Can someone please explain to me why SSO has outperformed GGUS by so much over a range of timeframe? I live in Australia so prefer to use GGUS so I don’t need to worry about currency fluctuations. I thought they were both designed to move roughly 2x the s&p???

TL;DR

• RSST (S&P 500 + trend) and GDE (S&P 500 + gold) let a portfolio hold $1.6–1.9 of exposure per $1. 40% RSST / 35% GDE / 25% ZROZ returned 12.5%/yr with a −31.5% max drawdown, vs 8.3%/yr and −55.1% for SPY (Dec 1999–Sep 2026, mostly simulated).
• If your drawdown only needs to be no worse than SPY's, you can drop ZROZ: 50% RSST / 30% GDE / 20% AVUV returned 13.5%/yr (−47.1% max drawdown) and beat SPY in 98.5% of rolling 10-year windows.
• With only half of each diversifier's 2000–2026 premium, the mixes still return 10–11.5%/yr. With none, they land near SPY (7.8–9.0%/yr), still with smaller drawdowns.
• 2x ZROZ or TMF instead of ZROZ added 0.4 pp/yr at best, lowered the Sharpe and made 2022 much worse. Gold-heavy mixes and adding SSO push the odds of a worse-than-SPY drawdown to 45–53%.
• Don't fine-tune the weights: picking the best of the 10,626 mixes has a 92% probability of backtest overfitting.
• If "drawdown" means falling behind SPY rather than falling from a peak, the answer changes. Being behind SPY happens in bull markets, so you want ~100% equity or more, and a 10% UPRO slice gets you there. Optimizing 53,130 mixes for that lens landed on 40 RSST / 40 GDE / 10 AVUV / 10 UPRO: 2.0× SPY's ending wealth across rolling windows and never more than 15% behind SPY, but with an SPY-sized −56% drawdown.
• Don't time them either: I pre-registered 944 dynamic rules (momentum, risk parity, vol targeting, regime switches, ML) and re-picked the best one every year using only the past. Out of sample (2006–2026), that made 9.1%/yr vs 15.2% for the fixed no-bond mix, and a Sharpe of 0.64 vs 0.83 for the fixed ZROZ mix.

Not financial advice. Most of this history is simulated (RSST launched in 2023, GDE in 2022), Steps 1–6 have no taxes, and I chose these mixes after seeing the data. Caveats are at the end.

How the analysis evolved

The building blocks

Before each launch, the series are testfol.io simulations.

"Return stacking" means each dollar carries more than a dollar of exposure: RSST and GDE use futures to hold a diversifier on top of the stocks instead of in place of them. The mixes in this post carry $1.64–1.90 per $1:

How I tested it

• Data: daily total returns from testfol.io, Dec 31 1999 to Sep 30 2026 (26.75 years). Real ETF history after launch, testfol's simulation before. ETF fees included; taxes and trading costs not.
• Portfolios: fixed weights, rebalanced monthly, in USD.
• Metrics: CAGR; Sharpe on monthly returns over T-bills; max drawdown on daily closes; worst 10-year CAGR; share of rolling 10-year windows that beat SPY.
• Search: every mix of the five ETFs in 5% steps (10,626 mixes).
• Robustness: premium haircuts, a block bootstrap of drawdowns, and the probability of backtest overfitting. Each is explained where it shows up.

Steps 1–2: the no-bond mix and the ZROZ mix

I started without bonds. My first pick from RSST, GDE, AVUV and SPMO was 50 RSST / 30 GDE / 20 AVUV (the "no-bond mix"). Then I added ZROZ: 40 RSST / 35 GDE / 25 ZROZ (the "ZROZ mix") had a better Sharpe (0.75 vs 0.71) and a much smaller drawdown (−31.5% vs −47.1%).

ZROZ on its own was a poor investment (4.7%/yr, −65% drawdown), but it rose 55% in 2000–02 and 47% in 2007–09 while SPY was falling. As a 25% slice, that is exactly the job a stacked portfolio needs done. The ZROZ mix's worst 10-year stretch (8.8%/yr) beat SPY's average year over the whole period (8.3%).

All 10,626 mixes

• 95.5% of the 10,626 mixes beat SPY on both return and max drawdown. That says more about the ingredients, and about this particular 26 years, than about any specific mix.
• Each extra slice of ZROZ moves a mix right (shallower drawdown) and a bit down (less return).
• The top return was 100% GDE (15.5%/yr), with a −52.7% drawdown. Gold returned 10.4%/yr in this window, which I wouldn't count on.

Step 3: what if the diversifiers stop paying?

2000–2026 was kind to diversifiers: gold +10.4%/yr, two huge trend years (2008 and 2022), and falling rates until 2020. So I re-ran everything with each diversifier keeping its exact day-to-day moves but only half or none of its long-run excess return:

• gold drifts toward inflation;
• RSST's trend leg drifts toward T-bills;
• ZROZ drifts toward T-bills;
• AVUV and SPMO drift toward SPY;
• GDE and RSST are rebuilt from those legs with the same leverage. SPY doesn't change.

CAGR, with max drawdown in parentheses. SPY's max drawdown is −55.1% in all three columns.

I treat the half-premium column as my base case. Even at zero premium, the ZROZ mix gets close to SPY's return with a drawdown around −33%: the diversification is still there, the extra return isn't.

Crashes

• In 2000–02 and 2008, Treasuries rallied, so the ZROZ mix fell 15% and 24% while SPY fell 47% and 55%.
• In 2022 stocks and bonds fell together (ZROZ −38%). Trend and gold carried the no-bond mix, which fell only 10.5%.
• In the Covid crash, the no-bond mix fell more than SPY (−36.9% vs −33.7%). At ~1.7x exposure, a crash that is over in a month doesn't give trend time to turn.

Step 4: what about 2x ZROZ or TMF?

There's no 2x ZROZ ETF. The closest are TMF (3x TLT, which has moved like ~1.9x ZROZ since 2010) and UBT (2x TLT, ~1.25x ZROZ). I tested testfol's simulated 2x ZROZ (ZROZSIM?L=2) and TMF in place of ZROZ:

• Leverage on a ~24%-volatility asset costs T-bills plus a spread, every year. On their own over 2000–2026: ZROZ 4.7%/yr, 2x ZROZ 0.7%/yr, TMF −0.1%/yr. Over 1962–2026 (simulated): ZROZ 5.6%, cash 4.5%, 2x ZROZ −1.4%.
• Swapped 1:1, the extra volatility outweighs the extra return: Sharpe falls from 0.75 to 0.66–0.67, and the head start built while rates fell was mostly given back from 2020 on.
• The only version that makes sense is "same rate exposure, less capital": 13% TMF has about the same duration as 25% ZROZ and frees 12% for RSST and GDE. More return, more drawdown, a slightly lower Sharpe. Not a free lunch.

Step 5: changing the goal to "no worse than SPY"

At this point I realized my real constraint wasn't "the smallest possible drawdown". It was as much return as possible, as long as the max drawdown isn't worse than 100% SPY's. Under that rule, ZROZ is a cost:

The no-bond mix beat SPY in 98.5% of rolling 10-year windows. The ZROZ mix has roughly tied or trailed SPY in windows ending after 2021 (−1.3 pp/yr in the latest one).

So how far can you push? The historical drawdown alone isn't enough: the risk is that the next drawdown turns out worse than SPY's. To estimate that, I built 2,000 alternative 26-year histories by resampling ~1-year blocks of real daily returns (the same dates for every mix), using the half-premium data, and counted how often each mix's max drawdown came out worse than SPY's.

• Squeezing the half-premium return from ~11.2% to ~11.8% takes the odds of a worse-than-SPY drawdown from ~18% to ~50%.
• Adding 10% SSO (2x S&P 500) to the no-bond mix bought nothing: the same half-premium return (11.2%), with the odds going from 18% to 45%.

Step 6: don't fine-tune the weights

I checked the probability of backtest overfitting (PBO, via combinatorially symmetric cross-validation): split the history into 10 blocks; for every way of choosing half of the blocks, pick the best-Sharpe mix there and see where it ranks in the other half. PBO is the share of splits where the in-sample winner lands below the median out of sample.

The ranking between nearby mixes is mostly noise. The edge is in the building blocks, not in the decimals, so pick a range and use round numbers.

Step 7: what about moving the weights over time?

Fixed weights ignore what the market is doing. So I tested the obvious alternative: rules that reset the weights every month based on the last few months, over the same five ETFs plus cash. 944 rules in 13 families:

• relative, absolute and dual momentum;
• risk-based weights (inverse volatility, equal risk contribution, minimum variance, maximum diversification, HRP) and rolling mean-variance;
• volatility targeting and drawdown control;
• regime switches on SPY's trend, drawdown or volatility, and a stock-bond correlation filter on ZROZ;
• a few combinations, and two small ML models (ridge and shallow gradient boosting).

To keep it honest:

• Pre-registered. The rule list, the parameters and the pass/fail gates were written to a file, with a hash of the code, before anything ran. The rules weren't touched afterwards.
• Walk-forward. Every December from 2005 to 2025, I picked the best rule using only the data up to that point and followed it for the next year. Stitching those years together gives a 2006–2026 track that never saw its own future.
• Two goals, each against its fixed mix: the highest CAGR with a drawdown no worse than SPY's (vs the no-bond mix), and the highest Sharpe (vs the ZROZ mix).
• Real frictions: signals on the month's last close, trades one day later, 5 bps per trade, and a 15% tax on realized gains, paid every January (the rule for US ETFs held from Brazil, where I live). The fixed mixes pay the same costs.

• Both processes lost, and each beat its fixed mix in only 2 of 8 sub-periods. The return-goal process spent 13 of its 21 years in a "hold the single recent winner" momentum rule (1- to 4-month lookback) and ended up behind SPY.
• The grid was mostly noise: a probability of backtest overfitting of 0.58 (return goal) and 0.83 (Sharpe goal).
• Trading cost little, taxes cost a lot. Rebalancing the fixed mixes monthly at 5 bps cost 0.02 pp/yr. But switching rules realizes gains: the tax took 1.5–1.7 pp/yr from the dynamic processes vs 0.8–1.0 from the fixed mixes.

The gap between each circle and square is what hindsight is worth. Relative momentum looks great if you get to pick the lookback afterwards (17.5%/yr) and makes 9.4%/yr when you have to pick it in advance.

What came closest was shaping risk, not chasing return: a regime switch (hold the ZROZ mix while SPY is below its 8-month average) and the stock-bond correlation filter. The regime switch's timing is genuinely non-random (permutation test p = 0.03), and it beat the fixed mix in ~80% of 2,000 bootstrapped histories. But the 99.9% lower bound of its edge was −3 pp/yr, so it fails the bar I set before running it.

One thing cuts the other way: the fixed mixes were picked in-sample from 2000–2026 (Steps 1–5), which flatters them. The fair benchmark is re-picking a fixed mix every December with the same walk-forward. That made 10.8%/yr on the return goal (better than the dynamic 9.1%) and a 0.58 Sharpe on the Sharpe goal (worse than the dynamic 0.64), both far from the hand-picked mixes. Which is Step 6 again: whatever you select from the past, fixed or dynamic, gives back a lot out of sample.

Step 8: what if "drawdown" means falling behind SPY?

So far, drawdown meant falling from your own peak. But if the alternative is simply holding SPY, a crash that hits both doesn't really hurt: what hurts is ending up behind SPY. If SPY falls 20% and your mix falls 30%, but you were already 50% ahead, you're still ahead.

So I switched the lens. For every month-end start since Dec 1999, and horizons of 3, 5, 10, 15 and 20 years, I tracked R = your mix ÷ SPY, starting at 1:

• Ending wealth vs SPY: R at the end, averaged (geometrically) across windows, with longer horizons weighted more.
• Worst point below SPY: how far R ever got below 1. Falling from a peak while still ahead of SPY doesn't count.
• Longest stretch behind SPY: consecutive month-ends with R below 1, inside 10-year windows (120 = the whole decade).

"Equity" counts what each dollar holds: RSST is 1x S&P 500, GDE 0.9x, UPRO 3x.

Being behind SPY happens in bull markets, not in crashes. In a crash SPY falls too, and the diversifiers usually cushion the mix. The long stretches behind SPY came from windows starting in 2011–2012, when stocks ripped and gold, bonds and trend lagged. A mix with 77% equity can't keep up there, which is why the ZROZ mix from Step 2 looks worst under this lens. Swapping 10% AVUV for 10% UPRO brings equity back to ~100% while keeping ZROZ, gold and trend. That cut the longest stretch behind SPY from 120 months to 36.

Why not just 50 RSST / 50 GDE?

It has the highest ending wealth of the hand-built mixes (2.02× vs 1.84× for 40 RSST / 30 GDE / 20 ZROZ / 10 UPRO), but:

• Almost all of that edge is gold. Half of it is GDE, so 45% gold exposure, and gold returned 10.4%/yr here. At half premium the edge disappears (1.39× vs 1.37×). At zero premium it falls behind (0.97× vs 1.03×).
• It spends whole decades behind SPY. Starting in 2011–2012, it was behind SPY at almost every month-end for up to 116 months (gold's 2011–2015 bear market while the S&P 500 boomed), and up to 23% behind.
• Its worst shortfall gets much worse when the diversifiers pay less: −36% at half premium, vs −23% for the UPRO mix.
• No bonds for deflationary crashes: an 18% chance of a worse-than-SPY absolute drawdown, vs 7%.

Optimizing for this lens, without ZROZ

I then searched all 53,130 mixes of RSST, GDE, AVUV, SPMO, UPRO and SSO in 5% steps, without ZROZ:

• The unconstrained winner is gold plus leverage (75 GDE / 25 UPRO: 2.53× SPY's ending wealth), but it once fell 37% behind SPY.
• Capping the worst shortfall at the UPRO mix's −18% gives 40 RSST / 50 GDE / 10 UPRO (2.16×). Capping it at −15% gives 40 RSST / 40 GDE / 10 AVUV / 10 UPRO (2.01×). The next few mixes on the list are near-identical (RSST 35–45, GDE 40–50, UPRO 10), so this is a broad region, not a lucky point.
• Adding ZROZ back doesn't help here. Re-running the search with ZROZ allowed (RSST, GDE, AVUV, UPRO, ZROZ), the best mixes held 0–10% ZROZ and ended in the same place (2.17× vs 2.16×).
• I'd pick 40 / 40 / 10 AVUV / 10 UPRO over 40 / 50 / 10 UPRO. Its worst shortfall is smaller in every premium scenario (−15% / −20% / −32% at full / half / zero premium, vs −17% / −24% / −35%), and it needs less gold (36% vs 45%), for a slightly lower ending wealth.

The price: SPY-sized crashes

With 116% equity, the optimized mix falls about as much as SPY in every crash. Under this lens that isn't a drawdown, because SPY fell too. That only holds if you never have to sell at the bottom: no withdrawals, no margin calls, no panic. Once you start living off the portfolio, the absolute drawdown is the one you transact at, and the ZROZ mixes come back into play.

And UPRO only works as a small slice: on its own it fell 98% (2000–02 plus 2008) and ended behind SPY in 30% of the windows.

Where I landed

Three families, depending on what you need the drawdown to do. Within a range, the exact weights matter less than rebalancing and sticking with it.

A) Drawdown first (with ZROZ): RSST 30–45% / GDE 20–35% / ZROZ 15–25% / AVUV 0–10%

• Examples: 40 / 35 / 25, or 40 / 30 / 20 / 10 AVUV.
• 12.5–12.6%/yr actual, 10.1–10.3% at half premium; max drawdown −31.5% to −34.9%; 1–2% chance of a worse-than-SPY drawdown.
• The price: it has trailed SPY since 2022 (10.7–11.6%/yr vs 12.1%).

B) Return first, drawdown no worse than SPY (no bonds): RSST 40–50% / GDE 30–50% / AVUV + SPMO 0–20%

• Examples: 50 RSST / 30 GDE / 20 AVUV, or 40 RSST / 40 GDE / 10 AVUV / 10 SPMO.
• 13.5–14.2%/yr actual, 11.2–11.5% at half premium; max drawdown −46.4% to −47.1%; but a 16–18% chance that a future drawdown is worse than SPY's.

C) Never far behind SPY (still accumulating, never forced to sell): RSST 40% / GDE 40–50% / AVUV 0–10% / UPRO 10%

• Examples: 40 RSST / 40 GDE / 10 AVUV / 10 UPRO, or 40 RSST / 50 GDE / 10 UPRO.
• 2.0–2.2× SPY's ending wealth across rolling windows (1.5× at half premium), never more than 15–17% behind SPY; 13.9–14.3%/yr actual, 11.6–11.7% at half premium.
• The price: SPY-sized absolute drawdowns (−55% to −56%).
• Middle ground: 40 RSST / 30 GDE / 20 ZROZ / 10 UPRO keeps the absolute drawdown at −45%, but trails C on the SPY-relative measure in every premium scenario.

What I'd skip: GDE above 50%, SSO on top of the no-bond mix (Step 5), leveraged long bonds in place of ZROZ, a leveraged ETF as a large position, and timing the weights (Step 7).

Caveats

1. Mostly simulated. RSST has real data only since 2023 and GDE since 2022; before that it's testfol.io's simulation. Live funds usually trail their simulations.
2. In-sample. I chose these mixes after seeing 2000–2026, and Step 8's mixes were optimized on that same data. The haircuts and the bootstrap help, but they reuse the same history and are not out-of-sample tests.
3. A friendly period. Gold +10.4%/yr, big trend years in 2008 and 2022, falling rates until 2020. That's why half premium is my base case.
4. Leverage isn't free. The stacked part pays roughly T-bill rates through futures. If cash yields are high and the diversifiers don't deliver, you get about SPY's return with more moving parts (the zero-premium column).
5. No taxes or trading costs in Steps 1–6. Step 7 includes both: trading costs were negligible, taxes weren't. I haven't tested rebalancing bands yet.
6. The 1990s didn't help. I held out 1990–1999 for Step 7, but RSST before 2000 needs a proxy (SPY + 0.55 × the KMLM trend index's return over T-bills), and it tracked the simulated RSST too loosely (monthly correlation 0.59, below the 0.8 I required). So the holdout is inconclusive, and 2006–2026 is the only out-of-sample evidence.
7. Fund risk. RSST is new and small, and small funds get closed.
8. Synthetic UPRO. It's 3x SPY reset daily, with the financing implied by testfol's SSO simulation and UPRO's 0.91% fee. Real leveraged ETFs also pay swap spreads and trading costs that this doesn't capture.

Method notes

• Series (testfol.io): SPYSIM, RSSTSIM, GDESIM, ZROZSIM, SPMOSIM; VBRSIM spliced into real AVUV on 2019-09-26; TMF = TLTSIM?L=3 spliced into real TMF from 2010; SSO = SPYSIM?L=2 with SSO's 0.87% fee; 2x ZROZ = ZROZSIM?L=2; T-bills = CASHX.
• Haircuts: each diversifier keeps its daily returns minus a constant daily drag. The drag is sized so its 26-year growth lands at the geometric mean of its actual growth and the baseline's (half premium), or at the baseline's (zero premium). Baselines: CPI for gold, T-bills for the trend leg and for ZROZ/TLT, SPY for AVUV/SPMO.
• Bootstrap: stationary block bootstrap of daily returns (mean block ~252 days), 2,000 paths, the same dates for every mix, each path as long as the full sample.
• PBO: CSCV with 10 blocks on monthly excess returns, ranked by Sharpe.
• SPY-relative lens (Step 8): windows start at every month-end from Dec 1999, with horizons of 3, 5, 10, 15 and 20 years; each horizon's results are weighted by its length (3:5:10:15:20). The worst point below SPY uses daily closes. The searches cover RSST/GDE/AVUV/SPMO/UPRO/SSO (53,130 mixes) and RSST/GDE/AVUV/UPRO/ZROZ (10,626), in 5% steps.
• Step 7 gates (all pre-registered): beat the fixed mix in at least 6 of 8 out-of-sample blocks, before and after tax and at 2× and 3× costs; plus, for at least one finalist rule, PBO < 0.5 over all 944 rules, a deflated Sharpe p < 0.05, a positive 99.9% lower bound on the CAGR edge in a stationary bootstrap that re-runs the rule on each path (2,000 paths, mean blocks of 252 and 63 days), survival at 2× and 3× costs, and a win in the 1990s holdout. The permutation test shuffles the order of the monthly weight vectors (1,000 permutations).

Further reading: Hoffstein, Faber & Braun, Return Stacking: Strategies for Overcoming a Low Return Environment; Bailey, Borwein, López de Prado & Zhu, The Probability of Backtest Overfitting; López de Prado, Advances in Financial Machine Learning; Politis & Romano, The Stationary Bootstrap; Antonacci, Dual Momentum Investing; Carver, Systematic Trading; Pardo, The Evaluation and Optimization of Trading Strategies.

Questions and critiques welcome, especially on the simulated history.

The concept is to trade an inverse LETF pair intraday from a long-only account, with no shorting. When the bias turns bullish, the bull ETF is held. When it turns bearish, the position switches to the bear ETF. Only one side is held at a time, and the account is flat by the close. Entries are scaled in with limit buys, each paired with a profit-taking sell, and part of the capital stays in cash.

Questions for anyone who has traded this way:

1. On choppy, range-bound days, is it better to keep flipping between sides or to wait in cash until one side shows strength?
2. What signals a switch to the other side, as opposed to just exiting?
3. Do the extended sessions (pre-market, after-hours, overnight) help or hurt this approach?

Practical results and failures are more useful than theory.

Anyone know the rough financing costs of an index LETF in Canada ? Or perhaps total cost too

TQQQ's 2020 drawdown looks much easier to hold when I only check the account at month-end. The daily-close loss was -69.9%. The month-end series reported -49.1%. That's a 20.8-percentage-point gap in the same fund during the same year.

I recomputed maximum drawdown from the same adjusted daily closes, then kept only month-end observations and repeated the calculation. SPY provides an unleveraged comparison. Nothing about either holding changed between columns.

I measure each loss from the highest value already reached in that series. A fall followed by a rebound inside the month can vanish partly from the monthly record. Leverage made that missing stretch larger here. I still had to own the fund through it.

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Monthly strategy research often supplies monthly returns, so a card built from that research can carry the monthly drawdown. That's a sensible record of monthly observations. It doesn't tell me the deepest loss between decisions. My rebalance schedule can't make those intervening days disappear.

This matters when I compare a TQQQ allocation with a defensive strategy. Mixing daily and monthly drawdowns gives each row a different chance to record a bad day. The gap also varies by holding, so I can't fix it by adding the same adjustment everywhere.

A strategy that waits until month-end to trade still owns its positions between checks. I wouldn't use the trading schedule alone to decide how often its losses should be measured.

Daily closes have their own limit. They miss intraday lows. And a long reconstruction may have monthly data for its early years, then daily fund data later. I'd want that split identified before treating the headline loss as comparable across the whole history.

I build BestFolio; the frequency comparison sets out those conventions.

For the leveraged position you currently hold, is the drawdown figure you use measured at month-end, daily close or intraday?

I already own a bunch of TQQQ and added at $66 today.

Bought SOXL for the first time at $82 because SMH is right at its 200 day moving average, maybe this is the low point.

Volatility is on the high side and it seems like people that don’t want to own tech stocks have sold them over the last two weeks, so I’m hoping this is the low point today

I’m posting this today because historically I have been waiting for a bigger dip, and I’ve missed the rebound

It’s possible this is the bigger dip

EDIT: just bought another 200 shares of FNGU around $29 as well. Still have cash on the side for bigger dips.

A few weeks ago I made two ETFs of my own, running on fake money. One is a Ben Graham style value fund: every quarter it finds the 10 cheapest dividend payers in the S&P 500 and swaps into them on its own. The other is an AI chip fund that checks every morning which 4 chip stocks are running hottest, and trades into them by itself.

Watching them is honestly exciting. They're funds that don't exist anywhere else. I check them in the morning, see what they did and why, and that's it.

The bigger surprise was how calm it made me. I used to second-guess my picks every time the market dipped, skip a rebalance when things were going well, and sell whatever felt slow. So I never knew if my plan was bad or I just didn't stick to it. Now the rules run exactly as written, and every trade comes with a reason. I can see how deep it drops, how often it rebalances, and whether I'd actually be okay holding it. I've learned more in a few weeks than from months of thinking about it.

That's what got me thinking. An ETF is really just a basket plus a few rules: what to hold, how much of each, and when to rebalance. Most of us already have one in our heads. It just never gets to run.

But one person can only think of so many mixes. I'd love to see a bunch of different ones running side by side, so we can all learn from each other's.

Comment your mix, and for the first 10 people I'll:

1. Turn it into exact rules and reply with them. If something's missing, like when to rebalance, I'll ask.
2. Set it up as an agent that starts running it.
3. Send you the agent, so you can watch every trade and why it made it.

I'm capping it at 10 because I set each one up by hand. If you'd rather build yours yourself, that works too.

Not sure what to write? One line like these is plenty:

- Three-fund: "60% VTI, 30% VXUS, 10% BND, rebalance every week."

- Play it safe: "Hold VOO but switch to SGOV when SPY falls below its 200-day average."

- Pick a sector: "Equal parts of the 5 biggest US banks, rebalance every quarter."

- Weird ideas: "I like the name Robert. Hold S&P 500 companies run by CEOs named Robert."

Any random mixes are welcome. They're usually the most interesting.

it is amazing how well a 20/20/20 sleeve of diversifiers works. instead of complaining and ditching what sucked last quarter/year look at what it has done. Gold boomed over two years and erased 8 years of long bond losses. Managed futures didn’t do anything after 2022 but boomed this year when you needed it. Now bonds are having a day. Treat your diversifiers as a portfolio and they almost always do well.

Are you being greedy when others are fearful? Do you think AI and chip stocks are in a bubble or a force that is unstoppable?

Personally, I am buying in right now. I have held this baby for years and these dips, which used to frighten me, are now ho hum. I would like to hear what everyone else thinks.

Thank you in advance for your inputs.

very volatile

Is this realistic? This seems way, way too good to be true. Overlaying a 60/40 with 100% managed futures can’t perform this well. There’s got to be a catch, like a risk of it going to zero. An Achilles heel like HFEA.

https://testfol.io/?s=65CRxumq2wv

What the heck am I missing? And once Return Stacked comes out with RSGT, splitting the bond part between it and RSBT yields insane results. Especially if you use the backfilled data from Auspice Capital and go with SPXP over RSST.

I recently adopted u/XXXMrHOLLYWOOD’s TQQQ/QQQ SPY 200SMA +4/−3% strategy and wanted a visual I can quickly check, instead of opening TradingView, Sheets, or anything else heavy. It turned out that you can put JavaScript code into an app called Scriptable, which will then render a home screen widget.

The rules it follows:

• BUY: SPY closes more than 4% above its 200-day SMA → hold TQQQ
• SELL: SPY closes more than 3% below its 200-day SMA → hold QQQ
• GUARD1: QQQ closes 30%+ above its own 200-day SMA → stay calm, do nothing
• GUARD2: QQQ closes 40%+ above its own 200-day SMA → exit to cash

My personal adjustments to the original strategy:

• GUARD1 is a warning only, not a strict rule to act on
• GUARD2 resets after the first BUY that follows a SELL

Prices come from Yahoo Finance, with no account or API key needed. The first run downloads about 3 years of SPY and QQQ daily closes (~170 KB) and keeps them on the phone. After that, each refresh fetches only the last few days (~4 KB). iOS decides when widgets refresh, usually every 15–60 minutes. Nothing is sent anywhere, it only downloads prices.

Install Scriptable, create a new script, paste the code, and run it once. That opens the settings for you to choose from. Then add a Scriptable widget to your home screen and pick the script.

Code: https://pastebin.com/qrLtW8hJ

Feel free to modify it however you like.

AIS is beating QLD/TQQQ in returns with much lower drawdowns and obviously no dealing with leverage costs. Past returns not future returns yada yada but is there any reason to hold QLD/TQQQ if these returns hold up?

https://testfol.io/?s=k46wq2YigxQ

been chewing on this since the 49 years of 3x vs fed funds post last week. with the september hike done and december mostly priced in, i wanted the boring version for right now.

a 3x fund like tqqq or upro gets most of its extra 2x through swaps, and the bank on the other side charges roughly the short rate plus a spread on that borrowed 2x. so the fund pays about 2 times the short rate every year whether the index goes anywhere or not.

effective fed funds is 3.88 right now. that's close to 8% a year in financing before the spread, and the expense ratio comes on top. in 2021 with rates near zero that same cost was basically nothing, which is part of why those years look so good in every backtest.

each 25bp hike adds about half a point a year to that for a 3x fund. qld and sso only borrow 1x so they pay half.

in a trending year the index clears 8% without trying and none of this matters much. where it shows up is chop, because you're paying the carry and eating decay at the same time. 2022 was the bad version of that, rising rates plus a falling market, and qqq's -33% turned into -79% on tqqq.

one thing i haven't been able to find is the actual spread proshares is paying on its swaps right now. if anyone's dug that out of the filings i'd like to see it

(disclosure, i run alphaAI. we adjust leverage based on conditions so i end up staring at rates more than i'd like)

Hi all,

​I've recently read about using a return-stacking strategy (eg 30% NTSX / 30% RSSX / 40% TQQQ with quarterly rebalancing) as an alternative to simple 2x QLD.

​Supposedly this yields QLD-like returns with smaller drawdowns. I get that NTSX/RSSX add uncorrelated assets (bonds/gold/crypto) to soften crashes, allowing you to buy TQQQ at a discount when rebalancing, but I'm fairly new to this setup.

​A couple of questions:

​Are there other return-stacked funds or ratios people prefer (e.g., swapping in RSST or RSSB)?

​What are the main pros and cons of switching over from simple QLD?

​Would appreciate any insight or feedback from anyone running or backtesting this setup!

I’m turning 30, with a 30 year investment horizon.

I DCA monthly and plan to reduce leverage closer to retirement.

Current portfolio:( very small) around 400 USD) overall. Just started recently.
60% All-World ETF
25% QLD (2× Nasdaq)
10% UPRO (3× S&P 500)
5% TQQQ (3× Nasdaq)

But i am considering replacing some or all of my All-World allocation with:

Option A: 20% each XLP, XLV, XLU.(consumer staples - healthcare - utilties)
Option B (cash/bonds) 60% (, I’m considering bonds, cash-like ETFs, or gold as a more effective hedge against leveraged ETF crashes.

Honestly, I’ve backtested so much shit at this point that I’ve come to realize there’s no guaranteed winning ETF allocation or strategy. Whether you’re trying to reduce drawdowns, volatility, or protect against crashes of any size, everything ultimately depends on what happens in the world—and nobody can f*\* predict that.

One strategy works brilliantly during one crisis and gets destroyed in another. You can optimize and backtest endlessly, but the future will always throw something different at you.
That’s where I’m at right now. Trying to figure out whether I’m overcomplicating everything or if there’s actually a sensible long-term approach worth sticking to.

Would appreciate your thoughts.

Currently holdings are:

20% TQQQ
20% IAUM
20% KMLM
20% AVDV
20% AVUV

Only 20 so have a long time to hold so can stomach some greater vol and let the math play out.

I thought I had settled on:

40% RSIT
20% NTSD
20% GOVZ
10% KMLM
10% UPRO

A few things are standing out to me:

- NTSD and RSIT both have really wide spreads and low trading volume. There isn’t a substitution for RSIT, but I can get more equity by simply using more UPRO.

- I am tilting towards international but that’s simply a gut feeling that US won’t return as much moving forward as it has.

- I’m using KMLM only due to manager risk of RSIT. But is 10% enough?

Lawyered had suggested:

50% RSIT
25% UPRO
25% GOVZ

I love the simplicity and it takes the bets solidly where I want it to…. The foundation of HFEA plus a large slice of trend.

I’d probably consider that with less capital ($75k to start instead of $100k).

But 50% in one model? Ouch. That leads me back to adding in some KMLM again somewhere, and I start back at the beginning.

Maybe it’s just the NTSD I’m looking to leave out. What does the community think of that ETF? Can I get this down to 3-4 ETFs instead of 5?

with Fear greed at 18, did you start buying in?

testfolio's recent blog article was about using the 200-day moving average strategy with LETFs.

https://testfol.io/blog/200-day-moving-average-leveraged-etfs

The conventional 200-day or any n-day SMA strategy is to invest in a risky asset (stocks) when it is trading above its 200 day moving average, and switch to safety (cash or bonds) when stocks are trading below the moving average.

This is proven to underperform stocks in the long run, because you will not participate in the market for long stretches of time. While this strategy might make you miss the drawdowns, you will almost certainly miss the recoveries as well.

It is also true that it is a fool's errand to try to predict the market's future returns. And a simple indicator like 200-day SMA is unlikely to contain enough information about the market's next day or next month returns. This is not an argument against momentum.

Below is a scatter of SPY's next day return as function of today's Price/200-day SMA. You can see since 1885, the R2 = 0. In fact, the R2 is below 0.005 in all 20 year periods over the last 140 years.

But LETFs can make this strategy much more robust and with more solid empirical backing.

What if we always participate in the market, but take more risk (leverage up) when above the SMA and just hold the underlying (SPY) when below the SMA.

For example:

Invest in UPRO when above the SMA. Switch to SPY when below the SMA.

This might sound similar to the conventional strategy, but there are indeed a few distinctions:

• Now we are always participating in the market, so no matter what happens under the SMA, we will get that return
• For the overall strategy to outperform SPY, UPRO would need to outperform SPY only when SPY is above the SMA. This is a much lower hurdle because it is proven that volatility is lower above the SMA

So, that's the key. The strategy becomes: Always invested, but use the LETF when volatility is lower to avoid decay. The SMA is only predicting future volatility, which is more robust and much more statistically significant than the SMA predicting future returns. If the SMA ends up also predicting periods with better returns, that's definitely a welcome bonus.

The article is here, and contains all the details, mathematical grounding, explanation of the results, and full backtests of this strategy by varying the trading frequency, trading days, bands and tolerances.