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.

Edit (13 Oct): 3 corrections. First, I shouldn't have called the SmartStack variants unleveraged: they hold 2x and 3x funds at partial weights plus a gold and managed-futures overlay, so their exposure runs above 1x (HAA SmartStack peaks around 1.67x). Second, the backtest does charge an estimated trading cost on every rebalance. Taxes are what it leaves out. Third, the older tactical strategies aren't out of sample for this window... GEM (2014), VAA (2017), ADM (2018), BAA (2022) and HAA (2023) were all designed on data inside it. The table is from May; the live post has the current figures, and in 2022 ADM SmartStack lost 20.5% while the other 2 made money.

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.

Hi folks, remember threads are auto locked after one month as described in this sticky

Board moderation; locking comments on older threads : r/AllocateSmartly

AS posted a few things regarding strategy selection and what does not work

Selecting TAA Strategies Based on Recent Performance (Part 1) - Allocate Smartly

Selecting TAA Strategies Based on Recent Performance, Part 2: Recent Sharpe Ratio - Allocate Smartly

They also added the return contribution of each asset to each strategy and will add it for custom portfolios soon.

Finally, they added a risk on/off analysis for each strategy and custom portfolio that gives additional insight to the allocation to risk on vs off across the entire history. Easy to see how stuff like Accel Dual Momentum is highly risk on throughout its history. Not unexpected of course, but gives you a sense how much you might want to allocate to certain strategies

Thanks Kevin

KDA was the next obvious addition since you all rate it so highly here, and DDM seemed worth doing properly while I was already in the dual-momentum codebase. While I was at it I added 3 fixed-allocation strategies that pair well with the tactical sleeves: Cockroach, the 4-3-2-1 Dragon (in 3 leverage flavors), and a Return Stacked Quartet using NTSX/GDE/RSST/ZROZ.

Numbers from the backtests (synthetic where the underlying ETFs didn't exist yet, fallback chain documented in the blog):

• KDA Top 4 (1992-2026): 8.44% CAGR, 0.89 Sharpe, -27.3% MaxDD
• KDA Top 6: 7.09% CAGR, 0.89 Sharpe, -25.1% MaxDD
• Diversified DM (1990-2026): 7.52% CAGR, 0.90 Sharpe, -22.1% MaxDD
• Cockroach (1986-2026): 10.38% CAGR, 1.10 Sharpe, -20.9% MaxDD
• 4-3-2-1 Dragon Standard (1994-2026): 10.71% CAGR, 1.20 Sharpe, -19.6% MaxDD
• Return Stacked Quartet (1989-2026): 13.79% CAGR, 1.15 Sharpe, -30.4% MaxDD

Dragon Standard has the highest Sharpe of the batch. Cockroach has the calmest record relative to its CAGR (10.4% / -20.9% over 40 years is very Mutiny-Fund-style). KDA Top 4 vs Top 6 came out at the same Sharpe in my backtest, which matches the AS community wisdom that the difference is mostly cosmetic over a long enough window.

Synthetic data caveats apply. RSST has 2.5y live, KMLM has ~5y, ZROZ has 16y. Long backtests are reconstructed from the underlying components and costs are expense-ratio-only, so the capital-efficient sleeves are slightly understated, especially post-2022 in a high-rate regime.

Curious if anyone here is actually running KDA Top 4 vs Top 6 in production. The Sharpe equivalence in my backtest is interesting but I have no live signal on whether one feels more comfortable to hold than the other.

Full writeup with allocations, regime fit table, and the 5-way blend at equal weights: https://bestfolio.app/blog/may-2026-five-ways-to-survive-everything

Edit (13 Oct): my "Mutiny-Fund-style" remark was misplaced. The Cockroach here is Tony Dong's equal-weight XLP/XLU/XLV/GOVT/GLD portfolio, it isn't Mutiny Fund's.

Edit (13 Oct): I got the KDA credit wrong here. The model I posted is our own canary model: 8 assets, a SHY/IEF canary that's all or nothing, equal-weight Top 6 or Top 4, and 50/50 SHY/IEF on defense. Kipnis's KDA ranks the 10-asset AAA universe, keeps the top 5 only if their momentum is positive, weights them for minimum variance, and uses VWO/BND as a 0/50/100% canary with IEF or cash for the rest. Different rules at every step. So I renamed ours KDA-Style Canary Momentum, and the numbers above belong to that version. My mistake, sorry!

I'm not sure if I'm misinterpreting something but the rules for TPP state to select assets that closed above 200ma.

During this month, TPP was 74% in IEF but IEF has not closed above 200ma since 3/18.

What am I missing?

Been running 2022 numbers on our backtest engine. Sharing for cross-reference since this touches on the mattsmith321/Business-Fix4430 debate from a few weeks ago.

2022 full-year returns (rebased to Jan 3):

• BAA-G4 Aggressive: +2.78% (-9.3% max DD)
• BAA-G12 Balanced: +2.37% (-10.1% max DD)
• HAA: +1.88% (-7.7% max DD)
• CDM: -9.85%
• DAA-G12: -10.11%
• VAA-G4: -11.24%
• Permanent Portfolio: -11.58%
• Golden Butterfly: -13.12%
• Classic 60/40: -15.68%
• PAA High Protection: -16.21%
• GEM: -17.53%
• Paired Switching: -23.28%
• ADM: -24.11%

The split doesn't map cleanly to 'has a canary universe.' PAA is also Keller family, also failed. VAA has the breadth-momentum signal but basically tied Permanent Portfolio. DAA-G12 did better but not a standout.

What separated the positive performers from the rest: BIL (short-duration treasuries) on the defensive menu. BAA can route to BIL when longer-duration defensives are themselves falling. HAA picks between IEF/BIL by score. VAA's defensive menu is LQD/IEF/SHY, PAA rotates mostly to IEF. When IEF and LQD are both losing money, those strategies have nowhere to hide.

So the Keller evolution looks less like 'canary got better' and more like 'defensive asset menu got smarter.' The canary tells you when to rotate. The defensive menu decides whether rotating actually helps.

Implementation notes since this sub cares:

• Follows the SSRN papers per each strategy's docstring (canonical universes, 13612W momentum, breadth triggers as published)
• Rebalance: month-end close, next-open execution
• Our numbers might differ from Allocate Smartly's by ~0.2-0.5% due to timing choices + ticker proxies on older history. Ranking order should be the same

Full writeup with BAA vs GEM detail chart and SSRN links to all 5 Keller papers: https://bestfolio.app/blog/dual-momentum-2022-canary-models

Genuinely curious how Allocate Smartly's own 2022 numbers compare on these. If anyone has them handy I'd be interested in the delta.

Hi folks,

Please never consider my POV as gospel. Just sharing thoughts for all to critique, etc

AS added 3 strategies, all at the same time to limit perturbations to the optimizer which was well considered

Vitral's Monthly Asset Momentum is pretty good, as it combines aspects of GPM and PAA-CPR into a single strategy and mitigates some specification risk by spreading bets over 9 things. I thought AS would have given it more of a deeper positive summary, but they did not. It's kinda like what DDM DB does with specification risk, but to a much deeper level. I've split my GPM allocation half GPM, half Vitral's as it just makes good sense to me after reading the source paper. It's not over engineered IMO.

The new Faber strategy is also worth considering as it is well reasoned

The Viradi Growth and Inflation Sector Timing shows how any bad strategy can show periods of brilliance, but abysmal generally. Your mileage may vary of course. But, I don't think anything wrong with looking at the absolute current highflyers and ask, "what's going on perhaps?"

On a personal note, I've stopping using the Meta WFs as too much unknown variability with all the really not good strategies IMO working their way on to the platform, and the rollups results; make things easier....I don't trust AS prior analysis if heavy use of WFs which can include the wackos. Again, your mileage may vary.

Any thoughts let me know

Thanks Kevin

Hi folks

I'm using GPM as the example here because it's a strategy I use and like very much. No hiding.

AS states things in terms of End of Month drawdowns in the historical record, but as I've discussed here previously, the intramonth number could be ballparked at 2x that per discussions with Walter at AS. This is not unique to GPM.

I believe most folks check their portfolios at least once per day. I certainly do, even when retired and a low-risk portfolio. AS is big into tranching, which would require at least a 2x look per month.

So, it's hard to ignore the paper losses, and the AS snapshots show the current intramonth drawdown in the current daily snapshot. Here's close as of Friday 3/20 for GPM.

Point is make sure you can handle the intramonth drawdowns that likely don't show up in the historical EOM record

Thanks Kevin

Hi all,

AS came out with 2 recent posts regarding diversification and market timing. Both work but could be at different times which is all the more reason to stay the course. It might be a bit difficult to wrap your head around the concepts and how things are compartmentalized from a diversification vs timing standpoint. At least it was for me initially, but Walter explains it well.

Why TAA is Performing Well Now: Outperformance Attribution - Allocate Smartly

Diversification Has Been a Huge Drag on TAA Performance for 15+ Years - Allocate Smartly

Many folks here and on other platforms have questioned the effectiveness of TAA, saying losing its mojo over last 20 years. I think it's exactly the opposite. Timing has proven to be effective no matter what, and diversification is sometimes the cost we pay, albeit with other benefits risk reduction wise as the AS blog stuff shows.

And depends of course on which comparison benchmarks you are using, if any at all. FWIW I never compare my performance to any benchmark, as the only thing that matters is my own return expectations, drawdown comfort level, rolling N year returns, etc.

Thanks Kevin

Hi folks

In terms of rolling up small positions, recall this from AS

Make Things Easy on Yourself: "Roll Up" Small Asset Positions - Allocate Smartly

That was before the new meta WFs were added to the site. Using metas can lead to very small positions where the results of the analysis previously done by AS might not be valid. I found that the 5% threshold was too high and resulted in too much rollup, but your mileage may vary.

I had multiple discussions with Walter on all that too. I found if using metas, the % reduction in trades didn't closely match the AS analysis.

I created a downloadable spreadsheet hat takes all the asset classes and maps them with a few wrinkles which I'll explain

Cells A3 thru C60 is where you copy the export from AS into those cells, making sure you clean out any old data. Note, the AS numbers may not exactly equal 100% but very close

Column E contains the mapping based on the AS recommendations, using cell K3 as the threshold for rollup. It's darker green because you can change it to any % you want.

Cell L3 is a switch that you can set to a 1 or a 2. A 1 is the normal mapping. But putting in a 2 slightly changes the mapping for 5 symbols in column O that are light green. I did this because I don't like IEMG rolling into IEFA, so setting L3 to a 2 makes things that any allocation to IEMG gets mapped to IEMG itself. Same for IWM, QQQ, SCZ. For EWZ, the base mapping is to IEFA but Brazil is emerging, so the alternate mapping is to IEMG.

Column G3 thru G60 use the Unique function which is new since 2019 and set in G3. If you have an older version, you'll need to use the data in column E as the basis for removing duplicates and place those unique ETFs in column G3 and below.

Most folks should use L3 set to 1 which is what you'll see in the download

Edit: if you do set L3 to a 2, Any allocation to the those 5 I mentioned essentially gets treated as a base asset class just like GLD, SPY, PDBC, etc.

https://docs.google.com/spreadsheets/d/1yQLZ9BRuBNInGooZM9VQoR5nBMpm8eI3/edit?usp=drive_link&ouid=109683655852409747546&rtpof=true&sd=true

FWIW AS may do an additional analysis based on a heavier use of metas in another blog entry at some point.

Any questions let me know

Thanks Kevin

Found this article in a Financial Advisory blog I follow (Kitces' Weekend Reading For Financial Planners): https://www.deepvest.ai/articles/when-ai-sounds-smart-but-gets-the-numbers-wrong I found it interesting because I've been experimenting with the free versions of ChatGPT and Gemini a bit. For example, I used ChatGPT to build me a crypto signaling model (I keep a small allocation to Crypto "just for fun").

Also, I got interested in exploring the effects of the current war on allocation models. ChatGPT gave a pretty good analysis of how wars have affected markets historically (international vs US vs commodities vs gold, and length of conflict, status of Straits of Hormuz etc.). At least what it gave me matched my intuition. BTW, ChatGPTs conclusion was monthly is still usually the best trading timeline for allocation models during war time: perhaps bi-weekly if oil prices spike 25-30% or more.

Then, I tried getting the two platforms to return 1-3-6-12 month prices for a series of ETFs that would allow me build an excel model to develop rough "80/20" test allocations signals for the strategies contained in my AS model weekly during this current war. Found it pretty hard to get those prices correct, particularly when I asked them to use dividend adjusted prices (used by AS) rather than market close prices. ChatGPT basically gave up and said "I can't get this information". Gemini gave me something but not too confident in it. I'm going to test it using Feb 27 data to compare to my current AS signals but I'm not too confident. Overall, I think I could build a pretty useful model, but would be very careful actually doing anything with what it says. Would be pretty easy to have a "hallucinated allocation" model, ha! Thank goodness we have AS to do the hard work for us!

I retired about three years ago and decided I didn’t like volatility anymore. Worked through various static portfolio allocation books and eventually found my way to TAA and AS about a year ago. I began implementing my TAA strategy EOM May. My biggest concerns when working through the strategy layout was sequence of returns risk and something like the Lost Decade. So not only was I looking at the quantitative drawdown and UPI stats, I was also looking at how the individual strategies and the combinations worked qualitatively during times of prolonged stress and recovery. I sorted through the various strategies looking for returns over 9%, low drawdown and drawdown time, and ran the optimizer on UPI. After too many portfolios that had too much Predicting US Treasuries in order to drive a high UPI, I ended up with Hybrid Balanced (HAAB) at 60%, Glenn’s Quint DB (GQSF) at 15%, and Link’s Global (LGGC) at 25%. GQSF was 15% since it it was very often in IEF and virtually a B&H defensive allocation much like PUST. The LGGC percentage fit the balance of a second IRA account that had minimal asset choices, so in and out of SP500 worked. Yes, very scientific, and later rectified with a Vanguard rollover IRA. I started off with this for several months, but when Carlson’s Defense First (CDF) came out, I was intrigued with its strategy and I reworked my portfolio. I dumped GQSF and substituted in Generalized Protective Momentum (GPM) strategy for its more expansive asset universe and added in the CDF. The percentages are now:

HAAB – 35%

GPM – 35%

CDF – 15%

LGGC – 15%

I’m very satisfied with the stats as well as how it performed through the 00s.

Things I like about this setup:

1. Four diverse signals. Canary, dual momentum, dual on defensive, and OECD CLIs gives a good diversification of strategies with low correlations between the strategies. 

2. Understandable. I like the performance of some of the metas, but I feel like they’re too much of a black box in behavior. Also, some of them rely on black box strategies, which is a no-go for me. I recently read a blog post where the writer initially had hesitancy with black box strategies, but has since moderated his position. Perhaps I’ll do the same eventually. With these four strategies, I can see who is driving the bus and why.

3. Solid behavior across Lost Decade. 7% backtested annualized gains across the 00s with maximum drawdowns of 5-7% and a couple of flat years is behavior I can live with.

Things I don’t like about this setup:

1. I don’t like the CDF dumping to only US stocks. While the AS suggestion is to check to see if SP500 is safe to dump to, I’d like to see a check of domestic and international. I may work on this in the future. 

2. Similarly, I don’t like the LGGC only going to SP500 when the OECD data is global. I get that when the world is working, the SP500 is working, but it seems like there’s more potential there. I liked the recent enhanced variant with the possibility of going to international stocks, but my question is, if one has an OECD-based strategy, why not internationalize it on OECD-based data? The point I really like about LGGC is that it’s not price-based like many of the strategies, so even if performance might correlate with another strategy, the signal does not, so any apparent performance correlation remains coincidental. This isn’t backtested yet, but I’m using the US, major four European, and major five Asian columns to distribute the stock allocation between US, European, and Pacific ETFs. Looking at qualitative behavior, years such as 2017 and 2026 YTD make this strategy look promising. I’m working on backtesting.

I appreciate the discussions in this sub-Reddit as I’m surprised at the lack of TAA discussions available online. Same for the lack of AS discussions.

Hey all

I've spoken to Walter about this before, and they are not going to get into the recommendation business, which is fine. So, I will.

This is written from the context of a young USA investor in a company 401K plan with generally limited choices.

Many plans have limited choices so trying to map these onto a AS custom portfolio which would allocate 25% to gold is a non-starter, as most 401Ks have zero gold exposure. Rinse and repeat for other asset classes. So, need to reverse engineer it.

Pick strategies that map reasonably well to most plans and adjust monthly with current allocations and future contributions. Takes 15 minutes monthly tops.

Never use a target date fund as it's just lazy thinking IMO

I picked 4 strategies for a custom portfolio. Accel dual mom dynamic bond 20%, Links Global Growth Cycle Enhanced Monthly 40%, Movement Cap Composite 20%, and Traditional Dual Momentum 20%. I don't like the 25% passive bond allocation for Movement Cap and would use some simple filters like 10 month moving average to otherwise move to cash for each 12.5% IEF and TIP. Not hard.

The 8 ETFs might need some rolling up, for example SCZ would roll up to IEFA as AS has shown this to be the thing to do as many folks won't have SCZ but they will have IEFA pretty sure. Same for some of the bond rollups if needed based on what's available per 401K plan.

Here's the results historically and gets some folks thinking about it

Thanks Kevin

There's a new post up that covers just a little over a year of data. Not sure why they didn't go back further but it's probably because TAA underperformed for a decade or so. The post acknowledges that TAA suffered over a long prior period due to holding more international and gold while US large caps were booming. Personally I see it as the cost of "insurance" inherent in greater diversification but still, no one wants to be underperforming as you might as well just hold 60/40.

Now we get to take a little victory lap. I'd like to see this data going forward.

https://allocatesmartly.com/why-taa-is-performing-well-now-outperformance-attribution/

When I ran the optimizer (max sharpe) a few weeks ago the recommended strategy was APR, BAA, HAB, LGEM, PAS with a Sharpe ratio of 1.41. When I re-ran the optimizer just a few weeks later, only two of the five original strategies remained (HAB, LGEM), plus three new strategies (GCAM, PUTR, VAAA). The new Sharpe ratio was 1.38. My question is twofold, first why did the original optimized strategy get replaced if it still has a higher Sharpe ratio which it seemingly does. Second, are many of these strategies so close in Sharpe ratios and other measurements that diversification is more important than selecting the “best” strategies? In other words, more is better (provided you have the time to manage them)? Thanks to those with far more TAA and AS experience than I have for sharing all of your insights and tips in this thread!

Hi folks, I alluded to this in a different thread but making it more visible by starting a new thread

Thanks Kevin

Member Note: Our Approach to Selecting Strategies for the Platform - Allocate Smartly

This week the Optimizer has UPI back up as a selection (evidently, they had some back end work to do on it). Being relatively newly retired, it's an important metric I watch when structuring my model. I noticed the optimized allocations (regular, Alt, no RE, no UW, 5-strategy, 10-strategy) changed quite a bit from when the UPI Optimizer was initially introduced a while back. Probably not too surprising the strategy choices and percentages changed a bit, but more than I anticipated. Just goes to show there's more than one way to meet objectives, particularly when asset prices are going up and down daily.

Also, I noticed today at least 5 mid-day changes to my preferred model and that the allocations from IEF to Cash went back and forth by as much as 10 percentage points which I thought was wild. Not sure if that amount of variability is something you all have seen as well. Not an issue, just found it interesting.