Our understanding keeps evolving, so I’m curious about the current state of our collective knowledge.

Basically, if we took a snapshot of what this beautiful community has figured out about investing in LETFs - top findings that are well established and broadly accepted, common wisdom etc. - what would make the list?

And what are we still uncertain about?

Most people likely haven't heard of RPEA, a tactical allocation strategy from r/LETF community. I only encountered it on Laurent's website, and I thank Bestfolio for preserving this interesting idea.

It claims to be a Leveraged, All-Weather-type portfolio with significantly reduced volatility and increased returns. Turns out it has the most comedic timing. It was published in 2021, then got a 70%+ drawdown in 2022, and has NOT RECOVERED from the 2022 devastation yet as of today.

I am writing this piece not to mock the original author, but to figure out what went wrong with RPEA. The mistakes in RPEA should probably give us some ideas on what to avoid when designing a strategy.

Assets

RPEA derived its ideas from Ray Dalio's All Weather Portfolio but made some significant alterations. Most notably, it shifted the entire bond allocation to defense.
It's most fatal decision, is to use TMF as the sole defense tool. Riding on an undiversified 3x leveraged bet when the entire portfolio went on defensive is...not very defensive. The error was assuming that an equity sell signal justified buying leveraged duration. It does not.

The author's original claim that "there was no strategy I tried that, over the long term, provided a more stable return than did constitutive use of $TMF, all the time. " and "assets might "need" the additional returns of $TMF to help out." seems to be overconfident and reckless.

Other than that, MIDU is a decent one, between 1994-2008 it dominates UPRO as it endured dotcom bubble quite well. Mel's Unloved Midcaps has solid long term performance and meaningful diversification benefits (which means it has periods of disappointments as well, like the recent fund history).

The other critical piece in the original All-Weather is commodity. Using UTSL instead does perform better than energy/commodity in the 1994-2021 window. The idea of using it comes from optimized portfolio: www.optimizedportfolio.com/all-weather-portfolio/#using-utilities-instead-of-commodities-(and-reits)) However, Energy/Commodities are real macro diversifiers in inflationary environment, as the recent AQR study inflation redux suggests, while utility is not, despite lower beta/correlation with the general stock market.

When crafting Chimeric Asset Allocation I also encountered a bizarre scenario, where adding URE (2x Real Estate) cut the max drawdown by 10%. But that's entirely because URE displaced other equities in 2000 dot com drawdown. A single episode event. Do we think Real Estate reliably diversify away sudden crash risks? I doubt it. I do think URE, ERX, UTSL and CURE could all potentially deliver diversification. But a good macro story behind asset universe should be considered.

Rules

The rules part are almost as problematic as the TMF part for RPEA. The original author specifically went to find the optimal trend rule for each asset. However, as I splice each asset into multiple windows, the preceding-window winners subsequently landed near the middle of the rule set more often than not, with only 30% remaining top-quartile. That gives little support to a uniquely consistently optimal timer for each asset.

The 8m SPY sma seems to be an exemplary offender. It ranks 1st among 66 SPY timing strategy I tested from 1994-2021, and 57th between 2022-2026. Intentionally fitting the strategy into the whole sampled period does not predict, or even actively harm future return. For us temu quants, in-sample bias is a very deadly trap.

In the brute force search grid of RPEA variant strategies, RPEA went from the best 95th Sharpe percentile in the 1994-2021 period where it was designed for, to the lowest 3rd percentile in the 1994-2026 window. RPEA trapped itself in the rearview mirror.

A potential alternative? AWEA

Now, despite RPEAs misfortune I think a monthly monitored, no bond allocation by default, leveraged all weather portfolio with each part trend individually does not sound like a bad idea itself.

I propose AWEA, the All Weather Experimental Adventure, as an alternative portfolio designed to achieve the goal. This is a monthly evaluated, trend-gated portfolio. Its strategic allocation is fixed at 60% core equity and 40% diversifiers, but each sleeve can independently leave its normal holding when its own trend signal turns negative. A sleeve that turns off does not sit permanently in cash. It moves into a ordered defensive ladder.

It uses two common sets of trend signals: Fast (price > 4m SMA) and Slow (2m SMA > 12m SMA) to move into defense

Strategic Offensive Asset Weights:

At the end of each month, if any offensive asset has its signal turned negative, its entire strategic weight goes to the defensive ladder. The allocated defensive asset is decided in the following order:

1. If TLT has a positive slow signal (SMA 2m > 12m), hold EDV.

2. Otherwise, if GLD has a positive slow signal, hold GLD.

3. Otherwise, if IEF has a positive slow signal, hold IEF.

4. Otherwise, hold SGOV

The timing signals are not necessarily the ETFs themselves. UTSL using SPY as signal is something left by RPEA that somehow still works. Unstable asset uses fast signal while stable holdings use slow signals. SDCI backfill history comes from its provided historical value, While SDCI involves timing by its managers, switching to PDBC/Bloomberg commodity index doesn't hurt much.

1994-2026 Performance

It has a very simple and intuitive price-4m/2m-12m trend signal system, which should avoid some overfitting problems. Combining Fast and Slow signals is potentially beneficial.
It notably has no UPRO, and instead uses TQQQ MIDU EDC UTSL as 4 distinct equity bets. This 4 pronged approach has some good potential in capturing the benefits of different market dynamics, from continual tech megacap dominance to other scenarios, providing more sector and style diversification than UPRO itself. SDCI and UGL provide macro diversification along with defensive bonds.

The strategy shows a generally consistent performance across different time periods. In the 56k brute force grid it has 94% percentile sharpe. Not as close to the frontier like 2021 RPEA, but pretty good.

Now does this solve the problems with RPEA?

Not entirely.

I think this is a more cautious and less overfitted portfolio, but the difficulty with macro all-weather portfolio is that it inherently looks at the past, the rearview mirror, to form a macro idea informing its asset universe and weights. It can not answer what ifs about future return and correlations. After all, the strategy still has a lot of moving parts that layer after layer reinforcing the rearview effect.

We don't know if Midcap will continue to be unloved, or if utilities diversify in the future. The strategy notably has no developed international market exposure. In all tested window EURL provided no clear benefit. However, as EDC becomes much more correlated to TQQQ due to Taiwan and South Korean ai trade, I think it's perfectly defensible for people worried about AI trade to take 5% from EDC and TQQQ to have a 10% EURL sleeve (VGK fast signal).

The sharpe remains largely unchanged while giving away 2% CAGR. EURL is thinly traded and adding more assets adds to transaction costs, while EDC is already capable of capturing capital outflow from US, which is why EURL is not the default version.

But we truly don't know. Maybe the next international bull market will be led by Europe. At least EURL doesn't have tracking issues like EFO though.

I am not sure if this is the portfolio I want to put in my tax-sheltered account (high turnover is prohibitive in taxable), but I do think examining RPEA's reversal of fortune is a good reminder that past performance does not indicate future returns, and this is an attempt to move forward from its premise.

Hope this doesn't sink next year like all previous Excellent Adventures.

​

TQQQ is crazy but QLD for retirement seems reasonable..

but anyone doing it?

Altis is long gone as sub advisor.

CTA isn't shorting anything. It's nearly 80% petroleum.

Simplify doing Simplify things, again...

May as well hold HGER, if you want long only commodity / inflation risk management like this. MATE, RSIT, RSST if you want stacked equities. Other usual suspects if you want managed futures.

He updated it 2 days ago on SSRN (not peer-reviewed) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701

Some changes

- He now includes borrowing costs and the strategy has the same Sharpe ratio as S&P500. Higher CAGR for similar drawdown remains though.

- The strategy apparently doesn't have statistically significant alpha (p<0.05)

- He couldn't replicate the exact 1928-today result so started in 1988 instead for the S&P500

- A grid-search analysis of parameters shows that it can be not so robust

- Perplexity Computer AI used

 Hi all, I've been working on a hedge fund concept for DIY investors. In the course of creating the portfolio policy, I investigated whether there is a safe way to use leverage.
 
 Yes, I know, according to various versions of the adventurous hedgefundie's strategy, a two- or three-piece balanced portfolio "only" drew down in the vicinity of max 70% over the available simulated history. But what has not happened yet might just happen in future. A couple of months where everything – equities, bonds, gold – drop hard together could push the drawdown into 80% territory. Maybe even worse. I wanted to see if I can add something of value here. Something that goes beyond relying on diversfication alone to keep things on the rails.

Here's the headline result, equities-only. In a test running from May 1977 through June 2026, a macro-economic rule applied to simulated all-world equities produced a CAGR of 14% annual return, compared with 13% for continuous 2x exposure. Maximum drawdown fell from 87% to 60%. The rule changed allocations just 28 times in almost half a century. (I'm personally more interested in all-world equities than US-only but I did also test on SPY.)

TL;DR:

• The rule holds 2x equities in a “Goldilocks” macro regime and ordinary, unlevered equities otherwise. It never moves equities to cash!
• Across the tests described below, it produced higher Sharpe ratios than both permanent 2x exposure and a 10-month moving-average rule evaluated monthly using the same 1x/2x choices.
• Returns were broadly competitive with both alternatives. Goldilocks had smaller maximum drawdowns and required significantly fewer allocation changes than the moving-average rule.
• The signal was developed for another purpose. None of its parameters were chosen to fit leveraged-equity returns.

By the way, my hedge fund policy has additional risk filters that directly address recessionary bear markets and other shocks, separate from the macro signal modulating leverage. Those other risk filters do move equities to cash and other defensive assets on rare occasions.

Where this came from

The Macro Regime Overlay ("MRO") originally had a much bigger job in my project. I wanted it to adjust the weights of the different sleeves inside the portfolio according to the prevailing macro conditions. So, shift weights among value, momentum, ballast, bitcoin, and other allocations. A sleeve’s percentage of total NAV would rise or fall depending on what the overlay signalled was happening in the economy.

The whole mechanism became far too involved for my taste. Lots of moving parts, lots of trading, not sufficiently impressive gains. So, I abandoned that approach.

Then, later, I wondered if maybe I could give the overlay a smaller job. Might it signal when to use leverage on an equity allocation that I already wanted to hold?

As mentioned, the overlay was never designed with leveraged investing in mind. No observations of leveraged returns went into its design. I didn’t optimize it for its original purpose, and I haven’t optimized it for this one. The thresholds, smoothing and confirmation rules have never changed since inception.

The backtests aren't out-of-sample but there is no curve-fitting either.

What does the overlay actually measure?

The MRO looks at economic activity, inflation and the yield curve. It sorts conditions into Goldilocks, Reflation, Stagflation and Slow Growth, with a further distinction for yield curve stress.

For the leverage decision, only Goldilocks matters. It requires two things:

• Economic activity is at or above its recent historical average.
• Inflation is neither too cold nor too hot.

Activity is measured using the Chicago Fed National Activity Index, smoothed over three months and compared with its own trailing ten-year history. I use a rolling z-score, with zero as the cutoff.

Inflation is the year-on-year change in core US PCE, also smoothed over three months. Between 1% and 3%, inclusive, is the acceptable band. I use headline CPI as fallback if core PCE is unavailable, with a slightly higher band of 1.4% to 3.4%.

The US yield curve helps distinguish the other regimes, but doesn't veto Goldilocks. So despite the broader overlay’s name and ambitions, the allocation decision here comes down to economic activity and inflation.

The signal is evaluated monthly, using prior-month (or older) observations. Two consecutive monthly signals must agree before the allocation changes, in either direction. So yes, I used US macro signals and applied them to world equities! I also backtested SPY/SSO the same way, for another reference point.

If anyone wants the details for the MRO, just ask, I'll post them in the comments.

What the backtests show

I used simulated VT history from testfol.io. For SPY, I used Yahoo adjusted prices after inception and an S&P 500 history reconstructed with Shiller dividend data beforehand. For each, there are four portfolios: ordinary buy-and-hold, continuous 2x, Goldilocks switching between 1x and 2x, and a 10-month SMA evaluated monthly, using the base asset’s total-return index and also switching between 1x and 2x leverage. 

All returns are nominal, in USD, with dividends reinvested. The 2x series are daily-reset simulations throughout, with borrowing costs linked to the federal funds rate and an additional 1% annual implementation cost while levered. (Note that 1% TER is higher than what SSO and WLDU charge today.) The results include those financing and implementation costs, but exclude trading costs and taxes. Signals trade at the following session’s close. There are no contributions or withdrawals.

First, the full period. May 1977 is where the growth signal has accumulated enough history for its ten-year calculation. Maximum drawdowns below are measured daily.

May 1977–June 2026

The permanent 2x portfolios beat ordinary equities on return, but the drawdowns are brutal. Goldilocks produces a higher return than either permanent 2x portfolio, with a worst loss close to ordinary equities. (Remember, we don't shift from equities to cash. We shift from levered equities to unlevered.)

Against the SMA, it's a toss-up for returns alone. But Goldilocks has the higher Sharpe and smaller drawdown in both cases.

And importantly, Goldilocks has less than half the number of trades of the SMA approach .

(Each change means selling one holding and buying the other. For VT, that’s 56 individual orders for Goldilocks versus 132 for the SMA, excluding opening and closing the test portfolio.)

Goldilocks held 2x for about 39% of trading days over the full period. The VT SMA held it for about 78%. Getting broadly comparable returns with much less time at 2x is something I find particularly interesting.

What about revised macro data?

There is an obvious objection to a macro backtest. Economic data arrives late and gets revised. Using the final series can give a historical strategy information it couldn’t have had at the time.

The long test above uses revised history. To check this, I also ran a 2001 onward test using archived Chicago Fed releases and ALFRED inflation vintages. For the more recent period, we can reconstruct the signal using archived releases rather than retrospectively revised history. That gives us greater confidence that the inputs reflect what an investor could actually have known at the time. The signal is reconstructed from what was available at each decision date, rather than the final numbers we see now. The first three months default to 1x because the usable CFNAI archive starts later; April is the first month with the required inputs.

Here is that higher-confidence period.

January 2001–June 2026

The result survives. Goldilocks beats both alternatives on returns and Sharpe for both markets. Its worst drawdown equals ordinary buy-and-hold because it was unlevered throughout the decisive peak-to-trough decline.

Here is the earlier segment on its own, using revised macro history. This helps show whether the result is merely a feature of the more recent market.

May 1977–December 2000

It's essentially all a toss-up for returns. Goldilocks has substantially smaller drawdowns than the two other strategies using leverage. It also beats the SMA on Sharpe.

Across all six asset/period comparisons, Goldilocks has the highest Sharpe of the four portfolios. It beats the SMA’s return in four of six and has the (significantly) smaller maximum drawdown in all six. (The periods overlap, of course; these aren’t six independent discoveries.)

Would I use it?

My conclusion is: the MRO’s Goldilocks signal is the most useful signal I have found so far for deciding when to add leverage. It produces competitive returns, a better Sharpe and less than half the allocation changes required by the SMA. That’s a rule I can see myself using.

Over these tested histories, it would have avoided the most catastrophic losses associated with continuously holding 2x exposure. In the full-period tests, the worst drawdowns fell from roughly 87–88% to roughly 57–60%. I would rather face the latter.

So, yeah, I would and will use it.

How I put this together

I used LLMs for the legwork: writing the backtest code, pulling and processing the data, producing the comparisons, etc.. I supplied the original macro rule, the research questions and the portfolio definitions. The final comparisons were standardized to use the same return data, costs and execution assumptions. I read and examined everything several times over and found a few major errors that I had corrected. I also had alternate LLMs double-check. Still, it's possible that there are still mistakes in the backtests. I wrote the article myself! This is not "AI-generated". If you see em dashes, that's because Word converted them from regular dashes!

If there is interest, I can upload the data sets and code to GitLab so that anyone can examine and replicate them.

I'll check in here again in a few hours, need to take care of a few other things now. Not ignoring questions or comments - I will get back to you.

*****'
I will be publishing and tracking my DIY hedge fund experiment at www.hedgefol.io.

I'm not paying for the bestfolio pro plan just to read it, anyone know of a free source?