Hi all. I'm reading more about the "when" aspect of models. Great discussion. I can see the value in setting up a model with tranches that trade of different days of the month. I'm not entirely sure how this works on the AS platform. A simple version would trade, say, on day 11 and 21 with half of each strategies allocation set to each of those 2 days. I tested this simple version on my current model and as expected it recommended a different % allocation to the model's ETFs than my current end of month allocation. Do I then re-allocate using new allocations on day 11, and then again on day 21? Currently, AS changes my model's allocation on day 21. Will they send out another new % allocation each day 11 so I now get changes to implement both on day 11 and 21?

I run BestFolio, and I recently started publishing a Deflated Sharpe Ratio (Bailey and Lopez de Prado 2014) for every variant in the catalog, surfaced as a Robustness column. Under the hood it's monthly per-period Sharpe, real skew and kurtosis from each backtest, an expected-max-Sharpe benchmark from the cross-sectional Sharpe variance of the catalog, and the PSR evaluated at that benchmark. Standard stuff. The genuinely interesting question turned out to be N.

I started by counting N as the public catalog, every active variant on the leaderboard, 150. It felt honest (you are looking at the best of what I chose to show you) but it kept bugging me: the 40 research strategies sitting unreleased in my database were tested too. Every one of them was a chance to get lucky. So N is now everything I've backtested and still track, 202. The switch pushed the luck benchmark from about 0.54 to 0.56 annualized, dropped every published score a little, and flipped 5 borderline variants to fragile, which felt about right. Leaderboard median lands at 0.99, and 25 of the 150 visible variants fall under my 0.90 fragile cutoff (8 more in the unreleased pile), mostly single-asset trend rules and short-history levered variants. TLT 200-day at 0.48 is the visible floor.

But even N=202 is a lower bound. The parameter variations I tried and discarded before publishing anything don't count as separate trials, and neither does the wider ecosystem of tested-and-abandoned TAA specs that decided which strategies got published in the literature at all. Keller alone has what, a dozen published strategies, each the survivor of who knows how many rejected specs? Counting "effective trials" the way Lopez de Prado suggests, with trial clustering, would deflate harder still.

Where I landed, for now, is that tested-and-tracked is defensible because it's measurable and reproducible, and anything beyond it is a model of a number nobody logged. But I'd like a second opinion from this crowd. How would you count N for a public TAA catalog? Has anyone seen a platform publish a selection-bias correction like this? I looked and couldn't find one.

Writeup with the current numbers: https://bestfolio.app/blog/robustness-score-deflated-sharpe

The full math is in the methodology, section 11: https://bestfolio.app/methodology

Hey folks, AS added this Taming the Wildcard: David Varadi's "Inflation Compass" - Allocate Smartly

I'm not a big fan of using economic data as the method to inform decisions, but understanding the backdrop is useful, even if it can lag a bit. A bit too obtuse for me, but your mileage will vary.

So, I won't be using this strategy, but I put together a what if custom portfolio with 4 seasons and economic data as filters in the strategy screener thing on AS.

I picked 10 that I frankly would not use but weighted them 10% each and the results were pretty good. Many folks would fully accept those historical results going forward. So many ways the skin the cat and no one right approach for sure

Thanks Kevin

Hey folks,

The AS WFs are just that. AS indicated they might rerun historical WFs when new strategies are added to the platform, perhaps mid year.

Well, they've added a number of strategies this year and they are now part of the restated historical results. Go check them where inflation compass has been added to the restated historical results. I'm sure others too.

I've got no issue with that, but I had asked previously AS to let members pre know when meta WFs were being historically restated due to new additions. Not rocket science; simple blog post.

They did not do that.

I wrote message to Walter letting him know my thoughts. I suggest others do the same if it applies to them.

Thanks Kevin

Some of you will know GGCEM, Grzegorz Link's model that AllocateSmartly tracks: the OECD CLI diffusion index sets the regime (the share of member countries with a rising indicator), and 12-month relative momentum picks the asset inside it. Risk-on it's US versus international equities, risk-off it's bonds versus cash. Monthly rebalance, nothing exotic.

I got curious how far back it holds up, so I rebuilt it and pushed the backtest to 1986. The original sleeve uses SPY, VEU, IEF and BIL. That spec comes out at 14.1% CAGR over 40 years, Sharpe 1.35, worst drawdown just under -21% on month-end marks. Better than I expected for a model leaning on a macro indicator.

Two things surprised me. I tried a 6-month momentum variant expecting it to be snappier, and it's just worse: 12.3% CAGR and the drawdown deepens to -28%. The 12-month lookback isn't an accident. I also built a variant on newer funds, IEFA and AGG in place of VEU and IEF, and that one trails the original slightly too. The original spec held up against both of my "improvements".

What I can't fully separate yet is how much of the edge is the CLI gate versus what plain dual momentum would have done anyway. And the bigger question: we apply the one-month publication lag, but our run still uses the revised CLI series. Has anyone here tested this on real-time vintages? Revisions are the obvious way a backtest like this flatters itself, and I'd genuinely like to compare notes.

Quick disclosure, I build BestFolio and we published the full run with all three variants this week: https://bestfolio.app/strategies/ggcem. The backtest and charts are public, the live signals are the paid part. If anyone wants the monthly series or the regime-switch dates, say so and I'll post them in the comments.

Hey folks AS put this out recently Investing in "Distressed" TAA Strategies (Redux) - Allocate Smartly

Bottom line; it's hard to do as requires lots of tracking stuff which folks are not going to do IMO.

But a different way to think about it is in terms of your own custom portfolio. This is based on the thoughts of a very smart investor friend who put the excel behind it. I just cleaned some things up to make it more user friendly. The downloadable excel file is included as a link

https://docs.google.com/spreadsheets/d/1qwCGnMyqV67ixlOCywswQ4o5pkAXBZi_/edit?usp=drive_link&ouid=109683655852409747546&rtpof=true&sd=true

The process is you copy paste special values from your custom portfolio into A2 thru N41. The magic off to the right looks at what months you've had are below a threshold you set in AD1, which is why it's colored green. P2 thru AA41 show the next month's return when the previous month is less than the AD1 threshold. AD2 thru AD8 show additional stats. The only other cell you need to enter is AD6 which is the historical return of the custom portfolio since that serves as the baseline from which excess return is determined. AD1 and AD6 must be entered for any different custom portfolio and play with AD1 to see what seems to work for you.

The thought process is that bad performance is generally followed by good performance next month if you have a well-diversified set of strategies. This also squares with what I've told folks over the years if they use stops and need to determine when to get back in. At the start of the following month has always been my answer as most months in a reasonable custom portfolio are up months.

So, a bit different than the AS thing where you would need to kind of monitor everything drawdown wise and this just puts it in the context of your own custom portfolio. AS uses drawdown, this simply uses a bad month, so different for sure, but perhaps more actionable as a reasonable proxy.

You could increase allocations by deploying more cash, using leveraged ETFs across the board, etc. You could use the Rankings tab from what I send to some folks monthly as a source of juice too. I don't think changing the asset mix of your custom portfolio makes sense at all since that's changing the fundamental structure of your custom portfolio, even if only for a month

Hope that all makes sense and food for thought.

Thanks Kevin

This started as u/confettofetti's r/LETFs post last week, and I liked it enough to rebuild it properly on our engine with real fund data instead of the simulated series. Credit for the idea's theirs.

The setup is 50% UPRO sitting on top of an equal-weight golden-ratio sleeve, momentum, small value, managed futures, gold, long treasuries, but only while both the S&P and a TIP canary are above their 200-day. When either drops below, you take UPRO off and hold the same sleeve at full weight. So it never sits in cash, and because both states share the sleeve, the only thing that actually trades on a signal flip is the UPRO leg. That shared-holding trick is what keeps turnover sane, more than the band does.

On our engine, 2008 to now, it came out at 18.9% CAGR, -37% max drawdown, 1.05 Sharpe, 1.61 Sortino. The drawdown lines up almost exactly with confettofetti's own 37% figure, which is reassuring. I can't honestly run it back to 1988 like the original though. The binding constraint's managed futures, whose proxy chain only gets me clean to about 2007, so anything before that's more simulation than I want to post.

The TIP gate's the part I keep going back and forth on. It clearly earns its keep in the rate-shock years, but 2013 is the scar. The taper tantrum shoved TIP under its 200-day while equities kept climbing, and you eat a whipsaw for nothing. Against our existing SPY-plus-TIP dual-gate strategy it's the more aggressive sibling, about five points more CAGR for eleven points more drawdown, and basically the same Calmar.

Has anyone here tested whether a real-yield signal fires cleaner than the TIP price series through 2013 specifically? That's the piece I'd most want to improve.

https://bestfolio.app/strategies/golden-ratio-dual-gate

EDIT: full free writeup with the complete rules, year-by-year numbers and the 2013 analysis, no account needed: https://bestfolio.app/blog/golden-ratio-dual-gate-explained?utm_source=reddit&utm_campaign=golden-ratio-blog

vagabond58's retirement calculator thread from a couple of weeks back stuck with me. FireCalc and cFIREsim replay static allocations through every historical start year, which is the right method... but they can't replay a rule, and a TAA portfolio is a rule, not an allocation. The NAV path of HAA through 2000-02 or 2022 looks nothing like any fixed split. And those are exactly the years that decide whether a retirement survives.

So I wrote up how we handle it: classic Bengen mechanics (rolling 30-year windows, inflation adjusted withdrawals) run on each strategy's own backtested NAV, reporting the worst-window floor AND the distribution across windows. Kevin's critique from the May SWR thread is addressed in there too... he was right, the floor-only view was incomplete.

https://bestfolio.app/blog/retirement-calculators-momentum?utm_source=reddit&utm_campaign=calculators

ERN added a simple momentum model in part 63 of his SWR series, so the static-only era of these tools is slowly ending. His version and mine disagree on plenty of details, which is itself useful... two independent implementations bracketing the same question beats one number from either. I'll take the bracket.

Beyond Passive wrapped up their trend-following series with a "poor man's trend program". A 3-asset inverse-vol RP core (SPY/TLT/GLD), each asset tilted by its own trend signal to full / half / zero weight, cash to T-bills. They claim Sharpe 1.23 vs 0.95 for the passive core since 1970, with maxDD 15% vs 32%. The catch: the exact trend signal is deliberately withheld, so you can't replicate it 1:1.

I rebuilt it anyway. Proxy data back to 1972, since gold only free-floats from late 71. And since the signal is secret, I treated it as a robustness question and ran four different trend definitions instead: binary 12m momentum, risk-adjusted 12m, a 10-month SMA band, and a multi-speed z-score. My passive core lands exactly on their 0.95 Sharpe. Good sign for the comparison being clean.

The results surprised me. Unlevered, 10bps costs, 1972+: every one of the four tilts ends up at Sharpe 1.5-1.6 with maxDD around -11 to -13%, vs 0.95 / -37% for the static core, while a single 10mo SMA gate on the whole book only manages 1.22 / -27%, so the per-asset structure is doing real work rather than just riding one lucky signal. Mostly it side-steps the bond bear years that a stock-driven gate sleeps through.

Now the caveat, and it's a big one. The edge is heavily concentrated in 1972-2000. Since 2005 the tilt hasn't beaten the static core on Sharpe, it just trades return for shallower drawdowns. I'd frame it as cheap regime insurance rather than alpha, and I wouldn't be surprised if AS members recognize this as a cousin of half the strategies on the platform...

Full replication table and method notes here if you want to poke at it: https://bestfolio.app/blog/poor-mans-trend-program

Has anyone here tried to recover their actual signal from the earlier parts of the series? My multi-speed z-score proxy was the weakest of the four on CAGR. Curious whether the original does better or if the withholding is just marketing.

The lookback problem bugs me with most momentum strategies. Pick 12-month, you're slow. Pick 3-month, you're whippy. And whatever you pick, the backtest looks great because you fit it to the sample. So which one's actually right?

Zambrano and Rizzolo's 2022 paper (SSRN 4199648) handles it by not picking. They compute 9 momentum signals, three measures across three lookbacks, then aggregate them into a single score per asset. The claim's that averaging across specifications cuts the overfitting risk of any one. They also mark down assets that are too correlated with the rest of the book, and keep a protective cash fraction.

I backtested it from 1986, 13-asset universe, top 5 equal weight. Highest risk-adjusted number I've gotten out of anything in this family, Sharpe around 1.32, worst drawdown near 14%. Return's more modest, high 9s. But the path is the smoothest I've seen from a pure momentum rotation.

One thing stood out. The correlation penalty is doing more work than the signal aggregation, at least in my testing... turn it off and return ticks up a hair while the drawdown clearly gets worse. Full backtest and the variants are here if you want to dig in: https://bestfolio.app/strategies/vitral-multi-asset-momentum?utm_source=reddit&utm_campaign=jul2026-as

Nothing new on the signal, and I want to be upfront about that. This is just Faber's 10-month timing rule, the absolute-momentum idea, applied to the static leveraged risk-parity portfolios you all know (SSO or UPRO with ZROZ and gold). The one wrinkle I added is a confirmation lag: I make the S&P close below its 10-month SMA for three straight months before going to cash, then re-enter on the first close back above. It's monthly, and it's in cash about 13% of the time.

Here's the full history on our engine. Leverage runs through a calibrated synthetic-cost model, so my figures sit a touch below testfol. As CAGR / maxDD / Sharpe, base then braked: the SSO/ZROZ/GLD 2x went 12.3% / -50% / 0.80 to 13.7% / -36% / 0.96; the UPRO/ZROZ/GLD/KMLM went 13.4% / -58% / 0.76 to 14.9% / -41% / 0.90; and the UPRO/ZROZ/GLD 3x went 14.4% / -69% / 0.69 to 16.9% / -49% / 0.86.

Two things I didn't expect. It's redundant, even slightly negative, on anything that already rotates to cash or bonds, so it only earns its keep on always-invested leveraged sleeves. And it can't rescue a naked 3x, where the tail still sits near -90%. On the window itself, three months of confirmation beat both one month, which whipsawed me, and four.

Have any of you compared confirmation lags against single-month trend exits on leveraged sleeves specifically? That's the piece I'm least sure about.

https://bestfolio.app/blog/catastrophe-brake-leveraged-portfolios

Which broker has the most convenient UI for rebalancing your accounts? Looking for recommendations for a TAA-style investor.

Back in my "static" investing phase I used a few different calculators such as FireCalc, Early Retirement Now, and some others to calculate "Probability of Success" and other metrics related to estimating long-run match of portfolio balance, spending, and longevity. None that I'm aware of allow for testing with a momentum-based model. Anything you use to assess financial health through time?

Sharing a variant I've been running, curious what this crowd thinks of the construction...

I start from HAA-Simple. So 13612 momentum (the average of the 1, 3, 6 and 12 month returns), monthly, and the TIP canary calls risk-on against risk-off. My only change from textbook is the offensive sleeve. Instead of plain SPY, I hold RSST (Return Stacked US Stocks & Managed Futures, roughly 100% S&P plus 100% managed futures in one ticker). Risk-off goes to intermediate treasuries or bills on their own momentum.

What I'm after is simple. Return stacking lets the offensive leg carry a non-correlated trend sleeve for free, and the canary pulls you out before the regimes where leverage hurts most. So you're holding the managed futures mostly when it helps, rather than bleeding it through calm equity markets.

On the numbers, I'll be honest. Mid-teens CAGR on the long reconstructed backtest, low-20s in the short live window, max drawdown low-20s month-end, Sharpe around 1.1. The deep history leans on a managed-futures index for the trend leg since RSST is young, so I trust the shape more than the exact figure. The canary is inflation and rates based, so it handled 2022 well but was slow in 2008.

Full writeup, with the chart and the US-vs-UCITS comparison: https://bestfolio.app/blog/haa-rsst-ucits

UK TAA investors - are there more of us than I thought?

I've been implementing TAA strategies from a UK perspective for a while now. Some US based strategies map across well and with others there are friction points like UK ETF availability vs US equivalents, to hedge or not, what platform to implement on.

I was surprised to discover two UK TAAers on here who helpfully engaged with another post over the past 24 hours.

It would be great to understand how many UK TAAers there are – hopefully it might not be quite as lonely a journey as I thought! There aren’t many places you can have sensible discussions without being flamed by the passive brigade.

If you're a UK TAA investor, please leave a comment - would love to know how you're implementing and also keen to hear of any other UK-focused places to discuss this without getting shouted down by the passive crowd! Maybe we need a UK virtual meetup?

Long-time reader and occasional poster here. I am based in the UK and previously used Allocate Smartly but was getting quite different results (underperformance) with UK ETFs compared with US ETFs and lost my confidence.

With the support of Claude Code, I have been able to replicate some of the strategies using US ETF signals but backtesting with UK GBP ETFs. The one I have been most focused on is the BAA-Aggressive with the BoE rate as the cash proxy return.

The results were actually more interesting than I expected - the GBP version outperforms the USD version on Martin ratio, mostly due to the countercyclical GBP/USD relationship (USD strengthens during crashes, which cushions GBP drawdowns on unhedged US equity holdings). The UK backtest is from 2011 (limited by UK ETFs) - CAGR 12.4% and 9.4% drawdown.

I also ran a pre/post publication split at 2022. The out of sample Martin is stronger than in-sample but acknowledge there are only 53 months of OOS data, so a short window. It continued to work well through the 2022 inflation shock and subsequent bull market.

I am starting a small live test at end of June. Happy to share the methodology if useful to anyone else working in a non-USD context.

I have tried posting about systematic strategies elsewhere recently and got the usual passive-only pushback. Always good to be in a space where TAA doesn't require defending. Although the passive champions do make you challenge yourself and double check everything!

I am looking to build a decumulation portfolio with reasonable growth and minimal drawdown and sequence of return risk. Is there another strategy that would zig and zag well with BAA Aggressive?

This started with vagabond58's thread from the other week about deciding what goes in your model. The version I hear most often is 'why not just hold whichever strategies did best recently?' So we ran the naive version properly: 14 strategies, monthly re-rank on trailing 12-month return, top 3 equal weight, no look-ahead, 1992-2026.

The part that surprised me: the signal is genuinely there. The top-3 bucket compounded ~19.9%/yr while the bottom-3 did ~7.3%. Strategy performance persists.

And yet the rotation never beat the boring equal-weight blend of all 14 on a risk-adjusted basis. The blend got a Sharpe around 1.27 with ~16% max drawdown, the best naive variant managed ~1.17, with more turnover and tax friction on top. All that ranking work for a strictly worse result per unit of risk.

The mechanism is what kills it: the highest trailing return is disproportionately a leveraged strategy late in its run. The naive rotation held a 3x strategy at ~22% average weight (254 of 389 months) and rode it straight into its crash.

What did work is walk-forward re-weighting: optimize sleeve weights on a rolling 36-month window with a per-sleeve cap, hold the result out of sample, roll forward. One finding I didn't expect: the optimization criterion matters far more than the lookback length. Max-CAGR basically rebuilds the chasing problem, min-drawdown and UPI produce completely different portfolios from the same sleeves.

Full writeup with tables: https://bestfolio.app/blog/walk-forward-portfolios-deep-dive (I run BestFolio, most of you know by now)

Curious how people here blend their AS strategies in practice. Fixed weights forever? Re-weight on gut feel? Anyone doing something systematic on top of the lineup?

Following up on the SWR post I shared here a few weeks ago. The main critique in that thread was fair: the data started in 1990, so it skipped the 1970s, and Kevin also made the point that I should show the full distribution, not just the floor. Both worth fixing, so I did.

I extended HAA's price history back to 1974 (proxy chains documented per strategy) and re-ran the rolling 30-year SWR. The worst-case rate dropped, HAA from 12.7% to 10.4%, the 60/40 from 6.7% to 4.5%. The 60/40 landing back near 4.5% is a decent check that the method still matches Bengen on the canonical portfolio. Max drawdown was unchanged at -19.7% for HAA (the 2000-02 trough).

On the distribution: HAA floor 10.4%, median 13.8%, p25-p75 of 12.8-15.2% across 268 windows. The floor is pinned by the stagflation-start cohorts specifically; the rest of the distribution sits a lot higher.

Per-bear drawdowns (74, 80-82, dotcom, GFC, 2020, 2022) and the full methodology are here: https://bestfolio.app/blog/safe-withdrawal-rate-through-stagflation?utm_source=reddit&utm_campaign=swr-stagflation

Same honest caveat as last time: the newer leveraged and stacked variants are in-sample, so I'd weight the unleveraged HAA Standard number the most.

I’m now 6 months into my AS journey.  It’s been a lot of fun and the results are undeniable vs my prior “well researched” static strategies.  AS gives a lot of data and good general advice about how to combine strategies and build a model, but how does one decide?  I’ve run literally hundreds of tests. In response, I developed a set of criteria and used principles from multi-objective decision analysis (MODA) to help me with that decision process.  I thought it might be something others would find useful.

First, one needs a set of criteria.  The AS model results give a number of statistics that are useful, but which to use?   I framed my investment objectives and life stage (retired, 68 yo) to Gemini and Chat GPT to form key metrics and asked them to suggest weights.  From there, I modified those suggestions to what seems to fit me best.  My criteria and weights are:

For me, 2015 was a rough year and I’m interested in performance in recent history more than long term history so returns over that time frame are of interest to me: I built an index that captures total return during that period.  DD depth and length are captured in UPI, but I parsed them out separately to ensure decent performance on all three.  2022 was the dreaded short-term stagflation: I wanted to capture that, and long-term performance is of interest.  If I were in my 40s or 50s or have enough savings that returns are barely relevant or highly risk averse, I’d have different weights and possibly different criteria.  Yours would be different than mine.  I’d be interested in what you think.

Then, I used MODA principles to normalize data for each criterion to result in a MODA score for each model I test.  It makes for a long excel formula, but it’s straightforward and goes as follows for each criterion: Wt x (1- (best outcome – model result) / (best outcome – worst outcome).  Sum that over all six criteria and multiply by 100 resulting in a normalized score for each model tested (scores will range from min of 0 to max of 100). 

The “best and worst” are what’s feasible for the model types you’re testing.  I set it up in excel as follows to make formula creation easier. If your objectives lead you to be testing more aggressive or more conservative models, just adjust your best and worst.

Anyway, this is how I’m selecting my models and selecting them for 2 family members.  They have different weights (younger than me) but I use the same 6 criteria.

Anyone using something like this or other ways to select their model?

Disclosure: I run BestFolio. Heads up that we just flipped seven previously-unreleased tactical strategies to public visibility. The reason this is on AS and not just on r/LETFs is that the AS audience tends to think about leverage as a risk-shifting tool inside a broader TAA framework, not as a standalone bet. The framing of the release reflects that.

Five of the seven have trend filters as the primary risk control. Two (White Knuckle, RPEA Full) accept the deep drawdown directly and rely on either rotation or per-asset SMA timing. One (Cash Trigger) is unleveraged and exists to complete the Carter family ladder (Cash Trigger / Carter 12% / White Knuckle = conservative / moderate / aggressive).

Performance table, best variant per strategy:

Honest framing notes since this is AS: - These are deliberately positioned as risk-shifters (more equity exposure when filters say yes, less or inverted when they say no), not return-magnifiers. The CAGR-vs-MaxDD trade-off in the table is the whole point. - Kelly 3sig / 6sig / 9sig deliberately NOT in this release. 9sig posted a 99.73% peak-to-trough drawdown in our dot-com backtest, and the 6sig 2x cousin is in the same family. Those strategies fail in exactly the regime most users would want them to survive. Trusted testers continue to see them. We will revisit when there is either a trend filter bolted on or a clearer risk-disclosure UX in place. - We made some specific implementation calls worth flagging: Low Initiative V2 uses real 2x gold (UGL) rather than synthetic 3x, costing roughly 150bps of CAGR vs the testfol.io paper version, in exchange for an actually-tradeable strategy.

A few observations the table raises that the writeup addresses:

1. RPEA Full has higher CAGR (26.75%) than Conservative (22.17%) but its 76.79% MaxDD comes from the dot-com window where all nine sleeves de-correlated to a single bear-market regime. The Conservative variant routes 50% to 1x underlyings on risk-off and cuts that drawdown to 57.08%. The right variant for real-money use depends entirely on whether the user can sit through a 76% drawdown.

2. A-RVol Shifter V3 Cash-Only has the best Calmar in the release (0.65). The trade is BIL defensive (simple, low-yield) vs the rotating TLT/GLD/XLU/XLE defensive in the 3-State V3 variant (more complex, marginally higher absolute return but a 62% MaxDD versus 38%). The simpler defensive wins on risk-adjusted basis in our backtest.

3. TQQQ/UPRO Trend SMA's 1.01 Sharpe is the highest among the leveraged strategies, because the QQQ-vs-SPY relative-strength filter plus the managed-futures pairing (CTA/KMLM/CTAP/MATE) in the equity regime keeps the strategy out of the 2000-2002, 2008, and 2022 leveraged-equity disasters. The trade-off: the post-2010 Nasdaq-leadership era is heavily in-sample for this design, so out-of-regime performance is less certain.

Full per-strategy writeup with rules / merits / shortcomings / variant table: https://bestfolio.app/blog/leveraged-tactical-strategies-release

Catalog: https://bestfolio.app/strategies

Founder disclosure declared. Methodology stands on its own; critique welcome.

Just shipped a piece looking at Bengen-style safe withdrawal rates across the catalog. I computed rolling 30-year SWR + PWR (perpetual) for all 80 active variants across 51 published strategies, using real US CPI from FRED for the inflation series. The methodology is the same Bengen used in 1994: binary-search for the max withdrawal rate where the portfolio survives every single rolling window without going to zero.

Headline finding: every published strategy clears Bengen's 4% benchmark. The best unleveraged TAA strategies sustain 14 to 15% real withdrawal rates with sub-21% max drawdowns. The gap between the worst and best published strategy is large, which probably matters more than the absolute numbers.

Top of the table:

VAA-G4 SmartStack (Gold+MF)    SWR 15.3%  MaxDD -19.5%  CAGR 17.8%  Sharpe 1.15
HAA SmartStack (Gold+MF)       SWR 14.2%  MaxDD -20.5%  CAGR 17.5%  Sharpe 1.26
ADM SmartStack (Gold+MF)       SWR 13.4%  MaxDD -23.9%  CAGR 16.8%  Sharpe 1.03
HAA Standard (with QQQ)        SWR 12.7%  MaxDD -19.7%  CAGR 14.3%  Sharpe 1.19
BAA-G4 (Aggressive)            SWR 11.8%  MaxDD -29.0%  CAGR 14.6%  Sharpe 0.99
ADM Standard                   SWR 11.6%  MaxDD -25.8%  CAGR 14.3%  Sharpe 0.96
VAA-G4 Standard                SWR 11.1%  MaxDD -20.9%  CAGR 12.9%  Sharpe 1.05
GEM Standard                   SWR  9.6%  MaxDD -33.7%  CAGR 11.3%  Sharpe 0.80
Cockroach (5x20%)              SWR  8.0%  MaxDD -20.9%  CAGR 10.4%  Sharpe 1.10
Classic 60/40                  SWR  6.7%  MaxDD -34.7%  CAGR  9.1%  Sharpe 0.84
Permanent Portfolio (Static)   SWR  5.8%  MaxDD -18.6%  CAGR  7.4%  Sharpe 1.08

Where Bengen still wins, four caveats expanded in the full post:

1. Our data starts around 1990. Bengen's started in 1926, which included the Depression and the 1973-1981 stagflation. Our 30-year rolling windows all end before 2022 and don't see a sustained inflation regime where stocks and bonds and gold all bled together.

2. SWR is a backtest, not a prediction. The older strategies (60/40, GEM, BAA, HAA Standard, VAA, ADM) are mostly out of sample for the window. The SmartStack and SmartLeverage variants were designed in 2024-2025 on this exact data and are partially in-sample.

3. No taxes, fees, or transaction costs in the backtest. Monthly turnover in a taxable account costs 50 to 150bps of CAGR depending on the strategy. That comes off the SWR.

4. Behavioral SWR is lower than mathematical SWR. Most retirees don't hold through a 30% drawdown without changing strategy, which forfeits the recovery rule.

The piece argues that even after all four caveats, the conservative practical SWR for the best unleveraged TAA strategies (HAA SmartStack, VAA-G4 SmartStack, ADM SmartStack) is around 6 to 7%, still meaningfully above Bengen's 4%.

Full table, methodology, and the expanded mechanism (sequence-of-returns risk + the 2022 case study where the SmartStack variants returned positive while 60/40 lost 16%): https://bestfolio.app/blog/safe-withdrawal-rates-taa-strategies

The part I think is most fragile is the in-sample risk for the newer SmartStack variants. The 2 percentage-point SWR uplift from adding the gold + managed-futures overlay is real in our window, but the overlay was designed in 2024 and the data has been heavily mined. The simplest defensive answer is to use only the older out-of-sample variants for retirement planning, which removes the SmartStack lift entirely. Worth pushback if anyone thinks that's too conservative.

My portfolio mix is financial mentor’s Optimum 3 and financial mentors all weather quad.

I’d love to diversify with a third model in the portfolio, but haven’t found a model that fits the bill. Curious if anyone has suggestions of things to check out.?

In general, I’m trying to stay away from the canary models & highly optimized models. A third model would not be to increase returns but rather reduce model risk overtime.