely to deliver results worse than any modeled scenario. Both failure modes are genuinely common, though fiduciary advisors (who are legally required to act in the client's interest) are not subject to the latter concern.

The scenarios here represent a well-behaved middle band: a disciplined investor working, where applicable, with an advisor acting in good faith — and one whose role is captured by only two of the many tools advisors traditionally bring: fund selection and fee structure. If your concern is whether you (or an advisor) will actually execute a strategy as modeled, or whether an advisor's full range of services is worth their cost, this tool can't answer those questions.

What advisors can add that isn't modeled

The simulator captures two things an advisor does: select funds (active vs. index) and charge a fee. A good advisor may also provide:

• Behavioral coaching Keeping clients invested through downturns may be the single most valuable thing an advisor does. One prevented panic sale in a major bear market can outweigh years of advisory fees — an effect this simulator cannot quantify.
• Tax, estate, and financial planning Tax-loss harvesting, Roth conversion strategies, insurance review, estate planning, and goal-setting are outside the scope of this tool but can represent substantial value depending on an investor's situation.
• Structure and accountability Some investors genuinely benefit from the discipline that a professional relationship provides — the knowledge that someone is watching and will call if things drift. That has real value for some temperaments and none for others.
• Dynamic strategy and responsiveness to changing conditions The simulator runs a fixed strategy through historical data without adapting along the way. A real advisor may actively adjust in response to conditions the sim treats as invisible: shifts in the macroeconomic environment (interest rate cycles, inflation regimes, recession signals), changes in tax law that open or close planning opportunities, or adjustments to withdrawal sequencing as a client approaches or enters retirement. Whether these adjustments add value consistently is debated — but they represent a category of advisor activity that this simulator makes no attempt to model.

Taken together, the simulator models two specific advisor behaviors — fund selection and fee structure — out of a much broader toolkit. It is a useful lens for understanding the cost and return implications of those two factors, but it should not be read as a comprehensive picture of what an advisor does or doesn't contribute. Whether those contributions justify the cost depends on the individual investor's situation, temperament, and the specific advisor involved.

Past performance and regime risk

In many of the historical windows available in this simulator, active funds and advisor-mediated strategies have performed competitively or better than the low-cost index baseline. Before drawing strong conclusions from this, it's worth considering a few things.

Active managers and advisors who claim predictive expertise may be "tuned" to the particular market conditions of a given era — specific interest rate regimes, sector leadership patterns, or volatility dynamics. When those conditions change, strategies that performed well may not continue to do so. The familiar caveat that "past performance does not predict future results" applies as much to investment strategies and advisor approaches as it does to individual funds.

The simulator's historical windows (1987–present and 2011–present) include prolonged bull markets, extended low-rate environments, and specific sector dynamics that may not repeat. Results might look quite different over other periods — or over the period ahead. The simulator's comparisons are most reliable as a measure of cost differences, which are structural and predictable. Return differences between active and passive strategies are real in the historical data, but their persistence is genuinely uncertain.

If active strategies appear to do well in a simulation, that's worth noting — but it's a reason to look more carefully, not a reason to expect the pattern to continue.

Monthly adjusted-close prices come from Yahoo Finance via the yfinance library; returns are computed from price changes and therefore reflect dividends (reinvested) and stock splits but not sales loads or short-term redemption fees. CPI data and yield curve spreads (T10Y2Y) come from the FRED API (Federal Reserve Bank of St. Louis). Data is cached for 24 hours.

The common date range for any simulation is limited by the fund with the shortest history among the selected set. Pre-ETF mode extends available history to 1987 at the cost of using older, less-liquid mutual fund proxies.

Transaction costs, bid-ask spreads, taxes on dividends, and fund minimum-investment requirements are not modelled. The momentum rotation assumes annual rebalancing is frictionless. No look-ahead is used in momentum weights, but survivorship bias may exist in the universe of funds available to select.

Returns are calculated from month-end prices, so results may differ slightly from figures shown on fund websites depending on the exact start date each source uses.