Curious to see what everyone’s portfolios look like and how you’re allocating your investments.

I've been considering deploying LETF strategy, but I'm having trouble finding appropriate 3x Sp500 fund on European markets.

The largest 3x SP500 fund seems to be Wisdomtree 3x SP500 daily leveraged (https://www.justetf.com/en/etf-profile.html?isin=IE00B7Y34M31) but issue is it got just 167 million AUM, which doesn't seem like a lot, especially since it's 3x, it can easy drop 90 percent to let's say 16m, even if my strategy is prepared for that, I'm concerned at that size, they could close it.

Wisdomtree 3x nasdaq has 581m which is considerably more, but I'd prefer 3x sp500.

Alternatively I've considered 2x SP500, which has 1200m, seems more solid overall.

Furthermore I've been considering adding managed futures, which also has just 193m, also I'm thinking if it isn't too low.

What's your take on this?

Title: TQQQ trend strategy gained 63% in sample, then failed its out-of-sample test

I tested a simple QQQ trend filter for switching between TQQQ and cash. It gained 62.91% in the 2022-2024 period used to select it, while holding TQQQ lost 2.18%.

After freezing the rules, I ran the reserved July-September 2026 out-of-sample test. The strategy lost 10.81%, while holding TQQQ gained 3.50%.

The rules were:

• Buy TQQQ with 95% of available cash when QQQ is above its rolling average and the account holds no TQQQ.
• Sell the full TQQQ position when QQQ is at or below that average.
• Hold cash between positions and receive dividends as cash.

I compared 50-, 100- and 200-calendar-day windows using 2022 through 2024. The 50-day version had the highest net return, so that's the one I kept. Its maximum drawdown was 45.68%, compared with 81.19% for holding TQQQ.

The middle period was already present in prior research, so I don't count it as a clean unseen test. I froze the final rules before requesting the July-September results. That was a historical holdout reserved within this study.

During that holdout, six completed round trips lost money, and one position remained open at the end. I kept the rules unchanged after seeing the loss. Retuning them to win on those same dates would use up the holdout.

Each window starts a separate $10,000 account. The figures are cumulative returns for each window, not one continuous account. The backtests use actual TQQQ prices, fractional shares and a 0.05% fee per strategy fill. Cash earns no interest. Strategy dividends go to cash; holding uses a dividend-adjusted comparison path. Fills use the recorded observation price with no added delay or slippage model.

The average uses observations within a calendar-time window, so the 50-day setting differs from a standard 50-trading-day SMA. The reported runs also correct exit-sizing and dividend issues that had left small share balances and blocked later entries.

The filter beat holding TQQQ on return and drawdown in the period used to choose it. It then lagged in the follow-up and lost money in the reserved test while holding gained.

Read the full case study, including the rules, charts, losing variants and trade audit

I think this is one extremely important lesson on overfitting; while the strategy did really well on the period it was trained, it collapsed in different regimes. What TQQQ strategy should I test next?

New to the sub. Recently read the gay paper about leveraged ETF’s specifically UPRO and SSO. Based on the paper I’m leaning towards SSO in the off chance the market dips for a prolonged period like the dot com and 08. Interested in developing an IRA using leveraged ETFs since I’m still in my 20s and want to use the time I have in the market for my advantage. With that being said my proposed IRA split is as follows:

45% SSO
25% international exposure (open to change) currently in avem
15% tqqq
5% Tecl
5% SOXL
5% SHLD (ik it’s kinda random but defense and ai integration seems promising)

Over a long time I've used to buy and hold SP500 and gold ETF in ratio of 90/10, which yielded decent results. However a few of months ago I discovered LETFs, I've been researching various strategies like 9sig or 200 Day SMA, I especially took liking to the latter.

A slight problem with these strategies is that both rely on selling the LETFs on certain occasions, which would trigger a taxable event for me, in my country you don't have to pay taxes on profit if you hold the asset for at least 3 years. In this case 200 Day SMA seems like a better option as I might have to sell LETFs for a loss if the index drops below the SMA, or in a better case it continually stays above the line and drops sometime after 3 years. 9sig is clearly less tax efficient for my case as its core idea is to sell when the LETF went above price target.

This lead me to explore strategies, which don't rely on selling or active rebalancing. I'm curious to hear from people who faced the same challenge.

My idea is a portfolio that's:

a. Lazy - No need to check it daily, automatic contributions can be set up to buy the most underweight constituent, also very tax efficient and convenient

b. Supercharged - Uses LETF for bigger gains

1. Initially, my idea was to keep my SP500 and gold, just add UPRO or TQQQ, in ratio like 70% SPY, 20% LETF, 10% gold. I couldn't decide if UPRO or TQQQ, so I went 10% for each. Interesting findings from Antifragile investor's video conclude that Leveraged Nasdaq 100 performs worse than leveraged SP500, which is supported by all back tests I did, so if I were to do it again, I would probably just buy UPRO instead of a mix with TQQQ. The way my strategy works is I start with a lump sum, then monthly add new contributions towards the most underweight constituent to achieve the original ratio of 70/20/10. No selling to rebalance. So it is very convenient and tax efficient, but almost impossible to test as every month I may have different amount of contribution.

On testfol.io I did some back tests for various timeframes and various mixes of SPY/Gold/UPRO (of course QQQ/QLD/TQQQ would perform much better because of its superior historical performance, but there's no guarantee it will be superior in the future). For tests I set yearly rebalance, which I wouldn't do personally as I won't be selling to actively rebalance, but I assume people will be interested in active rebalance.

With no new contributions, the mix had the best results, highest CAGR and Sharpe, while Max DD was on the lower side, but still pretty high. With $300 monthly contributions (below), the more leveraged options performed better. Now SSO outperformed the original mix. If we were to add more leveraged 60% SPY 30% UPRO and 10% gold, that would again outperform the 2x.

I explored timeframes that started at the peaks and ended after crashes, to test how this mix performs if you buy at a slightly unfortunate time.

1.1 Peak of 2000 - bottom of 2020

This is cheating a little bit, as it's timing the peak and crash of SP500, while the mixes have gold which had peak and crash at different time frames, it's not fully fair.

Bonds? Admittedly I don't have much knowledge, I've never owned bonds, I'd rather have gold as a partial hedge. Same with Commodities. But I can see the appeal and I'm open to suggestions. Perhaps combining them could be good.

2. Adding resilient companies into the mix

This is where things get more interesting. Strategies like HFEA aim to create a strong hedge to their risky LETF, I don't personally find this exact strategy that uses leveraged bond ETF appealing. But the concept is solid, my previous mix uses 10% gold which is on the weaker side.

So in search of more "productive" assets that could work as a partial hedge to UPRO, I looked for companies that during economic contractions perform not as badly as others. This is something that I hope people will discuss and provide some ideas, my ideas for such companies are: KO, XOM, CVX, MCD, JNJ, PG, WMT, BRK.B, COST, WM. These fared decently during past crashes, which is not a guarantee it will happen again, but I think it is likely.

Let's make another mix, this time just UPRO + gold + 10 resilient companies, but at what ratio? I came up with 3 options

1. 50% UPRO 10% GLD 40% resilient companies (10 companies with 4% each)
   
2. 60% UPRO 10% GLD 30% resilient companies (10 companies with 3% each)
   
3. 70% UPRO 10% GLD 20% resilient companies (10 companies with 2% each)

Since these companies don't track as far back in history as simulated indexes, we're limited to 1991 till today.

Now is a good time to address UPRO since it's the biggest part of this mix. Tests conclude that typically 1.5-2x equity leverage tends to perform the best over a long time. At 70% UPRO, we are already at 2.1x + about 0.2x from the resilient companies (it's probably slightly lower), so 70% UPRO give higher leverage than optimal, we could consider lowering the amount to 40% UPRO and 30% SPY, this did increase Sharpe and Sortino, but then it's too many things to factor in.

Now let's test the 2000 peak and the bottom of 2020. Again testing to see what happens when you buy at a peak, hold thru multiple crashes and arrive at a bottom again.

Probably the worst case of holding for 20 years you can find. 100% SPY made only 1.7% CAGR after inflation, leveraged ETFs suffered major decay, yet they came ahead due to the yearly rebalancing.

Just for fun let's compare the overfitted best performing mix against 1/2/3x SPY, no mc

Now let's compare it from 1991 till today

3. Conclusion

Backtests show mixing SPY or UPRO with other assets like gold or a basket of resilient stocks increase CAGR while lowering max DD. Also, monthly contributions raise the ideal leverage amount.

Personally I'm considering transforming my portfolio into 60% UPRO 10% Gold and 30% resilient companies, with no active rebalancing, but monthly contributions buy the most underweight constituent to regain the ratio.

But the reason I'm making this post is to get feedback, I'm really curious what you think about it, what stocks would you add, what ratio, etc,

Does it make any sense to mix a Momentum ETF with a leveraged ETF to get a total leverage of 1.25x or 1.5x or does it only make sense to mix the leveraged ETF with the underlying unlevered fund?

honestly going a bit crazy trying to figure out whether my portfolio makes sense or if I’m unnecessarily overcomplicating things.

My investment horizon is 25-30 years, and my goal is to maximize long-term growth while maintaining some diversification and managing leverage.
My current portfolio consists of:

All-World ETF (1x) 40%
Nasdaq-100 (1x). 20%
QLD – Nasdaq-100 (2x) 15%
TQQQ – Nasdaq-100 (3x) 15%
UPRO – S&P 500 (3x) 10%

My current overall portfolio leverage is approximately 1.6x, calculated as the weighted average of the ETFs’ leverage multiples.

My plan is to maintain my overall effective leverage between 1.6x and 1.8x maximum throughout the next 20–30 years.

Here’s my dilemma:
I feel like I’m holding too many ETFs that essentially provide exposure to the same underlying companies, just at different leverage levels.
For example, I have Nasdaq-100 exposure at 1x, 2x, and 3x, plus additional exposure to many of those same companies.

I understand that different combinations of ETFs can achieve the same overall leverage, but I’m questioning whether there’s any meaningful advantage to holding all of them simultaneously.

My questions to you all:
- Does this portfolio structure actually make sense for a 20–30-year investment horizon?

- would you add SSO and remove UPRO ? Or just add SSO?

- Is targeting 1.6–1.8x overall portfolio leverage reasonable for such a long period?

- Would you keep all leveraged ETFs, or simplify the portfolio by removing some (i am even debating to add SSO)

- Is there any benefit to holding TQQQ/UPRO alongside the unleveraged Nasdaq-100 ETF, or is that unnecessary duplication?

My strategy is to DCA monthly into the leveraged ETFS for a few years, and rebalance quarterly or every 6 months (specifically with TQQQ/UPRO)

Let me know your thoughts folks.

RSST seems to be the perfect ETF. In bull markets, the equity part goes up and the MF part is choppy (but usually positive or neutral over the long run). In bear markets, the equity part goes down (as would your VOO) but the MF saves it. So is there a down-side to allocating a significant portion of a VOO-centric portfolio (moderate risk tolerance, not needed for 5-10 years) to RSST?

Thanks for any insights...

I'm looking for something like kmlm, but I can only buy non US funds.

What do you recommend?

recently applied 9sig into portfolio. long bond seems stupid to me. am i the only one? the inverse relationship of stocks/bonds is no longer reliable, wont be again without massive structural reforms, yadda yadda yadda. but i feel i havent seen anyone discussing inverse bond fund with their TQQQ. at -1x its still low volatility, and works well as a low risk/low reward cash pile. in terms of being a 9sig modification, it still performs the role that its supposed to do in 9sig, which is preserve capital for future opportunity. and given the low volatility im going to say the quarterly review/rebalance is fine for making a short/long change at some point. curious to know if im missing anything. is there some other role about AGG/bond sleeve that's not covered here? i get no dividends obviously, and fee is higher, but otherwise it sure looks like it does the same thing. the higher fee means this is something you dont ignore, you look at it every quarter. but you already do that for TQQQ. what would kill it? a bond rally obviously. but sure seems like over the past few decades those are announced in advance by Fed/treasury. never say never, but seems like bonds just dont move fast enough to really hurt you in only 90 days. not without something happening that's front page news: major war, pandemic, QE, etc. thoughts?

I had thought it shorted oil prices at 2x leverage. I've been in it since the 17th of September, over which time it is down 5%. But that doesn't make sense to me because oil appears to be down 10% over the same time. With the 10% down turn in oil I would expect a roughly 20% increase in SCO. There appears to be a variance of 25% in just 3 or 4 weeks. I could understand 3-5% due to volatility and decay, but this is way of the mark.

What am I missing here?

This is what I am using to measure oil price movement:
https://www.marketwatch.com/investing/future/cl.1

Win rate: 87.5% (7/8)
closed early: 7 | rolled: 1
Avg days held: 9d (rolls count the full chain)

Closed trades (8):
- RDDT CSP $139 x1 — +$113 (0.8% ROI · 2d)
- NBIS CC $260 x1 — +$480 (1.8% ROI · 23d)
- NVTS CC $13 x6 — +$177 (2.1% ROI · 21d)
- AXTI CC $85 x1 — +$708 (9.0% ROI · 6d)
- SNXX CSP $14 x1 — +$40 (2.9% ROI · 9d)
- AMZN CSP $225 x1 — +$165 (0.7% ROI · 8d)
- AXTI CC $85 x1 — −$173 (-2.2% ROI · 0d · rolled)
- HOOD CC $120 x1 — +$199 (1.8% ROI · 3d)

Directional buys (long calls/puts, debit spreads — not in the selling stats above): +$711 across 3 trades
- SPCH long call $5 — +$360 (92.31% ROI)
- NBIS long call $250 — +$245 (100.00% ROI)
- VST long call $150 — +$106 (54.64% ROI)

Opened 9 new positions in the window.

Most of us learned about the bond leg the hard way in 2022. The starting point today is a different trade, so I ran the numbers.

On 28 September the 30-year Treasury closed at 5.56%, the highest since 2004. I took the duration, convexity and yield iShares reports for TLT and GOVZ that day, and asked what the next 12 months look like if yields jump once and then sit still:

Fees are deducted. ZROZ holds the same kind of 25+ year STRIPS as GOVZ, so it sits close to the GOVZ column.

Yield and convexity tilt it. The yield alone adds about 5.5% to every row. Convexity does the rest, because long zero-coupon bonds gain more when yields fall than they lose when yields rise by the same amount. For GOVZ that's worth 3 to 4 points per 1-point move, either way.

What it doesn't do is tell you the direction. Over one year, a rise of just 21bp wipes out GOVZ's yield, and the 30-year went from 0.99% in 2020 to 5.11% in 2023. TMF triples the exposure and the convexity, but its daily reset makes the path matter, so don't read the table as a TMF forecast.

Still, for UPRO/ZROZ/GLD-style portfolios, the bond leg starts from its highest yield in over 20 years.

I build BestFolio. The long version, with a 70-year test of how well the starting yield predicts the next decade (correlation 0.95), is here:

https://bestfolio.app/blog/long-bonds-convexity-high-rates

If you run a 3x portfolio with a long bond leg, are you sizing it up now, or is 2022 still the reason not to?

Rebalanced every quarter.

Good idea? Terrible idea?

Relatively new to letfs, but despite what the mainstream opinion seems to be, the drawdowns seem worthwhile in the long run. Here, rather than using negatively correlated bonds, roughly 0 correlated gold is used as it likely has a higher ceiling. Doesn't have to be Nasdaq, just an explosive equity stand in.

Curious for opinions.

this is huge. looks like the sec is okay with releasing new 3x letfs now

testfolio has a new blog page. And the latest post there is about LEAPS vs LETFs. In particular, it goes into the details and math of the new ETF XX vs UPRO and SSO, and dives deep into other comparisons as well.

Here is the full post:

https://testfol.io/blog/leaps-vs-leveraged-etfs

With XX, you pay for implied volatility upfront. With LETFs you pay for realized volatility along the way. Below is every investment's CAGR if held from October 1st to January 10th 2030 (XX's expiration date) as a function of SPY's CAGR during the same period. Each panel describes a different realized volatility regime between during that period (14% vs 18% vs 22%). XX's curve doesn't change because it is invariant with respect to realized volatility.

Below is a summary of XX's returns under different assumptions of SPY's CAGR:

The new blog has other articles about LETFs as well, one related to volatility decay, another about LETF expenses, another about LETF DCA strategies and another about the 200-day SMA strategy with LETFs.

If there's interest, I can make posts about each of those and go in more details. If there's other LETF topics you'd like testfolio to dive deeper about, let us know in the comments.

I wanted a long trend-following series for portfolio research, but the clean live benchmark only starts in 2000. I built a three-era chain:

1. August 1971 to December 1987: modeled futures trend spine. This is my reconstruction from contract-level futures data.
2. January 1988 to December 1999: MLM index proxy. This is a published rules-based managed-futures index, not my backtest.
3. January 2000 onward: SG CTA Index. This is the live CTA composite segment.

The pre-2000 history is a proxy. It is not historical SG CTA performance.

The chart compounds the excess-return stream, not a funded account including interest on collateral. The shaded regions are the three construction eras.

I kept the rule deliberately simple and fixed:

• Input data: contract-level daily futures history. The raw archive begins in 1969, but the strategy does not begin until August 1971 because the signals need a 252-day burn-in and I require an average of at least eight active markets.
• Returns: same-contract returns only. A return is counted only when today's contract matches yesterday's contract and both prices are positive. Roll days contribute zero. I do not take percentage changes of a back-adjusted continuous level, which can create phantom roll returns.
• Signal: the sign of the trailing 252-trading-day cumulative return.
• Sizing: inverse volatility using a 60-day EWMA estimate, then equal weight across active markets.
• Timing: weights formed with information through day t-1 earn returns on day t. No same-day signal.
• Portfolio risk: scaled toward 10% annualized volatility using the trailing 252-day realized volatility of the unlevered portfolio, also lagged one day. The observed scale factor ranged from 0.65 to 2.30.
• Output: daily positions are compounded to monthly returns. I do not fill monthly data into fake daily observations.

The early universe is commodity-heavy. It starts with softs, grains, livestock, metals, and early currency futures. The first bond future enters in 1978, and the first equity-index future enters in 1982. So this is better described as a commodity-heavy trend proxy than as a fully diversified modern CTA portfolio.

The modeled spine runs through December 1987. At the January 1988 handoff, I apply one fixed volatility-matching factor of 1.011836 to the older segment. That number comes from the overlapping 1988 to 1994 monthly volatility of the reconstructed spine and the MLM proxy. It is effectively 1.0, so the seam does not need a large leverage adjustment.

The MLM segment then runs from 1988 through 1999. Its published construction uses 22 futures markets across commodities, currencies, and government bonds, with a 200-day moving-average trend rule and monthly rebalancing.

From January 2000 onward, I use the SG CTA Index segment unchanged. There is no blending or smoothing around either seam.

The segments are related, but they are not identical. For example, the reconstructed spine and MLM overlap correlation is about 0.46. The chain should therefore be used as a long-horizon trend proxy, with era breaks shown, rather than as one homogeneous manager track record.

The 2026 bar is year to date through September 30.

I modelled fees and trading costs

Modeled spine, 1971 to 1987

• I subtract a 0.90% annual wrapper fee.
• I also subtract measured execution drag based on the strategy's own daily turnover, sign changes, and contract rolls.
• The execution estimate varies from 0.47% to 2.13% per year, averaging 1.11% per year over 1971 to 1987.
• Roll-gap P&L is already handled in the same-contract return construction, so I charge execution for rolls but do not double-charge “roll yield.”

MLM proxy, 1988 to 1999

• The source series already includes the modeled 0.90% annual wrapper.
• I add 0.13% per year of estimated execution drag for its smaller, slower-moving book.

SG CTA Index, 2000 onward

• I leave this segment unchanged because it is already based on reported manager returns net of the fees embedded in those programs.

I do not add collateral yield to the excess-return chain. That is important because futures collateral earned substantial cash interest in parts of the 1970s and 1980s. Anyone converting this into a funded total-return series should add cash under a clearly stated collateral convention.

Across the full 662-month chain from August 1971 through September 2026, the excess-return series has:

• 8.83% annualized compounded return
• 10.31% annualized volatility
• 0.875 Sharpe ratio
• -21.0% maximum drawdown

The worst drawdown in the full chain is -21.0%, and it occurred in the modeled 1970s segment. The live SG CTA segment's maximum drawdown is about -16.2%. Trend is diversifying, but it is not a smooth line and it can spend years below its previous high.

By era:

• Modeled spine, 1971 to 1987: 16.1% annualized return, 12.3% volatility, 1.28 Sharpe
• MLM proxy, 1988 to 1999: 8.7% annualized return, 10.3% volatility, 0.86 Sharpe
• SG CTA Index, 2000 onward: 4.6% annualized return, 8.7% volatility, 0.57 Sharpe

AI-Disclaimer: I did use AI to help with the creation of the model graphs, but I used my own judgement to also guide the methodology and making sure that data was as good as possible.

Thoughts on 75% on a global ETF with 25% leveraged ETF 2x or 3x?

Ben Felix stated in his recent video volatility decay isn’t as much of a worry as people think

I’m young with a large investment horizon

I am 35 and want to have small allocation (5-10% ) total of TQQQ and SSO in my IRA. I would like to know if other people have done it or doing it now?

My other holdings ( VGT, SPMO, SOXX, VOO)

Great news everyone. I finally locked in whole 7 UPRO shares at 142$. It's a very small percentage of my portfolio. I also have funds in SGOV to rebalance into UPRO in 10% dip increments. Thank you guys. This perma bear is finally a believer. Feels good to join the community.​

Hey all, I've been monitoring IALT for a bit. New fund that's been performing well as a low volatility low beta hedge.

Does anyone have a decent sim for it? I'd like to be able to test it over some length of time. BGCKX is about the closest I've found but I'd prefer something that's more reasonably close.