r/LETFs

5% 10% 20% 30% or all 100% leverage?

Been following this sub and r/TQQQ for a while, and see the most sophisticated folks on the investing subs here. I’ve been playing (so far successfully) with TQQQ and SPXL, both direct and short-term options (I know, but couldn’t help myself) but want to cut that out and get into a more systematic strategy long term.

I know enough I think about the research. 200-day SMA seems to have worked well historically but is getting killed in the taco world we live in. 6 or 9 sig seem to have massive upside but too risky for me.

I decided on a strategy where I’ve initiated 50/50 TQQQ/QQQ and will rebalance to par whenever the spread is 15% between them (underlying as denominator), both up and down. This should capture some of the buy-low-sell-high of 9sig without taking on so much risk, avoid the opportunity losses from abrupt market upswings of 200SMA after signal says sell (buy high sell low), and keep me in the market with all available funds at my disposal. Better than only holding QLD I think given the rebalancing, even if there will be more volatility drag.

Thoughts? Anyone backtested this type of strategy? Please feel free to crucify it with reasoned thought or analysis. I’m relatively new to this. As an aside, I’m implementing the same strategy with SPXL and AVNM, my proxy for VXUS. Same idea but gives me int’l exposure in this part of my port.

EDIT: I’m doing this in my 401k so tax drag isn’t a concern. Also, to reemphasize, the rebalancing strategy is the key to this in my mind over just holding QLD.

401(k): S&P500 100% 66k

Roth IRA: QLD 100% 88k

HSA: QLD 100% 51k

205k total (36y)

Any thoughts?

1m total and i want to do 25% of that on leveraged etfs how would you spread the 25% for aggressive growth?

I want to get moderately leveraged exposure to the S&P500 index in my retirement accounts (125%) and hold for 25-35 years.

I’ve looked into a few strategies and I’m hoping to get a gut check.

Strategy 1: CME Index Futures

This seemed like the most efficient strategy with low overhead. Pay roughly the risk free rate for leveraged index exposure, keep the position backed by short term treasuries like SGOV or an interest bearing money market account to offset the cost of opening positions and roll the contracts quarterly. *Except* brokerages that offer futures trading in an IRA require the positions to be backed by settled cash, and that would not be earning interest. The requirement is about 20% of the total position. Once I take that dead cash into account, the effective cost of leverage is actually quite bad. That strikes futures out for me

Strategy 2: DITM Leaps

The key advantage I see DITM calls is having a cap on losses. I would roll the positions a year before expiration re-open new ones. They seem to offer efficient leverage, but potentially wide bid-ask spreads make and calculating future “theta decay” make it difficult for me to understand what my effective cost of leverage actually is. With $115,000 in one of my IRAs I’d have to open one 350 C SPY contract to get the exposure I want at basically the risk free rate.

Strategy 3:

2x long LETFs. Great, but I don’t love the uncertainty of volatility drag. I’d hold 75% of my account in VOO and 25% in SSO and just let it rip. The biggest pro is that it’s easy to manage and easy to rebalance to maintain my target exposure.

Is my understanding of the cost of index futures correct?

Am I overestimating the complexity and impact of options pricing algorithms?

Am I too afraid of volatility drag? The LETF route is nice and maybe the simplicity is worth it.

I vibe-coded a web app to backtest leveraged ETFs.

https://l-etf.com/

It has answered many of my questions. I have nothing to sell; I'm posting this here in case it is useful for others.

The repo is here https://github.com/ravelab/l-etf

2x Leveraged Servicenow. I made 50% return on PALU 2x leveraged Palo Alto. I just wanna what you guys think. Worth it?

LETFs have always intuitively seemed mathematically sound, but brokerages warning from holding these for extended periods of time has put me off. So I skimmed a few papers and decided to build a simulation to test it out myself.
It's really quite simple, it gets daily data of SP500 from 1927-12-30. Shuffles it, runs the shuffled random selection for 20 years, rinse-and-repeat 10 000 times, both for lump-sum and DCA. The resulting histogram was non-informative, but taking a logarithm of all the values resulted in a beautiful normal distribution.
I used geometric mean because for exponential data, geometric mean and geometric standard deviation (GSD) are much more meaningful for an average investor as the geometric mean gives a more likely result. In a dataset with a few really large values, the arithmetic mean gets skewed in an unrealistic direction in our case, as the few small outliers that cause it happened due to incredibly lucky runs consisting of almost only days with positive percentage change which hasn't happened in stock market history.

Here's what I found for 20 years:
| | 3x leverage | 2x leverage | 1x leverage |

|:--------:|:--------:|:--------:|:--------:|

| Lump-sum geometric mean | 3.9 | 5.1 | 3.4 |

| Lump-sum GSD | 9.8 | 4.5 | 2.1 |

| DCA geometric mean | 3.2 | 3.0 | 2.0 |

| DCA GSD | 4.7 | 2.9 | 1.7 |

https://preview.redd.it/fez9rsts7a2h1.png?width=640&format=png&auto=webp&s=5620a6794d25da1c55514cbe73108795c76f3887

https://preview.redd.it/fag2tjhu7a2h1.png?width=640&format=png&auto=webp&s=c93699dc5b04aa8d38bbecf96ea3e9f3ea48219e

What was really interesting was that the lump-sum 2x leverage geometric mean was bigger than lump-sum 3x leverage geometric mean, but for DCA it was reversed. I am thinking that it might be a bug, but for the life of me I cannot find it. If anyone has any idea why for DCA the results are like this, please let me know. And constructive criticisim of course is always welcome.
The code in question: https://github.com/Tormihunt/2x-leverage-testing

What are people's thoughts on this ETF? It's a low, and I want to drop $4K from my HSA on it. Would you?

Do you think it’s a good idea to invest 50% in VOO and 50% in SSO/monthly for the long run? Or should I adjust the allocation a bit maybe 60–70% in VOO and 30–40% in SSO instead? Right now I'm investing mostly in voo and qqq but I want to spice things up a little bit. I'm not interested in 3x leverages ETFs like tqqq so pls dont mention it.

Has anyone here invested in leveraged ETFs long term? I’d love to hear your experience including qld as well. Thank you!

Running an “all weather” version of HFEA. Please poke holes in my strategy in the comments. 55% UPRO , 30% TMF , 10% KMLM , 5% UGL.

strategy: DCA weekly, quarterly rebalances

Market starting to feel pricey again. Trying to keep my Roth levered above 1x but not much above.

TQQQ grew to about 9% of my acct and obviously did amazing.

SSO feels like the right amount of leverage at these all time highs. When everyone starts complaining about everything again I’ll go back into TQQQ.

A few days ago I posted a primer on the 200-day SMA strategy for LETFs (link below). In that post I said filtered strategies "typically run 0.3 to 0.7 higher in Sharpe than buy-and-hold." That was the general community claim. I went back and re-ran the numbers on my own backtest harness to check, and the data doesn't support it.. at least not on this window. This is the correction post.

It turned out to be more interesting than the original framing.

The setup

Pure mechanical SMA200 strategy:

• Long the LETF when daily close > 200-day SMA
• 100% cash when below
• Signal computed at close, position taken at next bar's close (no lookahead)
• No confluence indicators (no MACD, RSI, buffer band, anti-whipsaw block)
• 1 basis point per side transaction costs
• Total-return data (yfinance auto_adjust, dividends reinvested)
• From inception of each LETF through 2026-05-15

Comparison: buy-and-hold the same LETF over the same window. Same data, same costs.

The numbers

SOXL (since 2010-03-11, ~16.2 years)

Metric	Buy-and-hold	SMA200 filter
CAGR	41.5%	28.5%
Max drawdown	-90.5%	-69.7%
Volatility (ann.)	89.3%	59.5%
Sharpe (rf=0)	0.839	0.723
Sortino	1.182	0.794
Trades per year	—	3.3
Time in market	100%	63.2%

TQQQ (since 2010-02-11, ~16.2 years)

Metric	Buy-and-hold	SMA200 filter
CAGR	43.8%	30.3%
Max drawdown	-81.7%	-50.0%
Volatility (ann.)	61.0%	42.1%
Sharpe (rf=0)	0.905	0.842
Sortino	1.165	0.923
Trades per year	—	2.7
Time in market	100%	74.3%

UPRO (since 2009-06-25, ~16.9 years)

Metric	Buy-and-hold	SMA200 filter
CAGR	33.1%	16.8%
Max drawdown	-76.8%	-56.7%
Volatility (ann.)	51.3%	31.7%
Sharpe (rf=0)	0.817	0.651
Sortino	1.009	0.706
Trades per year	—	2.9
Time in market	100%	73.3%

What the data actually says

Three things stand out:

1. Pure SMA200 filtering underperforms buy-and-hold on Sharpe across all three LETFs.

Sharpe differences (filter minus B&H):

• SOXL: -0.116
• TQQQ: -0.063
• UPRO: -0.166

Small but consistent in the same direction. The conventional "filter improves Sharpe" claim doesn't hold across these tickers over this window. This is the part I had wrong in Post 1.

2. The filter consistently slashes max drawdown.

• SOXL: -90% → -70% (20 pp better)
• TQQQ: -82% → -50% (32 pp better)
• UPRO: -77% → -57% (20 pp better)

A 90% drawdown on SOXL is the textbook "wiped out" outcome. Going from -90% to -70% is the difference between destroyed and badly bruised. On TQQQ the filter's -50% is rough but recoverable - most retail holders would survive it psychologically. -82% buy-and-hold, much less clear.

3. The CAGR cost is real and large.

Buy-and-hold absolutely crushes the filter on raw return:

• SOXL: 41.5% vs 28.5% (-13 pp)
• TQQQ: 43.8% vs 30.3% (-13.5 pp)
• UPRO: 33.1% vs 16.8% (-16.2 pp)

The filter strategy still turned $10k into $577k on SOXL, $735k on TQQQ, $138k on UPRO over the windows. Just nowhere near what B&H did, and B&H benefited from the entire 17-year run being one of the strongest bull markets in equity history.

Why the filter case is actually behavioral, not mathematical

Here's what the Sharpe number doesn't capture: backtests assume you would hold through a 90% drawdown. In practice, people don't. They sell at the bottom, re-enter after recovery is confirmed, and capture a much worse return than the backtest implies.

The 200-day SMA filter is risk management, not return optimization. Its real value is that you can actually execute it without ego-collapsing during a bear. You exit at -10 to -20% from the top, not at -90%. That's psychologically holdable. Buy-and-hold of a 3x LETF, for most people, is not.

So when someone asks "does the SMA200 filter work on LETFs?" the honest answer is:

• If you're a backtest robot with infinite holding capacity: it underperforms B&H on Sharpe over this specific window.
• If you're a human with money you'd panic-sell at -70%: it probably saves you from yourself, which is the whole point.

Caveats worth naming

Don't over-cite these numbers without flagging:

Sample bias. 2009-2026 contains one of the strongest equity bull runs in modern history, plus two unusually V-shaped drawdowns (2020, 2022). A different sample: e.g. weighted toward the 2000-2012 NDX lost decade - would tilt this comparison meaningfully toward the filter. Bear-heavy regimes are where the filter would dominate.

The 10-month SMA warmup. The filter is forced into cash during the first 200 days of each LETF's data (no SMA exists yet). Skipping this handicap lifts filter CAGR by 1-3 pp across these names without materially changing the Sharpe story.

Pre-tax. Filter generates ~3 short-term capital gains events per year. B&H generates zero until exit. After-tax results widen further in favor of B&H for taxable accounts.

No regime decomposition. A bull-vs-bear breakdown would show the filter winning hands-down in 2022 and underperforming materially in 2017/2019/2023. Inception-to-now totals smear that out.

One bonus finding worth flagging

For UPRO specifically, computing the SMA200 on SPY (the underlying) and applying it to UPRO position works meaningfully better than computing the SMA200 directly on UPRO:

Direct SMA on UPRO	SPY SMA → UPRO
CAGR	16.8%
Max drawdown	-56.7%
Sharpe	0.651

That's 8 percentage points of CAGR for free, plus a slightly better drawdown. Sharpe on the underlying-signal version basically matches buy-and-hold (-0.016 difference), but with the same drawdown protection.

For TQQQ, the QQQ-based signal is only marginally better than direct SMA on TQQQ (+0.007 Sharpe).

For SOXL, the SOXX-based signal is actually slightly worse (-0.049 Sharpe vs direct SMA on SOXL).

So the "track the underlying" claim that gets repeated in LETF communities has real evidence for UPRO/SPY, weak evidence for TQQQ/QQQ, and basically no evidence for SOXL/SOXX over this window. Worth its own post.. I'll get to it next week.

The honest takeaway

The 200-day SMA on LETFs is not a Sharpe-improver over very long bull-heavy windows. It's a drawdown manager that costs real CAGR. The pitch is "you can sleep at night and you won't panic-sell at the bottom," not "you'll outperform B&H."

Whether that's worth it depends on whether you'd actually hold a 3x LETF through -82%. If you would: you don't need this. If you wouldn't: this is the move.

I'm working on a Python notebook version of this backtest harness for GitHub so anyone can verify or critique the methodology directly. Will link when it's up.

Has this changed how you think about it? Or were you already running it as a drawdown filter rather than a return enhancer? Curious where the community shakes out.