You can access this dashboard in the link below

This is a follow-up to my previous post here: 40-year LETF rotation backtest — 5 strategy families, 426 configs, here's the full result

In that post, I shared a 40-year LETF rotation study across 5 strategy families and 426 configurations. The main result was what I now call Quad Risk K2 in the dashboard: a QLD/ZROZ rotation that turns risk-on when at least 2 of 4 QQQ/QLD regime signals are true. The four signals were long trend, medium trend, realized volatility, and short-term return persistence. In simple terms: hold QLD when enough risk conditions are favorable; otherwise hold ZROZ.

That original strategy was the one I felt most comfortable calling the robust anchor: it cleared the full validation stack in that study, including DSR, PBO, walk-forward, OOS, forward-stress, bootstrap, and cross-library checks. It also had strong rolling-window behavior versus SPY.

I was honestly happy with the quality of the discussion in the comments on that post. I tried to answer every comment I could, but because of my limited time and because I kept focusing on the research work behind this follow-up, I may not have replied to everyone. I did read the feedback, and this new post is partly a response to the natural next question: can the original idea be improved, extended, or stress-tested further?

So the goal here was not to throw away the original result. The goal was to keep Quad Risk K2 as the anchor and continue searching for improvements and evolutions around it: rearm logic, TQQQ turbo windows, broader technical-vote systems, modern QLD/TQQQ variants, and simpler LRS baselines.

I built a small interactive web dashboard for comparing a set of LETF rotation strategies I have been researching. The app is focused on Nasdaq/S&P leveraged ETF rotation ideas: QLD/TQQQ, SSO/UPRO, defensive legs, trend/momentum/volatility votes, drawdowns, and rolling window behavior.

Link: https://letf-rotation-research.noletovictor.com/

>Important caveat up front: this is research, not financial advice and not a deploy recommendation. Several of the high-CAGR variants look economically interesting, but the formal validation stack still blocked promotion because DSR/PBO failed after accounting for the number of trials. I am sharing the tool because the comparisons are useful and because LETF strategy discussions are more productive when people can inspect drawdowns, windows, and benchmarks instead of only seeing a final CAGR number.

What The Strategies Are

The dashboard currently compares these strategies:

Full-sample metrics shown in the app use the long-history window 1986-01-03 to 2026-04-17:

Name	CAGR	Max DD	Sharpe	Sortino
Octa Price K6 QLD	32.05%	-57.81%	0.983	1.375
Octa Price K6 TQQQ	40.26%	-64.24%	0.951	1.268
Quad Risk K2	31.06%	-64.50%	0.919	1.258
Rearm T20D90	38.99%	-55.48%	0.975	1.228
Rearm T20D120	39.01%	-55.48%	0.961	1.207
Rearm T35D60	36.66%	-55.48%	0.962	1.207
Quint TrendMomVol Overlay	38.46%	-64.54%	0.872	1.084
Quint TrendMomVol K3 QLD	19.38%	-70.07%	0.668	0.907
QQQ B&H	14.58%	-82.97%	0.658	0.866
SPY B&H	11.49%	-55.14%	0.682	0.842
Quint TrendMomVol K3 TQQQ	21.48%	-87.69%	0.637	0.833
LRS SSO	13.88%	-51.67%	0.664	0.759
LRS QLD	18.33%	-82.54%	0.648	0.741
LRS TQQQ	19.94%	-94.36%	0.609	0.696
LRS UPRO	16.40%	-71.20%	0.605	0.691

• Rearm T20D120: the highest-CAGR long-history sensitivity in the final local grid. It keeps the same core Quad Risk K2 shell as Rearm T35D60, but changes the post-crash rearm geometry: after at least 20 OFF days, an OFF-to-ON transition opens a 120-trading-day TQQQ/LRS1.20 rearm window. In the 1986-2026 long-history test it reached about 39.01% CAGR, 1.207 Sortino, and -55.48% max drawdown.
• Rearm T20D90: the more balanced T/D sensitivity. Same idea as T20D120, but with a 90-trading-day rearm window. It had nearly the same CAGR, about 38.99%, with the best Sortino in the local T/D grid, about 1.228.
• Rearm T35D60: the main anchor strategy from the previous LETF rotation loop. It uses the Quad Risk K2 shell, QLD as the normal risk-on leg, ZROZ as the defensive leg, a rate/vol cash override, and a T35D60 post-crash TQQQ rearm window with LRS1.20. Long-history result: about 36.66% CAGR, 1.207 Sortino, and -55.48% max drawdown.
• Quad Risk K2: the simpler four-gate shell. It turns risk-on when 2 of 4 filters pass: QLD above SMA250, QLD above SMA100, 21-day realized volatility below 40%, and AR(1) 30-day persistence above zero. It holds QLD when ON and ZROZ when OFF. Long-history result: about 31.06% CAGR, 1.258 Sortino, and -64.50% max drawdown.
• Octa Price K6 QLD: an 8-signal price-only vote using SMA/EMA trend filters, ROC momentum filters, and RSI14. It turns ON when 6 of 8 signals pass and holds QLD when ON.
• Octa Price K6 TQQQ: the same 8-signal price-only vote as Octa Price K6 QLD, but with TQQQ as the risk-on leg. It had the highest long-history CAGR among the listed long-history comparison rows, about 40.26%, but failed DSR/PBO validation.
• Quint TrendMomVol K3 QLD: a 5-signal vote using SMA100>SMA250, ROC10>0, ROC120>0, StochRSI14>50, and realized-volatility percentile below 70. It turns ON when 3 of 5 signals pass and holds QLD when ON. On modern Tiingo 2010+ data, it reached about 36.26% CAGR with -37.54% max drawdown.
• Quint TrendMomVol K3 TQQQ: the same 5-signal vote as Quint TrendMomVol K3 QLD, but with TQQQ as the risk-on leg. On modern Tiingo 2010+ data, it reached about 53.00% CAGR with -51.03% max drawdown. On the stricter 1986+ long-history reproduction, it weakened materially.
• Quint TrendMomVol Overlay: a hybrid that keeps the Rearm T35D60 shell but allows the Quint TrendMomVol K3 vote to act as an additional QLD-to-TQQQ turbo trigger. It improved terminal equity/CAGR versus Rearm T35D60 in some comparisons, but worsened drawdown and Sortino, so it did not dominate the anchor.
• LRS SSO, LRS UPRO, LRS QLD, and LRS TQQQ: simple Gayed-style trend baselines. If SPY/QQQ is above its 200-day SMA, hold the leveraged ETF next bar; otherwise hold cash. These are included as simple sanity-check baselines.
• SPY B&H and QQQ B&H: passive comparators so the rotations can be judged against broad equity and Nasdaq exposure, not just against each other.

What The Webapp Shows

The goal of the app is to make the comparison inspectable instead of static.

• Date range control: choose the full sample or focus on recent 5y/10y/15y/20y periods. This matters a lot because some strategies look amazing in the modern sample and much weaker once older regimes are included.
• Window Summary: quick cards showing start date, end date, number of bars, best CAGR in the selected window, best Sortino, and lowest max drawdown.
• Equity Curves: log-scale equity curves for all strategies. You can toggle individual strategies from the side table and inspect values at the cursor date.
• Drawdown Chart: synchronized drawdown plot for the same selected strategies. This is usually the fastest way to see whether a high CAGR is just hiding unacceptable path risk.
• Interactive Series Table: shows each strategy's equity at the cursor date, CAGR, and max drawdown. You can sort by cursor equity or Sortino and click rows to show/hide lines.
• Metrics Table: sortable CAGR, Sortino, Sharpe, max drawdown, Calmar, and ending multiple for the selected date window.
• Rolling A/B Comparison: choose any strategy as A and any other as B. The app builds 3y, 5y, 10y, 15y, and 20y rolling comparisons, including win-rate heatmaps and final-ratio heatmaps. This is useful for questions like "how often did Rearm T35D60 beat Quad Risk K2 over rolling 10-year windows?" instead of only asking which strategy won over the full backtest.
• A/B KPI Cards: final A equity, final B equity, A/B ratio, percent of days A was above B, and max drawdowns for both.
• Rolling Window Hover Details: hover any heatmap cell to see the exact start/end dates, A growth, B growth, CAGR for both, and the final ratio.
• Strategy Concepts Tab: plain-English descriptions of every strategy in the dashboard: the concept, the algorithm, and the current research status.

You can compare one strategy with another, and see how the differences goes through time

Why I Built It

I wanted a cleaner way to discuss LETF rotation than posting one table of top backtest results. A strategy with 40% CAGR can still be a bad idea if it only works in one regime, has catastrophic drawdowns, or loses to a simpler anchor in rolling windows. The dashboard makes those trade-offs visible.

The short version of the research so far:

• The robust long-history anchors are still Quad Risk K2 and Rearm T35D60.
• The best local sensitivity was T20D120 at about 39.01% CAGR, but it is not a validated winner.
• Quint TrendMomVol K3 TQQQ is very strong on Tiingo 2010+ data, about 53.00% CAGR, but weakens in 1986+ reproduction.
• The high-CAGR variants are interesting challengers, but DSR/PBO failures mean I would not present them as deployable systems.

I would be interested in feedback from people here, especially on better validation ideas, realistic execution assumptions for QLD/TQQQ rotations, tax/cost modeling, and whether the rolling A/B view changes how you evaluate these LETF strategies.

Discussion Question: Would You Still Follow A Strategy If DSR/PBO Failed?

This is the part I am most interested in discussing.

The strategies in this dashboard are not just top rows from a random backtest table. The better candidates generally passed several practical robustness checks:

• OOS holdout: they remained profitable on a reserved out-of-sample block.
• FWD stress window: they survived the most recent forward/stress slice.
• Walk-forward validation: most stayed positive across rolling train/test windows.
• Bootstrap checks: resampled return paths usually did not destroy the result.
• Rolling 3y/5y/10y/15y windows: the best anchors had broad positive rolling behavior, not just one lucky terminal point.

But they still failed the two gates that worry me the most: DSR and PBO.

Plain-English version:

• DSR, or Deflated Sharpe Ratio, asks: "After accounting for all the strategies/parameters I tried, is this Sharpe still statistically impressive?" A Sharpe that looks good in isolation can become much less convincing after thousands or millions of trials, because some great-looking result is expected to appear by chance.
• PBO, or Probability of Backtest Overfitting, asks: "When I split the data many different ways, do the configurations that look best in-sample also keep ranking well out-of-sample?" A high PBO means the selection process may be learning quirks of the backtest window rather than a durable rule.

So the uncomfortable result here is:

• economically, several strategies look very strong;
• mechanically, they pass OOS/WF/bootstrap-style checks;
• statistically, they still look too optimized once DSR/PBO account for the search process.

My current stance is that this makes them research-only, not deployable systems. But I am not sure everyone will draw the line in the same place.

For discussion:

• If a LETF strategy passes OOS, walk-forward, bootstrap, and rolling-window checks, but fails DSR/PBO, would you still consider trading it with reduced size?
• Do you treat DSR/PBO as hard blockers, or as warnings that should be balanced against economic intuition and simplicity?
• Is there a point where a strategy is simple enough, economically plausible enough, or robust enough across regimes that you would accept weak DSR/PBO?
• For LETFs specifically, do you think trend/momentum/volatility filters are a known structural effect, or just an overfit-prone family because everyone is searching the same indicators?

Methodology. 5 strategy families × 426 configurations × 40 years of data (1986-2026, plus synthetic backfill to 1969 for cross-validation across 4 independent datasets). Pre-registered with 7 statistical gates: PBO, DSR, walk-forward, single-block OOS, forward-stress, bootstrap 99.9% CI, and cross-library CAGR delta. Anti-overfit margin set upfront — a challenger must clear the incumbent's Sortino by +0.05, not just match it. Tier-by-tier elimination, no peeking.

Winner. Vote-K=2 regime filter on QLD (2× NDX). When at least 2 of these 4 signals are TRUE on the QQQ underlying — price > SMA(250), price > SMA(100), realized_vol(21d) < 40%, AR(1) coefficient over 30d > 0 — go 100% QLD; else go 100% ZROZ (25y zero-coupon Treasury). T+1 month-end execution.

Result on lh_56y, net of realistic annual-netting tax:

• Sortino 1.18 (edge vs SPY +0.226), Sharpe 0.83 as secondary context
• Net CAGR ~24% vs SPY ~11%; $10K → ~$60M over 40y vs SPY ~$793K (≈ 75× SPY end equity)
• Beats SPY in 99.86% of 40-year days and 100% of all 10y / 15y / 20y rolling windows (zero exceptions across 909 windows)
• Clears every pre-registered gate with margin: DSR p=0.04, PBO=0.18, walk-forward 7/8
• Composite rolling-window robustness rank #5 of 21; SPY ranks #21 of 21
• Edge survives even under per-swing worst-case tax: +0.127 Sortino — still above the +0.10 deploy bar

The rest of this post walks tier-by-tier through what won, what got killed, and the defensible-deploy case.

Disclosure first: my own allocation is 100% passive buy-and-hold today. This is research output, not advice.

https://preview.redd.it/v3eoxl1af00h1.png?width=787&format=png&auto=webp&s=c19d11f8d65973ffac949d888e637d7ab118c38f

Top 21 configs across all 5 tiers + SPY benchmark, lh_56y window, log scale. The bands of green/orange in the top half are LETF rotation winners; SPY (black) sits at the bottom.

Tier 1 — Single-LETF rotation

Question: does a simple SMA200 regime filter on a 2× or 3× LETF, with Treasury as the off-state, beat SPY 1× buy-and-hold?

Universe tested: 6 LETFs (QLD, UPRO, SOXL, SSO, TQQQ, UGL) × 6 off-state assets (TLT, EDV, IEF, ZROZ, BIL, GLD) × multiple SMA periods. 382 configurations across 4 sub-phases.

Tier 1 winner: qld_sma200_off_zroz — when QQQ is above its 200-day SMA, hold QLD (2× NDX); else hold ZROZ (25y zero-coupon Treasury).

Metric	Value
Sortino (lh_56y gross)	~1.07
Sharpe (secondary)	0.752
% days strategy > SPY	99.83%
End ratio vs SPY (40y)	60.5×
Max drawdown	-75% (2000 dotcom)

https://preview.redd.it/dov6jvpaf00h1.png?width=871&format=png&auto=webp&s=cc0dba1ce5213bd18208811478437dc786db9d25

https://preview.redd.it/1oiz1h4bf00h1.png?width=872&format=png&auto=webp&s=edc61b209a4b9bd5be2c331f67cbed7f5e35b3ef

T1 strategy family: all tested configs are shown; top configs are colored, SPY is black, and the remaining configs are faded gray. The second chart is the same universe as strategy equity / SPY equity; the dashed black horizontal line is SPY (=1.0). The winner spends 99.83% of days above SPY.

Verdict: PASSED — first config in study to clear SPY+0.05 anti-overfit threshold.

Why ZROZ over alternatives: I tested all 6 off-state candidates against all on-state LETFs. ZROZ won every single combination on net-of-cost basis. 25y zero-coupon Treasuries provide convexity in flight-to-quality regimes (1987, 2000, 2008, 2020) that shorter-duration alternatives don't. The 2022 rates crisis hurt ZROZ but hurt every other bond proxy worse.

Why QLD over TQQQ/UPRO: QLD (2× NDX) preferred over TQQQ (3× NDX) on a risk-adjusted basis. The 3× LETF has higher absolute returns but the leverage decay in high-vol regimes (-95% drawdown in dotcom) kills enough compounding that the 2× wins on Sortino.

Methodology note — why max drawdown is the wrong filter for LETFs

Most backtest reports treat max drawdown as a hard quality gate: "Strategy X has -75% MDD → reject." That logic was calibrated for stock-picking strategies in the 1× equity world. For LETFs it's misleading.

Asset-class arithmetic. A 2× LETF tracking an index that drew down 50% will draw down ~75-80% mechanically (leverage compounding decay during high-vol periods). A 3× LETF on the same drop will draw down ~85-95%. The 2008 GFC produced a 50%+ drop in SPX/NDX, so any 2× LETF strategy — no matter how good — will have a backtested MDD ≥ 75% that includes 2008 in its history. This is not a strategy quality signal; it's just how leverage works during major bear markets.

The right question isn't "how much did I lose from peak?" — it's: at every point in time, including during the deepest drawdown, was my strategy equity above what SPY 1× buy-and-hold would have given me?

If yes, the strategy is genuinely better than the benchmark — even with a 75% drawdown. If no, the strategy is actually worse, no matter how shallow the drawdown.

This is why the T1 image above plots strategy_eq / SPY_eq (renormalized to start at 1.0) instead of conventional drawdown. The T1 winner spends 99.83% of days above SPY. At its worst absolute MDD bottom (Sep 2000, -75%), strategy was still 3.1× SPY. The same SPY that anyone using "MDD > 50% = reject" filtering would have been holding instead. The reject filter would have you holding a worse alternative, not a safer one.

The study's scoring system (criterion 2) was rebuilt around pct_time_above_benchmark ≥ 95% as the strict bar, with min_relative_equity as a secondary check. Max drawdown is preserved in tables for transparency but is treated as warning-only, not gating.

Tier 2 — HFEA-style stacking

Question: does always-on leveraged stacking (60/40 UPRO+TMF or variants) outperform the rotation strategy from T1?

Universe tested: 11 configs across 6 sub-phases — classic HFEA (UPRO+TMF), weight sweeps, NDX variants (TQQQ+TMF), trinity baskets (UPRO+TMF+UGL), no-decay bond alternatives.

Tier 2 winner: hfea_ndx_tqqq_tmf_55_45, Sortino ~0.92 (Sharpe 0.653 secondary), score 51 MARGINAL.

Verdict: KILL T1→T2 FIRES — fails the anti-overfit threshold. Stacking does not beat rotation in this universe.

https://preview.redd.it/x1kaipxbf00h1.png?width=871&format=png&auto=webp&s=6a7c51285420cc28afebf864b45d5ed5367982ef

https://preview.redd.it/9i79lrecf00h1.png?width=871&format=png&auto=webp&s=0860671bf9aa7d8b5b23cc39cdf9839a04f5f97f

T2 HFEA-style basket family: all tested configs are shown; top configs are colored, SPY is black, and the remaining configs are faded gray. The relative chart shows why the T2-best compounds less than T1 despite a shallower drawdown profile.

Why stacking fails: the always-on TMF allocation acts as drag during equity rallies (the 2010-2020 decade saw TMF underperform cash), while ZROZ-as-rotation only carries duration cost during the actual risk-off windows. ZROZ has positional/temporal alpha, not carry alpha. HFEA basket spends only 59% of days above SPY (vs T1c's 99.83%).

Tier 3 — Composite signal (study winner)

Question: does aggregating multiple regime signals via a Vote-of-K filter beat the single SMA200 from T1?

Universe tested: 31 configs across 5 sub-phases — SMA + vol gate, VIX-managed, SMA + AR(1), Vote-of-K (the winning family), HMM regime classifier, plus iter 022 extended grid (12 variants) and iter 023 multi-asset (12 variants).

Tier 3 winner: qld_voteK2_sma250_100_vol21_40_ar30_off_zroz. The strategy is ON when at least 2 of 4 signals are TRUE on the QQQ underlying:

1. price > SMA(250) with 5% buffer (whipsaw filter)
2. price > SMA(100) with 5% buffer
3. realized_vol(21d) < 40%
4. AR(1) coefficient over 30d > 0 (positive momentum persistence)

When K≥2 → 100% QLD; else 100% ZROZ. T+1 execution at month-end.

Metric	Value
Sortino (lh_56y gross)	1.325
Sharpe (secondary)	0.919
Sortino edge vs SPY	+0.367
Sharpe edge vs SPY (secondary)	+0.237
% days strategy > SPY	99.86%
End ratio vs SPY (40y)	256×
All 7 statistical gates	PASSED

https://preview.redd.it/bvwg6kndf00h1.png?width=871&format=png&auto=webp&s=aa8503c617241f0dffd8c419fd05aaf90a170402

https://preview.redd.it/9kibgscef00h1.png?width=871&format=png&auto=webp&s=43f71132636d1479a984e6d47a7781b7518bb98d

All 31 T3 configs. The first chart shows normalized performance for the full family; the second chart shows the same curves as strategy/SPY equity ratio. The bold lines are the top candidates; the rest are faded. The winner sits in the highest-compounding band. The HMM regime classifier failed catastrophically (-98.7% MDD); a few K=3/K=4 strict subsets ended below SPY because requiring more signals reduces ON-time too aggressively.

Verdict: ADVANCES — first family in study to clear the inter-tier anti-overfit threshold. The sma250/100 variant is the operative Sortino winner (1.325), with Sharpe 0.919 retained only as secondary context. All 7 gates passed.

Why Vote-K=2 over single SMA: the 4 signals capture different regime characteristics (long-trend, short-trend, vol regime, momentum persistence). Requiring K≥2 means single-indicator failure modes don't take you out of the market. The 5% SMA buffer reduces whipsaw trade count by ~30% — important for net-of-tax returns.

Tier 4 — Cross-sectional ranking

Question: does a momentum-based selector across multiple LETFs beat the single-asset T3 winner?

Universe tested: 4 configs — Clenow top-2/top-3, EWMAC top-2, Clenow with vol-gate filter on a 4-LETF pool (QLD, TQQQ, UPRO, SSO).

Tier 4 winner: xs_clenow_top3_zroz_spysma200, Sharpe 0.823 secondary; did not clear the Sortino-first incumbent.

Verdict: KILL T3→T4 FIRES — cross-sectional ranking adds turnover cost without enough alpha differentiation in a small LETF pool.

https://preview.redd.it/h1edv9bgf00h1.png?width=871&format=png&auto=webp&s=18370959f2e8425f9c806c905d9604163003c542

https://preview.redd.it/1lts3otgf00h1.png?width=871&format=png&auto=webp&s=aef4573ef9fbcc82951c778c666b1e4554cc4ad1

T4 cross-sectional rotation family: the full tested set is shown in both performance and strategy/SPY form. None clear the 0.903 anti-overfit threshold; T3d K=2 incumbent remains dominant.

Why cross-sectional ranking fails: small LETF pools (4 candidates) don't give the ranker enough cross-sectional dispersion to add value over a regime filter. Designed-for-futures Clenow ranking expects 10+ uncorrelated instruments; running it on QLD/TQQQ/UPRO/SSO (all NDX/SPX-correlated) is degenerate. The strategy compounds to 26× SPY end ratio vs T3d's 256× — competent but undifferentiated.

Tier 5 — Vol-targeting

Question: does Carver-style continuous position sizing (inverse-vol weighted) beat the binary T3 signal?

Universe tested: 22 configs — original single-asset and multi-asset vol-targeting, plus post-close expansion across sigma-target sweep, carry forecast, IDM/pool grid, and HRP/ERC weighting.

Tier 5 winner: erc_multi4_sigma030, Sortino 1.1399 (Sharpe 0.799 secondary).

Verdict: KILL T5-expansion FIRES — best expanded T5 Sortino 1.1399 is below the incumbent threshold 1.272. Continuous vol-targeting, carry forecast, and HRP/ERC weighting under-allocate during clear LETF uptrends and end below the binary signal's compounding pace.

https://preview.redd.it/ushjtwjhf00h1.png?width=871&format=png&auto=webp&s=9115e6c83737663959d4aea79289110784076810

https://preview.redd.it/tyo7w80if00h1.png?width=871&format=png&auto=webp&s=c8af753cd43eb64afe992942511a2a430d6c0157

T5 vol-targeting family: the full expanded set is shown in both performance and strategy/SPY form. The best T5 config improves the original T5 result but still fails the Sortino threshold and ends far below the T3 winner.

Why vol-targeting fails here: the Carver framework was designed for futures portfolios with 10+ uncorrelated instruments where continuous sizing dominates binary on/off rules. With a small LETF pool (4 instruments, all equity-correlated), the volatility signal under-allocates exactly when you most want exposure (clear bull trends with rising vol) and over-allocates exactly when you don't (post-crash low-vol bounces). The expanded tests confirm this across sigma targets, carry, IDM grids, and HRP/ERC weighting. Binary Vote-K=2 wins decisively on this universe.

Final winner — performance summary

The study winner across all 5 tiers: qld_voteK2_sma250_100_vol21_40_ar30_off_zroz (T3d Vote-K=2 with longer SMA windows).

Net-of-tax performance under annual netting (the realistic regime):

Track	Sortino	Sharpe	Edge vs SPY (Sortino)
Gross	1.325	0.919	+0.367
Net M2 (annual netting 15%)	1.183	0.827	+0.226
Net M1 (per-swing 15%)	1.084	0.766	+0.127

40-year compounding: $10K → ~$60M (M2 net) vs SPY ~$793K. Net-of-tax CAGR ≈ 24% (M2) vs SPY ≈ 11%.

https://preview.redd.it/rl04dwsjf00h1.png?width=1440&format=png&auto=webp&s=5c62bcff6f01c3bad5208417fc2446c834ff6d12

All top configs scatter-plotted by their Sharpe (x) and Sortino (y) on lh_56y gross. The y=x diagonal is dashed; points above the line are penalized more by Sharpe than by Sortino — exactly the LETF asymmetric-upside signature. The winner sits in the upper-right cluster, well above both reference lines.

Cohort behavior — what happens if you enter at the worst possible time

8 historical entry dates were tested (5 worst-case peaks + 3 control troughs):

Entry date	Event	Strategy 5y CAGR	SPY 5y CAGR
1987-08-25	Black Monday peak	+24.7%	+7.6%
2000-03-24	NDX dotcom peak	-1.6%	-3.7%
2007-10-09	GFC peak	+16.7%	+0.6%
2020-02-19	COVID peak	+21.6%	+14.5%
2021-12-27	Pre-rates peak	+8.2%	+11.3%
2003-03-11	Dotcom trough	+9.3%	+12.6%
2009-03-09	GFC trough	+33.5%	+25.3%
2022-10-12	Rates trough	+41.9%	+23.4%

The 2000 dotcom peak is the only cohort with a negative 5y CAGR — and it's still less negative than SPY-from-same-date. Every other entry produces a positive 5-year outcome. Strategy beats SPY in 6 of 8 cases.

By regime: when entering during high-confidence ON state (all 4 signals positive), strategy beats SPY 95% of the time over forward 5y windows. Even when entering during OFF state (defensive ZROZ position), beats SPY 96-98% of the time.

Rolling windows — consistency across entry dates

The 8-cohort table above tests handpicked dates. The structural question is broader: across every possible entry month, how does the strategy hold up?

Method: for the top-20 strategies + SPY benchmark, recompute the equity curve, then for each rolling window size (3y, 5y, 10y, 15y, 20y), slide the window forward one month at a time and recompute risk-adjusted return diagnostics / CAGR / MDD / pct_time_above_SPY at each start date.

Total: 37,359 window-level backtests across 21 configs × 5 window sizes.

https://preview.redd.it/8otq433nf00h1.png?width=631&format=png&auto=webp&s=b7bf5e460d1b0ad130b4f1a14b6e0ec37f38a734

Median rolling Sharpe per (config × window size), kept as a legacy robustness diagnostic. Rows sorted by mean across all sizes. Green = robust (consistent edge across entry dates); red = era-dependent (works in some windows, fails in others). T3d K=2 family dominates the green band; SPY 1× sits in the middle row with median 0.678; the red rows at the bottom are configs that worked in single-shot lh_56y but fall apart on rolling.

Headline numbers for the study winner (qld_voteK2_sma250_100_vol21_40_ar30_off_zroz):

Metric	Value
Avg median rolling Sharpe (all window sizes)	0.829 (highest of 21)
Avg minimum rolling Sharpe	0.167
Avg pct of windows beating SPY	89.6% (highest of 21)
Composite robustness rank	#5 of 21

For comparison, SPY 1× buy-and-hold benchmark: avg median Sharpe 0.678, avg minimum -0.048, composite rank #21 of 21 (every other strategy in the top-20 dominates SPY on rolling robustness).

https://preview.redd.it/7mb2gk0qf00h1.png?width=878&format=png&auto=webp&s=fa52377b1a96e3a7b4c0faded8f2a0c73c9d5f5b

Top 21 by composite robustness rank (legacy Sharpe diagnostics + pct above SPY, computed across all 5 window sizes). Captures both "good when good" and "not-bad when bad". The top-5 cluster is all T3d K=2 family.

https://preview.redd.it/5np2g5jqf00h1.png?width=862&format=png&auto=webp&s=36220c7c612d2bf44af28c7bd7f739faccff2d71

Worst rolling-window Sharpe achievable per config. Red = strategy lost money in some 3-20y window; orange = sub-0.3 worst case; green = ≥0.3 worst case (strategy maintained at least modest edge in worst regime). The T3d K=2 winner sits in the green band.

Translation: if you'd entered the strategy at any month-end between 1986-2026 and held for 5 years, you'd have beaten SPY ~90% of the time. Not "in the backtest period" — in every 5-year sub-window of the backtest period. The strategy isn't path-dependent on a lucky entry date; it works across the calendar.

What about the windows that didn't beat SPY?

The 10% headline obscures an important detail: win rate scales sharply with horizon.

Window	Beat SPY (end-equity ratio > 1)	"Lose" windows
3y	88.6%	51 windows
5y	96.5%	15 windows
10y	100.0%	0 windows
15y	100.0%	0 windows
20y	100.0%	0 windows

Every 10y+ rolling window beats SPY. Zero exceptions across 909 windows. The losses are entirely in 3y and 5y horizons — and even there, the magnitude is modest, not catastrophic.

Distribution of the 66 losing windows by their end-equity ratio vs SPY:

Quantile	end_ratio (strategy_eq / SPY_eq at window end)
Worst (min)	0.738 (= -26.2% vs SPY)
25th	0.829 (= -17.1%)
Median	0.900 (= -10.0%)
75th	0.959 (= -4.1%)
Best (max)	0.999 (= -0.1%)

Median losing window: strategy ended at 90% of SPY equity — i.e., behind by only 10pp over 3-5 years, then catches up in subsequent windows. The worst single window was -26% relative (a 3-year cohort starting 2003 dotcom-trough recovery, where strategy was defensively in ZROZ during a 50% SPX rally).

Pattern of losing windows by entry year:

Era	# losing windows	What happened
1987-1988	5	Post-Black-Monday bull recovery; strategy in ZROZ caught the early rebound late
2003-2005	24	Post-dotcom bull recovery; strategy defensively in ZROZ during 50%+ SPX rebound
2008	1	Single 3y window, modest miss
2017-2019	12	Late-cycle low-vol bull; strategy occasionally went OFF on weak signals while SPY ran
2020-2021	22	Post-COVID rally + pre-2022 peak; strategy in ZROZ while SPY rallied 60%+

Notable: zero losing windows starting in 2000-2002 (dotcom bear) or 2007-2008 (GFC). Those are the windows where the strategy's defensive ZROZ allocation actually saved capital while SPY collapsed — strategy dominated SPY in those cohorts, exactly as designed.

Failure mode summary: the strategy underperforms SPY in bull-market recoveries from drawdowns, where holding ZROZ misses the early bounce — by 10-20pp over 3-5 years. It does NOT underperform in deep bear markets, where the regime filter does its job. This is the trade-off the strategy makes: smaller upside during sharp recoveries, much smaller downside during crashes. Net of 40 years: 256× SPY end ratio.

Tax models — how tax law affects the top-10

A 30%+ CAGR backtest is meaningless if the tax regime takes most of it back. The study modeled two interpretations of tax law on offshore investments:

Model 1 — per-swing 15% (worst-case) Every profitable exit pays 15% tax immediately. Losses do NOT offset gains across trades. This is the most punitive interpretation, equivalent to treating each round-trip as an independent taxable event.

Model 2 — annual netting 15% (realistic regime) Annual gains − losses consolidated at year-end; loss carry-forward indefinite. Intra-year losses offset intra-year gains; unused losses roll forward without expiration.

The two models are dramatically different in their effect on rotation strategies:

Tax model	Survivors (Sortino edge vs SPY > 0, lh_56y)	Avg CAGR drag
Gross (no tax)	10 of 10	—
M2 (annual netting)	10 of 10	~3.7pp/yr
M1 (per-swing)	5 of 10	~7.2pp/yr

M1 is roughly 2× the drag of M2 — and that's the difference between all top-10 strategies surviving and half of them dying.

Top-10 by tax track (Sortino edge vs SPY, lh_56y)

Strategy	Gross	M2 (annual)	M1 (per-swing)
sma250_100_..._off_zroz (winner)	+0.367	+0.226	+0.127 ✅
voteK2_..._sma200_50_vol42_40_off_zroz	+0.253	+0.127	+0.011 ✅
voteK2_..._sma200_50_vol21_40_off_zroz	+0.264	+0.136	+0.009 ✅
voteK2_off_zroz_alt	+0.264	+0.136	+0.009 ✅
vote_k2_off_zroz (canonical)	+0.264	+0.136	+0.009 ✅
voteK2_..._sma200_50_vol21_30_off_zroz	+0.252	+0.127	-0.030 ❌
tqqq_voteK2_off_zroz	+0.201	+0.103	-0.045 ❌
voteK2_off_edv	+0.165	+0.047	-0.067 ❌
voteK2_off_tlt	+0.165	+0.047	-0.067 ❌
voteK2_off_ief	+0.145	+0.028	-0.077 ❌

✅ = beats SPY under M1; ❌ = falls below SPY under M1.

Two patterns jump out

1. ZROZ as off-state survives M1; alternatives don't. Every non-ZROZ off-state variant (EDV, TLT, IEF) dies under per-swing tax. Same for the 3× LETF (TQQQ instead of QLD). The reason: non-ZROZ defensive assets generate more frequent rebalancing trades (worse trade timing, more whipsaws), and 3× LETFs have more leverage decay events that trigger taxable rebalances. ZROZ's lower-frequency trade pattern combined with its convexity in flight-to-quality regimes minimizes M1 friction. This is a second independent argument for ZROZ-as-universal-off (the first was raw performance in T1).

2. The winner sma250/100 has the largest M1 edge by 4× margin. Under per-swing tax, most surviving strategies are at boundary (+0.009 Sortino edge). Only the new winner clears the deploy threshold of +0.10 with the comfortable margin of +0.127. Its longer SMA windows (250/100 vs 200/50) mean fewer signal flips per year → fewer taxable swings → less M1 drag.

Practical implication: under conservative interpretation (per-swing, no offset), only 5 of the top-10 strategies are deployable, and the winner is one of two that clear with comfortable margin. Under annual netting, all 10 survive and the deploy bar relaxes considerably. Tax law interpretation has more impact on which strategies survive than the choice of risk-adjusted metric.

M1 is implemented as FIFO per-asset cost-basis accounting; M2 is annual realize with explicit carry-forward state. The full tax comparison runs 10 strategies × 4 datasets × 3 tax models = 120 result rows.

Why deploying this is defensible

The strategy clears every pre-registered statistical gate with margin, not at the boundary, and the residual risks are quantified rather than hand-waved.

Statistical gates passed (all defined upfront):

• Walk-forward: 7 of 8 sub-windows positive (threshold ≥ 6/8)
• DSR (deflated Sharpe ratio) p-value: 0.04 (threshold < 0.05)
• PBO (probability of backtest overfitting): 0.18 (threshold < 0.5)
• Bootstrap 99.9% CI lower bound on the primary edge > 0
• Cross-library CAGR delta < 3pp/yr (custom dispatcher vs vectorbt — engine validated)
• Single-block out-of-sample: positive
• Forward-stress: positive
• Anti-overfit margin: challenger Sortino must clear incumbent + 0.05; winner clears the operative Sortino threshold

Out-of-sample resilience that doesn't depend on lucky entry dates:

• 99.86% of 40-year days strategy ≥ SPY equity curve
• 100% of 10y, 15y, 20y rolling windows beat SPY — zero exceptions across 909 windows
• 5y rolling: 96.5% beat SPY; worst 5y window ended at 90% of SPY (median losing window: -10pp over the period, then recovers in subsequent windows)
• Composite robustness rank #5 of 21 across rolling-window stress; SPY ranks #21 of 21
• 8/8 cohort entry dates including dotcom-peak: only one negative 5y CAGR (-1.6%, still less negative than SPY's -3.7% from the same date)

Tax-net edge clears deploy threshold under both regimes:

• Annual netting (realistic): +0.226 Sortino edge, ~24% net CAGR vs SPY ~11%
• Per-swing (worst-case): +0.127 Sortino edge — still above the +0.10 deploy bar by margin
• Winner has 4× the M1 margin of the next surviving config; not a knife-edge result

Risks that are accepted explicitly, not ignored:

• 75% MDD floor is asset-class arithmetic for 2× LETFs through dotcom + GFC, not a quality signal. The relevant comparison (strategy_eq / SPY_eq) shows strategy was 3.1× SPY at the worst MDD bottom — i.e., the alternative ("stay in SPY to avoid the drawdown") was strictly worse at that exact moment.
• 2022 rates dual-drawdown is a structural risk for any LETF + long-duration-Treasury rotation thesis. ZROZ went -30%, but the strategy still ended the rolling window above SPY net.
• Strategy underperforms SPY by 10-26pp in some 3-5y bull-recovery cohorts (defensively in ZROZ during sharp rebounds). This is the explicit trade-off for crash-cohort outperformance (+10-25 CAGR pp/yr in 2000/2008/2020 entries).
• Synthetic pre-2006 LETF reconstruction calibrated against real QLD 2010-2026 (≤ 1% tracking error) — accepted as a known modeling assumption.

Net of 40 years and realistic tax: $10K → ~$60M (M2 net) vs SPY ~$793K — roughly 75× SPY end equity.

For skeptics — methodology

Pre-registration. Every metric, gate threshold, and anti-overfit margin was committed before the first backtest ran. Challenger configs must clear the incumbent's Sortino by +0.05, not just match it. Sharpe is retained as secondary context and for DSR.

The 7 statistical gates (all defined upfront, all required for advancement):

1. PBO (probability of backtest overfitting) < 0.5 — combinatorially-symmetric cross-validation against the rest of the config universe
2. DSR (deflated Sharpe ratio) p-value < 0.05 — accounts for multiple-testing inflation
3. Walk-forward: ≥ 6 of 8 expanding-window sub-tests positive
4. Single-block out-of-sample: positive primary metric on holdout
5. Forward-stress: positive on synthetic perturbation paths
6. Bootstrap 99.9% CI lower bound on primary edge > 0
7. Cross-library CAGR delta ≤ 3pp/yr — independent re-implementation in vectorbt vs the custom dispatcher; if the two engines disagree by more than 3pp, the result is rejected as engine artifact rather than alpha

4 independent datasets cross-validate every result: lh_56y (1969-2026 with synthetic backfill), modern_1990 (real data only, no synthetic), spy_real, ndx_real.

Cohort + rolling-window robustness:

• 8 hand-picked entry dates (5 worst-case peaks + 3 control troughs)
• 37,359 rolling-window backtests across top-21 configs × 5 horizons (3y/5y/10y/15y/20y) × every month-end as start date
• Per (config × window size): legacy rolling Sharpe diagnostics, percent of windows beating SPY, composite robustness rank

Sortino-vs-Sharpe re-scoring. All top configs evaluated under both. LETF returns have asymmetric upside: big positive bursts are the point of using leverage in risk-on regimes. Sharpe penalizes positive and negative volatility equally, so it can understate the quality of strategies whose volatility is mostly upside. Sortino penalizes downside semideviation, so Sortino edge is treated as the primary deploy signal.

Tax modeling. 10 strategies × 4 datasets × 3 tax models (gross / annual netting / per-swing) = 120 result rows. M1 (per-swing) is FIFO per-asset cost-basis accounting. M2 (annual netting) is annual realize with explicit carry-forward state, no expiry on losses.

Synthetic LETF reconstruction. Pre-2006 LETF series are rebuilt from underlying index + financing rate + expense ratio, calibrated against real QLD 2010-2026. Tracking error ≤ 1% over the calibration window. Pre-2006 results are not claimed as identical to real-LETF behavior; they are stress paths consistent with the asset class.

Threshold sweeps. SMA buffer (0% / 2.5% / 5% / 7.5%) and hysteresis variants tested to confirm the winner is not a knife-edge optimum on any single tunable parameter.

Reproducibility. Full code, data manifests, gate scripts, and result tables are open-source.

Disclaimer

Research artifact. Not investment advice. LETFs (2×/3×) carry structural leverage decay risk during high-vol regimes. The 40-year backtest includes 2000 dotcom (NDX -83%), 2008 GFC (SPX -56%), and 2022 rates dual-drawdown — all real history, no fitted scenarios. Past performance ≠ future results. The 2022 rates regime is unresolved structural risk for any LETF + Treasury rotation thesis. My own capital is 100% passive buy-and-hold across this entire research process.

TQQQ and SOXL are obvious ones but do you have a list of tickers you track for when they have deep drawdowns to then DCA into. Ideas that are long term strong and not just speculative single name tickers.

Edit: to be clear, I’m asking for what’s in your watchlist and why you think it’s a long term strong performer with frequent enough market panics crashing it.

We all have that one trade. The one that taught us more than any book or video ever could.

For me, it was going all-in on TQQQ right before a Fed announcement without a stop-loss. I thought "it's just 3x, how bad can it be?" Turns out, very bad. Lost 25% in a day.

What's YOUR biggest leveraged ETF mistake?  Share it below so beginners know what traps to avoid.

I'll start:

- ❌ No stop-loss

- ❌ Position way too big (50% of account on one trade)

- ❌ Didn't check VIX before entering

- ❌ Held through earnings "because it would come back"

Beginners: Read these carefully. Every mistake below is a free lesson.

This community is for educational purposes only. Trading leveraged ETFs carries significant risk of loss. Never trade with money you can't afford to lose

Questions? Drop them below! 👇

Wow this one moves!

Still only about 2 months in LETFs in particular, and there is a question that keeps coming popping back up in my mind regarding a portfolio's leveraged exposure and its relationship to volatility decay.

My understanding is that the consensus is 1.5x - 2x leverage is optimal for the long run, as anything greater will succumb to excessive volatility decay over time. I will see a lot of people run (using cute numbers for clarity), 50% of their portfolio in a 3x fund, and claim it is an effective 1.5x leveraged rate.

The formula for returns is something like the below:

LETF CAGR ≈ (L * r) - [((L^2 - L) / 2) * v^2] - expense ratio

Where:

L = leverage factor (e.g. 2 for 2x, 3 for 3x)

r = expected annual return of underlying asset (decimal form, so 10% = 0.10)

v = annual volatility of underlying asset (decimal form, so 20% = 0.20)

source: https://www.reddit.com/r/LETFs/comments/1dqzier/beautiful_and_model_free_1_line_formula_for/

If leverage is being raised to an exponent, doesn't that make the formula exponential and therefore you cannot compare 2x or 3x? Essentially, you cannot 'cut' the 3x -> 1.5x with a 50% position (formula would be 0.50 * 3 = 1.5x)? I guess taking it to the extreme, if you have a 50x leveraged position that represents 3% of your portfolio (0.03 * 50 = 1.5x), that would obviously not be equivalent to a 1.5x leveraged position because any drop of 2% would wipe out the leveraged portion.

Obviously that is an extreme - is the difference in 2x and 3x so nominal that they can be compared like this? It just seems like it is imprecise to call it 1.5x, since a 3x portion carries a disproportionate risk of volatility decay to a 1.5x.

I found a decay factor formula (honestly not sure if it even applies here), which is:

Volatility Decay Factor Formula: Decay Factor = (L^2 - L) / 2, where L is the leverage multiple

And so therefore,

1.5x LETF

(1.5^2 - 1.5) / 2 = 0.375

2x LETF:

(2^2 - 2) / 2 = 1

3x LETF:

(3^2 - 3) / 2 = 3

So that the volatility decay risk of a 3x is 200% more than a 2x, and 700% more than a 1.5x, and therefore you can't really say a 50%, 3x position is effectively 1.5x leverage, because the leveraged sleeve of the portfolio is carrying a disproportionate amount of volatility decay risk (+700%)?

not a math major so REALLY out of my depth here, kindly looking for insight and wisdom on this topic

Tried a quick 200SMA vs B&H comparison for TQQQ and I don't think I trust the results I'm getting - wondering if someone can check my work. Here is my spreadsheet, and here is the summary of results:

• With both strategies, in both scenarios, I assume you start on Jan 2, 2011, and never add another dollar to the account
• B&H assumes you buy $1000 worth of TQQQ on Day 1, and never take another action
• 200SMA assumes very simply that if the daily price is below the QQQ 200SMA (TQQQ SMA is never used), to sell the entire pot and hold in cash. If the daily price is above the (QQQ) 200SMA, buy and hold in TQQQ. This has 2 versions:
  • Scenario 1: Make a same-day decision based on the opening price. E.g. on May 11th, if the price opens below the 200SMA, convert all TQQQ to cash. Or if holding cash and price opens above 200SMA, convert all cash to TQQQ. If no change in above/below 200SMA, take no action.
    • Note that in Scenario 1, 200SMA is calculated off opening prices for the last 200 days
  • Scenario 2: Make a decision based on previous day's closing price. E.g. if May 10 closed below 200SMA, convert all TQQQ to cash, without even thinking about May 11 opening price. If May 10 closed above 200SMA, convert all cash to TQQQ, without even thinking about opening price on May 11. Of course, if no change in above/below 200SMA, take no action.
    • Now, 200SMA is calculated off closing prices for the last 200 days
    • Note that in scenario 2, even though I'm using previous day closing price as my decision criteria, I'm still calculating value based on opening prices (since that's the price I buy or sell at
• Note assumption: GOOGLEFINANCE() returned #N/A on some days. To simplify, I assume that if this happened, I would just copy the last valid price. In other words, some missing data, but unless anything dramatic happened in those days, shouldn't impact.

Now here are the results that shocked me as being so different:

• Scenario 1 (same day decision based on opening price):
  • B&H wins, with a final value of $182,000
  • 200SMA loses, with a final value of $78,000
• Scenario 2 (decision based on previous day's closing price):
  • B&H now loses, with the same final value of $182,000
  • 200SMA now wins, with a final value of $665,000 (!!!)

https://preview.redd.it/aia4rfuzwi0h1.png?width=1170&format=png&auto=webp&s=ecd2f1c98948577fbb33330e7d3cf6a22ffd6194

Hoping someone can check my work and see if I've made any mistakes. I'm shocked that there would be an 8.5x difference in results just by choosing to use the previous day's close price as a decision point, rather than the current day's opening price. Assuming there are no mistakes in my work:

1. Is just random? But it seems to grow systematically better with this decision, through ups and downs. How would I backtest this even further (i.e. how can I replicate pseudo-TQQQ data before 2010?)
2. Is there an explanation for this? Is this really a valid result that is known?

Sharing here because other than my wife - I don’t want to share this with anyone else I know in real life.

Early 40’s and just started taking investing and learning about investing seriously within the last 3 years. I spent a few years chasing crypto gains only to loose about $4K on shit coins during COVID era. I got into ETH in 2021 near the top and just sold at a very minor profit last year. I got tired of loosing and started getting serious about investing. I stopped looking at it as a get rich quick and started accepting my fate that I would have to build my wealth slowly.

Everything changed when I started to educate myself and start learning about different investments, how to compare ETF overlap, and dive into the actual underlying holdings. I also learned to break from the consumerism cycle that holds many.

I would build test portfolios and start to back test my theories. I stopped investing in stuff because some random people said it was a good idea and started investing because I understood what I was investing in and how it worked.

I spend the last 3 years maxing my contributions to my ROTH IRA and this am maxing mine already (last transfer was today).

So yeah…. That’s my story. It’s a very small win compared to many other people in here - but it’s my win. And by god - I’ll celebrate it! 🎂🍾

Here’s to the next 20 years investing AND learning. I couldn’t have done this without the learning part. Leveraged ETF’s help get me here! 🥂

I've been following this LETF since it bottomed out in 2022 but never actually bought any because I'm risk averse. However, liberation day seems like the most obvious buy in point for this one, I'm scratching my head at why I didn't just throw $10k in at least, seeing that would be an over $150k gain on so little money. If I put $30-40k in I wouldn't have to work anymore.

It seems like it was kind of a no brainer that a semiconductor boom would follow with the AI boom. I know the saying is hindsight is 20/20 but I remember seeing it hit these low levels back in 2022 and when it hit them again a little over a year ago I knew that was probably a good entry point, but for some reason couldn't get myself to do it.

But I keep telling myself I would've sold probably when it hit $40-50 anyways. Did I miss out? No way am I buying in now but I'm unsure if it will ever get that low again and it hurts to think about how I passed up what could have been very early retirement. I can't get the idea out of my head really.

Is now the time to start buying SOXS?

I've been obsessed with backtesting lately and I think I found a setup that actually solves the drawdown problem without killing the gains. I'm still a beginner at this, but every time I run the numbers, this portfolio crushes it.

Here is the breakdown:

25% TQQQ 60% KMLM 15% TMF

The logic is pretty straightforward. TQQQ is there for the raw power, and KMLM is the heavy hitter for crisis alpha. With 60% in managed futures, the idea is to have a huge buffer against volatility.

The backtests show a massive CAGR compared to just holding QQQ, but the max drawdown is actually lower. It seems like a much better way to get leverage without the typical 70-80% crashes.

https://preview.redd.it/j5j8twyq810h1.png?width=2444&format=png&auto=webp&s=1ffd066980ac09b814e9574b4bd16b2320e51ece

We see a lot of backtests over the wonderful last 17 years and it often ends in tens of millions. Has anyone actually made that much or are most of us in the 5 figures and hoping the next couple years go well stage?.

20% TQQQ

20% UPRO

20% BULZ

20% TECL

10% QLD

10% SSO

total port 800k goal is to try do 7x the 800k

Hey everyone, I’m a new investor trying to build a portfolio for maximum long-term returns.

Right now, I’m looking at a 50/50 split of TQQQ and RSBT. My reasoning is that QQQ’s historical "sweet spot" for leverage is usually between 1.5x and 2x, and this combo puts me right at 1.5x QQQ exposure while adding 50% Managed Futures (Trend) and 50% Bonds on top.

However, I have a few concerns:

1. Leverage Decay & Returns: If QQQ’s future returns drop, the optimal leverage ratio might fall well below 1.5x, making this setup less efficient.
2. RSBT Issues: It’s a relatively new fund with thin daily volume. When I tried backtesting its components, the performance didn't really seem to match the "50% Bonds / 50% Trend" promise.

What’s your take on this combo? And if you’re purely chasing the highest possible CAGR—completely ignoring volatility and drawdowns—what would your ultimate portfolio look like?

Man what is going on! I mean I know this what leveraged does but man. I’ve been looking at trends and wasn’t expecting this! I bought 15 originally at $40 and sold when it hit 80$ but wish I held on it’s now in the 175+ range! (Punching the air right now!) 😂😂😂😂

Im too lazy to rebalance or DCA. it would be a small part of my portfolio (60k crypto, 150k VTI/VXUS, 20k individual stocks). I’m intrigued by the potential upside and do have diamond hands for a downturn but am looking to retire in 5-10 years and want assets I can just buy and hold. I am doing this with a small amount of SSO and am comfortable with it so far. thanks!

Hi everyone, I have a pretty straightforward question but couldn’t get reliable results when I tried to run it myself. I’m looking for the annualized return (CAGR) of a pure DCA strategy into TQQQ (3x Nasdaq-100) — meaning regular fixed dollar investments (e.g. monthly) with no timing, no moving averages, no rebalancing, no additional strategies — just buy and hold DCA. I’m especially interested in the longest possible backtests, even if they use synthetic/pro forma TQQQ data going back to the 1970s or 1980s (before the actual ETF existed). If you have done or found such simulations, I’d love to see: What CAGR you got Over what exact time period Monthly / weekly / annual DCA frequency (if it makes a big difference) Bonus if you can compare it to QQQ or SPY under the same DCA rules. Thanks in advance! Appreciate any serious backtests or links.

want to say I appreciate everyone in here, although LETFs are incredibly risky and we are all a little crazy, everyone here seems very well-read and educated, and are diligent in backtests and always earnest in discussing different strategies.

have been getting recommended different investment subs and it is straight brainrot. 20 year olds DCAing into yieldmax ETFs in taxable brokerages. everyone asking what the next ___ stock is. VOO and chill'ers absolutely not chill with their 8% YTD and missing all the rewards from the AI supercycle. paper handers selling on the first red day and whining on reddit.

just wanted to say i really appreciate everyone in r/LETFs. y'all have had me reading more academic papers than I read in college, lol

I am personally holding 5000 qty , bought recently earlier this month at $64. Planning to DCA if it ever dips 25% from ATH.

Hello again LETFs community!

I'm considering adding a small allocation for:
- iShares Bloomberg Enhanced Roll Yield Commodity Swaps ETF.

(well, technically, I already own it in my speculation pie, and it has done well, but considering it as a part of my main portfolio).

What is it?
Enhanced roll commodity ETF to invest in energy, precious metals, agriculture, industrial metals, softs, and livestock through future contracts. Rather than auto rolling into the next contract, the 'enhanced roll' rules-based process tries to avoid contango (negative roll yield). So it should do better than a more traditional broad commodities ETF.

Arguments for including:
Diversification: In stead of just gold, fundamentally, adding a broader commodities fund adds diversification. Gold, after all, isn't like other commodities, and is a buy & hold monetary & inflation hedge, rather than a production input like oil or industrial metals. We've seen some recent macro events (global conflicts, oil supply shocks) where everything went down, while commodity ETFs went up.

Correlation/beta of BCOM index to:
- Equities: ~0.4
- Gold: ~0.3
- Long-bonds: ~0.0
- CTA MF: ~0.3

Overall, quite low correlations to the above asset classes. Should be good for harvesting rebalancing premia?

Regime hedging: Energy does well during inflationary regimes, especially stagflation and supply-shock. During some recent events (global conflicts, oil supply shocks), there were moments the above 4 asset classes went down, while this commodities ETF went up. So if you're aiming for an 'All-Weather' portfolio, adding 5-10% in broad commodities seems to be solid.

Arguments against:
- Recency bias, as it's in the news. Prices are high now.
- Historically, long-term CAGR is pretty rubbish on broad commodities ETFs. Granted, these are usually standard roll funds. Enhanced roll ETFs are relatively new.
- Swaps counter-party risk.
- Back-testing: tricky. You can put in some broad commodities ETFs, but they basically never increase Sharpe and/or CAGR, unless you really try to, probably due to BCOM-type funds' long-term CAGR being pretty shit.
- 0.4 beta to equities isn't the best. Gold, long-bonds, and managed futures are closer to zero.

My current strategy (UK-based):
- 55% 2x NDX
- 15% Long-bonds (20yr duration Euro Gov)
- 15% DBMF
- 15% Gold
+ rebalance bands
+ 200SMA (using SPY) on 2x NDX.

Considering splitting the 15% gold into 10% gold 5% Enhanced Roll Yield Commodity Swaps. Or 7.5%/7.5%.

Anybody have insight on this?

Thanks guys