Frequently Asked Questions

Leveraged ETFs try to move by a multiple of an index each day. If the index is up 1% today, a 3x fund tries to be up about 3% today. If the index is down 1%, the fund tries to be down about 3%.

• UPRO — about 3x the S&P 500
• TQQQ — about 3x the Nasdaq 100
• SSO — about 2x the S&P 500
• QLD — about 2x the Nasdaq 100

The important catch is that the leverage resets every day. Over months or years, the result will not be exactly 2x or 3x the index's total return.

It is a rule for when to be aggressive and when to step aside. The strategy compares the market price with its moving average, which is just the average price over the last N trading days.

• Price above the moving average: hold the leveraged ETF
• Price below the moving average: move to the selected safer asset

The goal is to stay invested during long uptrends and reduce exposure during major bear markets. Shorter moving averages react faster but can switch too often. Longer moving averages are calmer but react later.

The basic idea is that stocks have historically gone up over long periods. If the market return is high enough to overcome borrowing costs, fees, and bad periods, leveraged stock exposure can compound strongly.

The moving-average rule is a way to avoid some of the worst crashes. It is not free: it can sell too early, buy back too late, or switch back and forth in choppy markets.

This app lets you test that idea over long history. The simulated UPRO data goes back more than 140 years, and the simulated TQQQ data goes back more than 55 years.

This approach tends to look best when markets trend upward or when the rule avoids major crashes. It can look bad in sideways markets, where leverage costs and repeated switches can eat away returns. Historical results are not a guarantee.

CAGR means the average yearly growth rate. Real CAGR means the average yearly growth rate after inflation. It is closer to your real buying-power growth.

Small yearly differences become large over time. For example, $10,000 growing at 8% for 20 years ends around $46,600. At 16%, it ends around $194,600.

That is why the app focuses on real CAGR and real end value instead of only short-term gains.

Score is a quick ranking number for comparison tables. It is not a return, and it is not a recommendation. It is just a shortcut for comparing strategies.

A higher score usually means the strategy had a better mix of:

• Higher inflation-adjusted returns
• Better results in bad historical periods
• Smaller losses from peak to bottom
• Less excessive trading

Use it as a sorting aid, then look at the actual return, drawdown, and trade-count numbers before drawing conclusions.

The app tries to avoid showing fantasy returns. It subtracts costs where they matter:

• VOO and QQQ: The benchmark rows include ETF expense ratios, so they are closer to owning the real funds.
• Simulated leveraged ETFs: The engine subtracts the fund fee every day and estimates borrowing costs from market interest-rate data plus a swap-spread estimate.
• Real ETF presets:These use historical fund prices, which already include the fund's internal costs.

A drawdown is how far your portfolio falls after reaching a high point. It is measured from the peak to the lowest point before recovery.

Example:If your portfolio drops from $100,000 to $40,000 before recovering, that's a 60% drawdown.

Drawdowns matter because big losses are hard to live through and hard to recover from. A 50% loss needs a 100% gain just to get back to even.

Risk-off assets are what the strategy holds when it is not holding the leveraged ETF. Think of them as the defensive parking place.

The choices include:

• SGOV and VGSH — short-term Treasury bond funds.
• GLDM — gold.
• BRK.B — Berkshire Hathaway.
• VOO — S&P 500 exposure.
• QQQ — Nasdaq 100 exposure.

For long backtests, the app extends these series backward with historical proxy data when the ETF did not exist yet. You can also use equal-weight mixes such as VGSH + GLDM or BRK.B + GLDM + VGSH.

When the strategy switches, the app subtracts an estimated trading cost. This is meant to represent the bid-ask spread: the small cost of buying at the ask and selling at the bid.

A switch means selling one asset and buying another. The app charges a spread cost on the leveraged ETF side and on the risk-off side. If the risk-off asset is a mix, it averages the spread cost across the assets in the mix.

Regular-session trades use tighter spreads. Same-day close execution uses wider spreads to be more conservative.

Assumed half-spread fractions: regular session / wider close spread

• TQQQ: 0.0001 / 0.0010
• UPRO: 0.0001 / 0.0010
• QLD: 0.0002 / 0.0012
• SSO: 0.0002 / 0.0012
• SGOV: 0.0001 / 0.0006
• VGSH: 0.0002 / 0.0008
• GLDM: 0.0002 / 0.0010
• BRK.B: 0.0002 / 0.0008
• VOO: 0.0001 / 0.0006
• QQQ: 0.0001 / 0.0008

Example: 0.0001 means 0.01% of the traded amount for that side of the trade. Brokerage commissions are not modeled separately.

The Futures page simulates SMA strategies with index futures and risk-off securities executing at the next open.

• Uses explicit futures costs such as IBKR-style fees and spread assumptions.
• Applies maintenance-margin scenarios (Normal, Stress, Crisis, Extreme).
• Tracks futures-specific diagnostics like leverage delta, excess liquidity, and transaction-level roll behavior.

It is still a model, but it is designed to emulate real futures workflow more closely than ETF-only backtests.

These show the best and worst periods found in the rolling-window tests.

• Best Real CAGR: the best inflation-adjusted yearly return in any tested period.
• Worst Real CAGR: the worst inflation-adjusted yearly return in any tested period.

A strategy with a great best case but a terrible worst case may be harder to stick with.

Some pages test fixed-length periods, such as 10-year or 30-year windows. Near the end of the data, there may not be enough future days left to complete the full window.

On the SMA Period, SMA Buffer, SMA Risk-Off Assets, and Holding Periodpages, the app can still include those recent starting months.

The app does not look past your selected end date. Instead, it fills the missing tail by wrapping back through older history. This avoids using future data that would not have been known at the time.

This is a modeling choice. It gives newer start dates a full-length test window, but the tail is historical stand-in data, not a prediction.

Use the SMA Period tool. It runs the same strategy with many moving-average lengths and compares the results.

Short moving averages react quickly, which can help in crashes but can also cause too many false switches. Long moving averages are smoother, but they can react late.

A buffer is a cushion around the moving-average line. It prevents the strategy from switching just because the price barely crossed the line.

The buffer is a pairof values: the lower (sell-side) threshold for falling below the SMA, and the upper (buy-side) threshold for rising above it. They're shown in the input field as −[lower] , [upper] %.

Symmetric example: set both to 5% and the strategy exits when price falls 5% below the SMA and re-enters when price rises 5% above it.

Asymmetric example: set −4 / 8% to exit relatively quickly on the way down (4% below SMA) but wait for a clearer recovery before buying back in (8% above SMA). The reverse — −8 / 4% — is more reluctant to sell but eager to re-enter. Tuning the two sides independently lets you bias toward fewer whipsaws or earlier exits.

Buffers can reduce false switches and trading costs. The tradeoff is that they can also delay exits or delay buying back in.

Why simulate at all?The real ETFs are young — UPRO and SSO launched in 2006-2009, TQQQ and QLD in 2006-2010. That's not enough history to test how a strategy would have done through, say, the 1929 crash or the 1970s. So the app builds a synthetic version of each ETF going back as far as the underlying index data exists (S&P 500 since the 1800s, Nasdaq 100 since the 1980s).

How a real leveraged ETF works in plain terms. A 3x fund like UPRO doesn't hold $3 of stock for every $1 you put in — it holds roughly $1 of stock plus a $2 swap with a bank that mirrors the index. The bank essentially lends UPRO $2 of exposure, and UPRO pays for that loan every day. So your daily return is:

1. 3 × what the index did today, minus
2. the fund's expense ratio (a tiny daily slice), minus
3. the cost of borrowing the extra $2 of exposure (interest + a bank fee).

That's exactly what the simulation does. Step 1 is the index. Step 2 is the published expense ratio. Step 3 is what we have to model.

Modeling the borrowing cost.The base borrowing rate is a real interest-rate series: historical bank rates back to 1885, stitched with the modern overnight SOFR rate from 2018 onward. On top of that base rate the bank charges an extra premium (called the "swap spread"). The premium isn't fixed — when interest rates are higher, banks charge a higher premium. So we model it as a slope and an intercept: rate-sensitivity (how much the premium rises when rates rise) plus a small base-spread (the fee when rates are zero).

How we know those two numbers are right. We have actual UPRO, TQQQ, SSO, and QLD prices going back to their launch dates. The app runs the simulation against those real prices and adjusts the two numbers until the simulated NAV tracks the real ETF as closely as possible day by day, over the full 15-20 year history. The fitter looks at four kinds of error at once (day-to-day tracking, long-term drift, average gap, worst gap) and picks the parameters that minimize the combined score. This calibration re-runs automatically every Monday.

Current calibrated values:

A few sanity checks fall out of this. 3x funds have higher rate sensitivities than 2x — banks charge more for taking on more leverage. Nasdaq-tracking funds are slightly more rate-sensitive than S&P-tracking ones, which fits the intuition that a more volatile index has a more expensive swap book.

If you want the math. The daily formula is:

R_LETF(t) = L × R_index(t)
            − ER_daily
            − (|L| − 1) × (R_borrow(t) + swapSpread_daily(t))

swapSpread_daily(t) = (rateSensitivity × R_borrow_annual(t) + baseSpread) / 360
NAV(t) = NAV(t-1) × (1 + R_LETF(t))      (floored at 0)

The (|L| − 1)factor is "you only pay financing on the borrowed portion" — a 3x fund borrows 2x its own capital, so it pays the borrow + spread on that 2x slice. Compounding day by day is what produces volatility decay over long horizons.

How well does it match reality? After the latest calibration, the simulated cumulative returns for UPRO, TQQQ, SSO, and QLD match their real counterparts to within roughly a few basis points of final return over 15-20 years. You can verify this yourself on the backtest with simulated and real ETFs side-by-side — the simulated and -real lines should overlap.

What this simulation does not capture. Intraday rebalancing slippage, bid/ask widening during stress, dividend timing quirks, and any future regime change in how banks price these swaps. Treat the synthetic history as a reasonable approximation for research, not a guarantee.

• Source code on GitHub: github.com/ravelab/l-etf
• Reddit discussion (r/LETFs): r/LETFs thread

No. Past performance does not guarantee future results.

This site is for education and research only. Market conditions change, borrowing costs change, and strategies that worked historically may fail later. Leveraged ETFs can lose money very quickly and are not suitable for everyone.