Frequently Asked Questions

SMA (Simple Moving Average) strategies switch between a risk-on asset and a risk-off asset based on whether price is above or below a moving average.

How it works:

• When the price is above the SMA → Stay invested in the leveraged ETF
• When the price is below the SMA → Move to risk-off assets (like bonds or gold)

Shorter SMAs react faster but can whipsaw more. Longer SMAs react more slowly but tend to filter more noise.

The core idea is simple: if borrowing cost stays below long-run stock market return, leveraged equity exposure should work over long periods.

The SMA overlay is like paying an insurance premium. You give up some upside and accept some whipsaws in exchange for avoiding the worst crashes, which are what usually destroy leveraged strategies.

In this app, the UPRO 3x S&P 500 simulation works over more than 140 years of history, and the TQQQ 3x Nasdaq 100 simulation works over more than 55 years. Leveraged + SMA has also tended to reduce volatility enough to improve risk-adjusted results versus the underlying index.

I think it works best in markets that trend up over time and still works reasonably well in falling markets because the SMA can get defensive. It works much less well in markets that stay sideways and cyclical for very long periods, because leverage drag and SMA whipsaws can dominate. I do not expect that to describe the U.S. stock market going forward, but that is still a thesis based on historical behavior, not a guarantee.

Aiming for higher real CAGR matters a lot because compounding makes small annual differences turn into huge ending-value differences. For example, $10,000 growing at 8% for 20 years ends around $46,600, while 16% for 20 years ends around $194,600. That is more than 4x the ending value of the 8% case, which shows why higher real CAGR matters so much over long periods.

That is why this app puts a lot of emphasis on final real value and real CAGR rather than only looking at short-term gains. Over long horizons, compounding dominates.

This is a research web app for leveraged ETF strategies. You can compare simulated and real leveraged ETF behavior, test SMA timing rules, inspect rolling-window outcomes, and run detailed backtests against long stitched market history.

Leveraged ETFs are funds that aim to multiply the daily returns of an index. For example:

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

These ETFs reset their leverage daily, which means returns over longer periods can differ significantly from simply multiplying the index return.

These labels describe when the strategy trades after a signal is triggered:

• SMA — trade on the trigger-day close
• SMA (no) — trade on the next trading day open
• SMA (nc) — trade on the next trading day close

The shared default trade time in the app is currently Next Open.

In the current app, plain preset names such as UPROand TQQQ refer to the simulatedleveraged series.

The real ETF price histories use separate preset names:

• UPRO-real
• SSO-real
• TQQQ-real
• QLD-real

Simulated presets use long index history. Real presets only use actual ETF history after launch.

The Score is a ranking metric for comparison tables. Higher scores indicate stronger return and drawdown tradeoffs across rolling windows, but the number itself is not a return.

It rewards:

• Higher average final real value
• Higher average real CAGR
• Better worst-window real CAGR

It penalizes: large drawdowns, especially severe worst-ever drawdowns, plus excessive trading. It is designed to compare strategy profiles, not to act as a standalone investment recommendation.

A drawdown measures how much an investment falls from its peak to its lowest point before reaching a new peak. It's expressed as a percentage loss.

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

Lower drawdowns are generally better — they mean less stress and faster recovery during bear markets.

Risk-off assets are investments that tend to be safer and more stable during market downturns. When SMA strategies signal caution, your money moves to these assets instead of leveraged ETFs.

Available options:

• SGOV — Short-term Treasury bonds (0-3 months)
• VGSH — Short-term Treasury bonds (1-3 years)
• GLDM — Gold
• BRK.B — Berkshire Hathaway (diversified holdings)
• VOO — S&P 500 Total Return
• QQQ — Nasdaq 100 Total Return

Note: For historical data before BRK.A's inception (1980), we use S&P 500 Total Return since Berkshire is an equity investment. This provides realistic equity-like returns rather than cash-like T-Bill returns.

You can also combine multiple assets, such as VGSH + GLDM or BRK.B + GLDM + VGSH, for equal-weight risk-off allocation.

These metrics show how strategies performed in their best and worst rolling periods:

• Best Real CAGR: The highest annualized return (adjusted for inflation) across all rolling windows
• Worst Real CAGR: The lowest annualized return (adjusted for inflation) across all rolling windows

A smaller gap between best and worst suggests more consistent performance across different market conditions.

On the SMA Period, SMA Buffer, SMA Risk-Off Assets, and Statisticspages, History Wrap allows recent starting months to still produce full-length windows.

The app does not look past your selected end date to use later real market data. Instead, it completes the missing tail of the window by wrapping back through earlier history.

That means recent windows can still be included without peeking into the future. Wrapped windows are counted as simulations and marked as using wrapped history.

Use the SMA Period tool to test different SMA lengths across rolling windows. This helps you compare how sensitive a strategy is to faster versus slower trend signals.

Shorter SMAs react faster but may produce more false signals. Longer SMAs are smoother but slower to respond.

An SMA Buffer adds a threshold around the SMA line to reduce whipsaws (frequent switching between in and out of the market).

How it works:

• With a 5% buffer, you only exit when price falls 5% below the SMA
• You only re-enter when price rises 5% above the SMA

Buffers can reduce transaction costs and false signals, but may also cause you to miss some gains or incur larger losses.

The Signals page shows the current SMA signal state for the S&P 500 and Nasdaq 100 using the shared SMA parameters from the tool pages.

It is useful when you want the current signal without running a full rolling analysis or backtest.

No. Past performance does not guarantee future results.

This tool is for educational and research purposes only. Market conditions change, and strategies that worked historically may not work in the future. Leveraged ETFs involve significant risk and may not be suitable for all investors.

All historical data used in this app is stored as CSV files and is publicly accessible. You can browse and download the data directly:

Loading file list...

The idea for this app was inspired by:

https://www.leveraged-etfs.com/