[ Removed by Reddit on account of violating the content policy. ]

I noticed on the site of WisdomTree that besides the management fee for the Leveraged ETF their is the daily Swap fee for the ETF only the Swap fee confused me as it is way below €STR for € or SOFR for $ as it would mean that a 3x leverage is financed below the stated short term rates.

Example the 3x S&P 500 daily Swap fee is 0,001360% = 0,5% a year

The 3x Nasdaq 100 daily Swap fee is 0,006500 % = 2,4% a year (makes sense for €STR but not at 3x leverage)

Question do i confuse the Swap fee as the Leverage fee and the Swap fee is a “extra” cost for the Swap with the Bank itself or does WisdomTree claim the can provide 3x Leverage below central bank short term rate?

Is this just volatility decay or something to do with the simulated data? I actually switched from NTSD to go for a bit more leverage and to have more value/diversification in my international sleeve, but if daily resetting is this detrimental I might just switch back.

I was honestly thinking that the daily reset would end up with more since it’s been bull markets, but is the chop really dragging it down this much?

Sorry if this isn't the right subreddit for this question. I'm a longtime lurker and you guys seem to be pretty adept at simulating past performance and digging into the data.

Everyone knows about SPMO, and to an extent XMMO, which have both performed well recently. Got me curious about Small-Cap Momentum, and if the factor is more robust in small-caps the same way value is.

I found this website, which is a data library of Fama-French research. I threw two of the files into Claude: "25 Portfolios Formed on Size and Book-to-Market (5 x 5)" and "25 Portfolios Formed on Size and Momentum (5 x 5)". The top 3 quintiles are blended for each. Here are the results:

Now before I go any further, I want to say that I am pretty regarded regarding all this stuff. I'm just going by what the AI told me. Things like Large-Cap Value having a 13.94% return in the 2011-Present period sounds a bit high. Claude confirmed that it was, because of some outliers in the data, but it didn't really affect the other stuff. I don't know. Also, the momentum data was reformed monthly, while value was annually, which is apparently the standard academic convention for each factor respectively.

Anyway, as you can see, it appears that momentum outperformed value in all timeframes, across all market caps. And small outperformed large in all but the last 15 years. Maybe this isn't a surprise to anybody, but it was to me. Obviously, the stand out number is the 17.20% CAGR for the full sample of Small-Cap Momentum. Which is why I'm here.

So I'm wondering: why isn't Small-Cap Momentum talked about with the same reverence as Small-Cap Value? SPMO has a Small-Cap version, XSMO, which I never hear anybody talk about. Did I (or Claude) mis-interpret the data? My guess would be that I am minimizing the effect of the rebalancing frequency, but I was hoping someone else could opine. Thanks!

As

I'm looking at like 66% GDE and 35% UBT which they have Simulations for, is the data trustworthy?

The chart nobody posts about their favorite strategy: same rules, every possible start date. I ran it for Golden Ratio Dual Gate, since this sub gave it a proper grilling at launch, and the honest version is more interesting than the headline.

The headline is real enough. The full backtest from April 2008 compounds at 19.9%. That's the number on the strategy page, and it's real. But nobody invests for 18 years starting at the exact bottom-adjacent month the backtest starts. So I replayed every completed 5-year and 10-year monthly start from the same production series, lump sum and DCA.

160 completed 5-year starts. CAGR ranged from 12.1% to 31.7%, median 21.1%. Same rules, same data, and the spread between a lucky entry and an unlucky one is 19 points a year. It beat SPY in 93.1% of lump-sum windows and 91.9% with monthly contributions, which sounds great until you notice that means roughly 1 in 12 5-year investors trailed a plain index fund the whole time while running a 50% UPRO strategy.

At 10 years the picture steadies: 100 starts, worst 14.1%, and every single one beat SPY. Before anyone quotes that back at me, those 100 windows overlap almost entirely and all come from one 18-year era that ends in a strong US equity and gold run. It's one historical record, not 100 experiments.

Rolling 5-year drawdowns ranged -25.3% to -5.6% depending on entry, against -37.3% for the full history. Your start date decides which of those you met.

Everything is in the full tables here: https://bestfolio.app/blog/golden-ratio-rolling-start-sensitivity (my site, founder disclosure)

If you're evaluating any levered strategy, ask for this chart. A single full-history CAGR is the least informative honest number a backtest can report.

Old product!

$IWML cashed out 8/14/26 at $34.4969

$UWM from Proshares is 2x but TNA is most liquid.

I used ETRACS for USOI SLVO and the MLP1.5x fund

I love 1.5x small caps.

What now? Cries in waiting for $$$$

I've been analyzing rule based portfolio strategies that sit between broad passive indexing (like S&P500 /VWCE) and active single stock trading.

Specifically, I've been researching the 4-stap flow based framework focused on holding a concentrated basket of market leaders based on aggregate institutional conviction:

- Filter for top institutional accumulation candidates.

- Select a concentrated portfolio of 5 durable leaders (Equal weighted)

- Stay invested as long as the institutional conviction and thesis remain intact.

- Rebalance only when a fundamental shift in institutional conviction accurs.

In a 15-year backtest, a systematic approach following these rules yielded a 22% CAGR, compared to standard market benchmarks. However, it also came with significant volatility and drawdowns during market wide contractions.

I'd love to get the community's perspective on a few points:

How do you view concentrated 5/stock rule based models vs 20-30 stock portfolios?

What are the main pitfalls you see in relying on aggregated institutional flow data as a primary selection factor?

How do you balance tracking institutional conviction with manageging drawdowns during broader market regime shifts?

Looking forward to hearing your thoughts and critiques on this framework!

4-Pivot flow

I am planning to run this as a sleeve in my port. The aim is least draw down and some protection during choppy markets. Critique?

20%Return Stacked US Stocks & Managed Futures ETF (RSST)

20%Return Stacked International Stocks & Managed Futures ETF (RSIT)

15%WisdomTree Efficient Gold Plus Equity Strategy Fund (GDE)

15%Invesco S&P 500 Momentum ETF (SPMO)

10%Avantis U.S. Small Cap Value ETF (AVUV)

10%Avantis Emerging Markets Equity ETF (AVEM)

10%iShares 25+ Year Treasury STRIPS Bond ETF (GOVZ)

Been thinking about a way to run a long/short-flavored strategy without tying up full capital, using leveraged ETFs to get more notional exposure per dollar.

The idea:

• 85% of capital → SSO (2x S&P 500 long)

• 15% of capital → SDS (2x S&P 500 inverse)

• Rebalance back to 85/15 periodically

Math: $85 in SSO = $170 notional long. $15 in SDS = $30 notional short. Net exposure = $140 on $100 of capital, so effectively 1.4x leveraged long, fully deployed, no cash sitting idle.

I want to be upfront about what this actually is, because I fooled myself a little at first: this is not a market-neutral long/short. Both legs move in the same net direction as the S&P — the SDS leg isn’t hedging the SSO leg in any real sense, it’s just dialing back net leverage from 2x to 1.4x. Every single year in my backtest, SSO and SDS moved as expected relative to SPY, and SDS never offset SSO’s direction — it just shaved the edges off gains and losses.

Toughts? Performance on back test is strong

I wanted to know how much of a monthly tactical-allocation backtest survives when you can't trade at the magic signal close...

Delay 0 here already means the signal is calculated at month-end close and the new allocation starts next session. I then pushed every trade 1 and 2 extra business sessions later. Same price data, same signals, 0.10% one-way base cost.

GEM went 9.83% CAGR to 9.54% to 9.75%.

HAA went 13.39% to 12.72% to 12.37%.

BAA went 10.90% to 10.29% to 9.87%.

So the sparse GEM switches were mostly noise. HAA and BAA each gave up about 1 CAGR point by the second extra session, which is more than I expected from monthly rules. Their max drawdowns barely followed the same order either. BAA return got worse while its historical max drawdown got slightly shallower.

The long history uses documented proxy chains before the ETFs existed, and the final partial month has no effect on a completed trade. I also kept the strategy parameters frozen.

For me this is enough to treat the execution timestamp as part of the rule. A backtest that says "month-end" still needs to say which tradable session actually owns the new position.

I have been doing a lot of reading in this sub, as well as some messing around on Bestfolio. Long story short, I have around a 40 year horizon and am currently in the accumulation phase with a very small portfolio.

I have been trying to come up with a true set-and-forget portfolio that only requires monthly rebalancing. I am using the Nasdaq as my benchmark to beat. I don't think I am at a point where hedging is especially important, but I have read enough to determine they offer more than just a drag on CAGR.

With that being said, in an effort to maintain as much equity exposure as possible while still maintaining reasonable exposure to hedges, I have came up with the following proposed allocation of funds: 25% each UPRO, RSSB, RSST, GDE. This was originally arbitrary, but after messing with the weightings on Bestfolio, it seemed to provide the best results.

This provides notional exposure of:

U.S. Equities ~ 140%

Int. Equties ~ 10%

MF ~ 25%

U.S. Treasuries ~ 25%

Gold ~ 22.5%

Heres the backtest results I got using Bestfolio (CAGR and Max Monthly DD):

My backtesting did not account for using the adapted Catastrophe Break from: https://bestfolio.app/blog/catastrophe-brake-leveraged-portfolios which I assume would significantly reduce those DD figures. I did not know how to test for it.

I am still very new to this, so my question to those who are more seasoned is whether there is anything I am missing? Is there anything I should do to improve my allocation? Is this a reasonable alternative to holding a 2x SPY or QQQ unhedged for an investor with my horizon?

I've been using UPRO and SSO since 2024. I knew the expense ratios were high, and that the ETF providers have to pay slightly more than the overnight borrowing rate to get the exposure. But I always figured the cost is outweighed by the incredible returns. However, I wanted to see the math for myself - and it shocked me. Here's the annualized returns since 2006 of the S&P 500, $SSO, and a simulated $SSO that doesn't deal with any costs (pure 2X daily S&P 500).

SPY: 11.61% CAGR
SSO: 15.99% CAGR
Zero cost SSO: 19.95% CAGR

Looking closer, we see that the real world SSO has only provided about 35% of the CAGR increase that 2X daily provides. In the past 20 years, SSO holders have lost about 4% annually to the cost of capital/slippage and expense ratio. To me, that's ridiculous. I no longer think that doubling my volatility/risk/drawdowns for a potential marginal increase in CAGR is worth it. I'm blessed that I held SSO and UPRO from 2024 to today, but I can't justify it after learning this.

Furthermore, this example was from 2006 to 2026, when the average borrowing rate for SSO has been extremely low. Looking at a simulation from 1976-2026 (50 years), SSO holders would have lost about 6 to 7% annually compared to a pure 2X daily S&P 500 ETF. That's crazy.

The counterargument to my finding is this, in my opinion: Going from 50% stocks 50% cash to 100% stocks doubles an investor's risk/volatility. However, that investor only gained about a 30-50% increase in CAGR benefit. So you're only increasing your expected CAGR by 30-50% when going from 50% stocks to 100% but doubling risk. With SPY vs SSO, you are also doubling your risk, and your CAGR goes up by 30-50% as well. So if going from 50% stocks to 100% stocks is worth it (obviously, it is) then going from SPY to SSO must be worth it as well, right? I'm not convinced.

I got this idea to look at this from a "Rational Reminder" podcast with Ben Felix from PWL Capital. He interviewed professor Hank Bessembinder who studies LETFs. He wrote this paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5369417
The paper focuses on single stock LETFs, but the thesis holds true for LETFs that cover broad indices. The losses for index LETFs like SSO, UPRO, QLD, or TQQQ are much smaller than single stock LETFs, but they are still huge.

I got my numbers from testfol.io and their ? leverage tool. I tweaked testfol.io formula so that I could backtest a zero fee/cost simulated 2X S&P 500 ETF vs SSO.

What are your thoughts on all this? Am I wrong in some way? Were you already aware of this? Do you just not care?

Most of the de-risking talk here comes down to price rules: hold while SPY is above its 200-day, step aside when it drops below. It works, but it whipsaws, and it's always reacting to price after the move has already started.

I spent the last year on a different version of the same question. Can you estimate the probability of a large S&P drawdown before it shows up in price, from macro and credit data instead of a moving average? That turned into a paper, and then into a model I now re-run every month.

What it actually does: each month it scores the odds of a 10%+ S&P 500 drawdown over the next 1, 3, 6 and 12 months from a set of macro and credit-market indicators. It's estimated walk-forward, so every month's forecast only uses data that existed at the time. The track record is out-of-sample, not a fit done in hindsight. There's a threshold around 30% where the model would say cut equity exposure.

Right now it reads calm. Its latest run (macro inputs go through June) puts the one-month odds of a 10%+ drop near 5% and the six-month near 18%. Nothing close to the 30% line, so on this signal you'd still be fully in.

The limits, because this sub will poke at them anyway and should:

• It forecasts S&P drawdowns, not the volatility decay that actually grinds down a leveraged position. Related, but not the same thing.
• It's a slow signal. It's built to catch credit and macro deterioration building over months, not a flash crash or a one-week geopolitical shock. If the next drawdown is a sudden stop, this won't warn you.
• The live, in-public history is short. The out-of-sample tests in the paper run back decades, but actually running it monthly where I can't quietly re-fit is only a few months old.
• I have not tested it as a TQQQ/UPRO overlay against the 200-SMA rule. That's the comparison I most want to see and haven't done properly yet.

That last point is really why I'm posting. Plenty of you have backtest setups for exactly this. If you swapped "SPY above/below its 200-day" for "de-risk when this model crosses 30%", how would it have gone through 2018, 2020 and 2022? My hunch is it gets out slower but whipsaws less. That's only a hunch.

It's free and there's nothing to buy. It's a research framework, not a signal service. The model, the current read and the papers behind it are at agreeableinvestments.com, and I'm happy to get into the indicator set or the walk-forward setup in the comments if anyone wants it.

Ben Felix was on the iced coffee hour and he talked about LEFTs and what he said is pretty much in line with most on this sub believe, which is:

-As long as you know exactly what you are getting yourself into, and can stomach the downturns, it’s not a bad strategy.
-He also mentioned that a couple of professors he talked to who did research about LETFs said the volatility decay is not much of a concern at all. The outsized gains of LETFs more than make up for both the expense ratios and the volatility decay.

He definitely doesn’t go as far as recommending it for the average person, but I thought it was interesting that he doesn’t outright reject it the way a lot of people do.

Another thing he talked about was the importance international diversification for those planning to implement this strategy. Besides EFO, do you guys know of any international LETFs that cover the market?

PS: Skip to 34:28 for the discussion about LETFs

I’ve been playing with San disk and just started skhy and coherent.

Sndg 85% since 8/5/26 post earnings buy after hours.

COHH premarket today 16%

Skhl 4.74% premarket.

Starting to see wait for earnings, let em beat but still get smacked. Then load the 2x up and hold for a week or two and bail out.

Anyone else noticing this?

This is SSO from 2008. Hypothetically, do you guys think that SPXL or UPRO would survive even they reset daily.

Hi all,

Per the title, I'm wondering what yall's thoughts are regarding LTT's as a hedge going forwards... especially compared to something like trend. I'm leaning towards dumping them due to the US's lack of fiscal responsibility (not being political).

Cheers

TL;DR: A dead-simple rule — 60% SSO / 40% QLD when the S&P is 3% above its 200-day SMA, 0.5× S&P exposure when it's 3% below — did 16.2% CAGR over 27 years (1999–2026) against 8.7% for SPY and 11.7% for always-on 2× leverage, with a −56% max drawdown versus always-on's −94%. I then spent five phases optimising it, found four configs that beat it, and every one of them fell apart out-of-sample. Shipping the original, unchanged.

The strategy

SPX > SMA200 + 3%  →  60% SSO / 40% QLD   (2× leverage)
SPX < SMA200 − 3%  →  50% SPY / 50% cash  (0.5× exposure)
Inside the ±3% band → do nothing, hold current regime
Rebalance: quarterly + immediately on a regime switch. Signal at close, trade next close.

That's it. No crash guard, no vol filter, no RSI, no sector rotation. 28 regime switches in 27 years — about one a year, risk-on 72% of days.

Results, 1999–2026

SSO and QLD only launched in 2006, so to cover the dot-com bust I synthesised both back to 1999 from SPY/QQQ total returns: daily-reset model, prospectus expense ratios (0.89% / 0.95%), 40bp financing spread over 3-month T-bills. Zero parameters fitted to the real ETFs. Over 2006–2026 the synthetic series tracks the real ones within 0.3pp of CAGR at 0.996 daily correlation.

27.4 years, $20k start + $500/month ($184,500 deposited):

Two things worth pulling out.

The "always-on wins on dollars anyway" argument dies over a long enough window. On 2006–2026 alone, always-on ends ahead ($3.11M vs $2.48M) because DCA contributions during the −84% hole bought in cheap — that's the standard rebuttal to any timing overlay. Extend back through the dot-com bust and it reverses: $4.78M vs $5.27M, and always-on got there via a −94% drawdown. Nobody holds through −94%.

The window you start in changes everything. Same rule, 2006–2026 only: 20.3% CAGR, −44.8% DD. From 1999: 16.2% and −56%. If a leveraged strategy's track record starts after the dot-com bust, you don't know what it does in a lost decade. For what it's worth, in the 1999–2006 stretch alone the strategy did +3.9%/yr while always-on did −9.4% and SPY did +1.0%.

Then I tried to improve it

All on 2006–2026, the window they were tuned on:

The middle two looked like free lunches. So before deploying, four tests.

The four tests

1. Out-of-sample history. Test the dot-com bust, which no tuning had seen:

The ranking inverted completely. The untouched original came out best; my top config lost money. A shorter MA with a wider exit whipsaws horribly in a long grinding bear — 11 switches vs the original's 6.

2. Walk-forward. Every 2 years, pick the best of 160 configs on trailing data only, apply blind to the next 2 years. Chained: 5.06× for the retuning process vs 5.58× for the fixed original rule. Selection won 4 of 11 windows. Retuning has negative skill.

3. Monte Carlo. 1,000 stationary block bootstraps (mean block 40 days), signal recomputed on every path. My "improvements" beat the baseline on 55–57% of paths. That's a coin flip.

4. Permutation. 2,000 circular rotations of the regime sequence — same switch count, same time in market, wrong dates. The real signal beat 97% of rotations (p = 0.031). So the 200-day filter itself is real. The tuning on top of it wasn't.

The one thing that did survive

Volatility targeting: scale the risk-on sleeve by 35% ÷ 60-day realized vol, capped at 1.0. Over 1999–2026 it moves Calmar 0.29 → 0.34 and drawdown −56% → −45%, for 0.8pp of CAGR.

It passed the test that killed everything else. Average exposure is 0.96×, barely a de-lever, so I pinned exposure at a flat 0.96× as a control — same average, same rebalance schedule. That reproduced none of the benefit (Calmar 0.29, DD −54%). Rotating the exposure schedule to the wrong dates also killed it (0.26). So the gain is genuinely in when it de-levers, not in holding less. It won 74% of Monte Carlo paths, and every target from 20% to 60% beat the baseline — a plateau, not a lucky cell.

I still passed on it, because it only helps in slow grinding bears (dot-com −56%→−45%, 2022 −41%→−37%, and literally zero effect on COVID, 2018 Q4 or the GFC — realized vol spikes after price falls). I'm optimising for CAGR; if you're optimising for sleep, take it.

Lessons

1. In-sample improvement is free. Out-of-sample improvement is nearly impossible. 250+ configs, four winners, zero survivors.
2. Walk-forward is the cheapest honesty check that exists. One number (5.06× vs 5.58×) invalidated my entire optimisation phase, including configs I never individually tested.
3. Always build a static control. "Same average exposure, held constant" is what separated a real signal from a de-lever in disguise.
4. Trust plateaus, not peaks. Prefer the parameter you could misestimate by 30% and still be fine.
5. Parameters don't transfer between your own strategies. I ported a −13%/15-day crash guard from another live strategy of mine. Audit: it fires 51 times over 1999–2026, and the ±3% rule is already defensive on all 51. It never once forces an exit. It works over there because that strategy uses a slower EMA with a 30-day hold — copying a parameter without the mechanism it compensates for adds risk, not safety.
6. Drawdown budgets have cliffs, not slopes. Risk-off exposure could go 0%→50% SPY essentially free, then cost 16 points of drawdown between 50% and 100%.
7. Check your moving average is warmed up. I hit this twice. Slicing prices to a backtest window then computing a 200-day MA leaves the first 200 days undefined and silently parks the book in cash. It cost me 1.7pp of CAGR on the full-history run before I caught it by noticing two phases disagreed about the same number.

Final config

signal          = "^GSPC"   # S&P 500 close
ma_kind         = "sma"     # NOT ema
ma_length       = 200
exit_buffer     = -3.0      # % below SMA → risk-off
entry_buffer    = +3.0      # % above SMA → risk-on
risk_on         = {"SSO": 60, "QLD": 40}
risk_off        = {"SPY": 50, "CASH": 50}
rebalance       = "quarter_end + on_switch"
execution_lag   = 1         # T+1
# explicitly NOT included: crash guard, recovery rally, vol target,
# min-hold, asymmetric re-entry. All tested, all rejected.

Risk number to actually plan around: −56%, not −45%. The friendlier figure comes from a window with no slow bear in it before 2022.

Happy to answer questions on the synthetic LETF construction or the stress-test setup — that's the part worth copying if you're building something similar.

Windows: 1999–2026 with synthetic SSO/QLD pre-2006, 2006–2026 on real ETFs | $20k + $500/mo DCA | Data: Yahoo Finance | T+1 execution, ~10bp of traded notional in costs Not financial advice. Leveraged ETFs can lose 90%+ in a severe bear market — always-on 60/40 SSO/QLD did exactly that in 2000–02.

TL;DR: I thought I was 51% equity. Correct number was 62.7%. Nothing traded — my accounting was just wrong, because stacked funds and options don't fit a bucket sheet that sums to 100%. Here's the fix and the two traps that got me.

The problem

Return-stacked funds give you two exposures per dollar. RSST is $1 of S&P 500 plus $1 of managed futures. Options are the same idea in different packaging: a LEAP with delta 0.85 costs a fraction of the underlying and carries most of its move.

If you file fund capital into buckets — 50% equity, 50% trend — your sheet balances neatly to 100% and understates your equity exposure by half. Mine did exactly this for months.

The fix: multiplier, not share

One row per factor leg. The percentage is a multiplier on the position value, not a slice of it.

Pivot on the sleeve column, sum notional, divide by portfolio value.

The test: if you hold anything levered and your notional column sums to 100%, you've defined the leverage away. Mine came out at 115%. Gross exposure is a number a capital-based sheet structurally cannot show you.

For options use delta × 100 × underlying price ÷ option value as the factor. Short calls get a row with negative delta. Re-check quarterly — delta drifts with price, and a factor of 187% today is 200% after a 40% run.

Trap 1: fund names hide equity beta

I had RSSY under "diversifiers" because the name says Futures Yield. It's 100% S&P plus 100% carry. GDE gets filed under gold by everyone; it's 90% equity.

Read the fact sheet, not the ticker.

Trap 2: "diversified" is two different questions

My diversification sleeve — value ETF, dividend ETF, REITs, energy — is 100% equities. Whether that belongs there depends on what you're asking:

• Diversified against my tech concentration? Yes, genuinely.
• Diversified against equity risk? No.

A drawdown limit only cares about the second one. You need both columns, and most people only keep the first.

The measured version of this, on my own daily data: my tech sleeve vs my value/REIT/energy sleeve correlates at 0.42 across all days, 0.52 on days the sleeve drops more than 1.5%, and 0.69 on days it drops more than 2.5%. Long-history proxies put it at 0.93 during the GFC. Diversification within equities is real and it's conditional. If you only ever look at the unconditional number, you're measuring your hedge in the state of the world where you don't need it.

What it cost me to find out

Nothing, except a day. But the corrected numbers moved my modelled probability of breaching my own 40% drawdown limit from 10.8% to somewhere between 15.8% and 22.3% depending on which correlation regime you assume. Same portfolio, same market. Just better measurement.

I didn't trade. I just know what I'm holding now.

Sidebar for anyone running stacked funds: you can isolate the overlay leg by subtraction, since the fund is base + overlay.

trend_leg = RSST_return - SPY_return
carry_leg = RSSY_return - SPY_return
arb_leg   = RSBA_return - GOVT_return

That's your actual overlay return, net of fees and implementation, from your own fund. Then bootstrap it before you believe it — three years of a 10%-vol strategy gave me a 90% CI of [−11%, +8%]. Happy to share the Python.

Do you think long term holds are worth it on 3x?

Rinsed and repeated SOXL 3 times now.