No changes to the portfolio this cycle.

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2026 has been rough so far, but I’m still optimistic. We’re already more than a quarter of the way through the year, and I still think reaching seven figures by year-end is achievable.

Hope your portfolio fared better — and more importantly, that these ceasefires turn into something lasting.

The Original

Varadi’s strategy slices the macro landscape into four quadrants — think of them as “four seasons” — based on the direction of growth and inflation, similar to All Weather or Permanent Portfolio frameworks:

To estimate forward growth, Varadi uses the current trend of the S&P 500. The logic is that the market is forward-looking, so the S&P 500 can serve as a reasonable proxy for expected U.S. economic growth.

For inflation, he gets more creative. He builds two sector portfolios — one with positive expected inflation beta and one with negative — then divides the first by the second to create a ratio-based leading indicator:

From there, using a mix of empirical evidence and economic intuition, he maps each growth/inflation regime to a sector that tends to perform well in that environment:

📣 Quick note: This post is for informational purposes only, not financial advice. I’m not recommending any specific investment. Do your own research and consult a professional if needed.

My Adaptation

For my QuantMage version, I simplified things. I used the XLE/XLP price ratio as an inflation proxy — XLE captures the positive inflation beta, XLP the negative. Then I applied momentum filters (SMA12 and 13612W) to SPY and the ratio to gauge how growth and inflation are trending:

You’ll notice an intentional inconsistency: the momentum filters applied to the ratio differ depending on the growth regime, and the consecutive-day requirements vary too (highlighted above). These are deliberate tweaks to squeeze a bit more juice out of the strategy.

The result? It handily beat SPY in both absolute and risk-adjusted returns over a 26-year period. I’m quite pleased that performance lands in the same ballpark as the original despite the simplification. It also handled the recent geopolitical turmoil well, parking in the energy sector at just the right time:

That said, it’s not immune to overfit concerns — the fine-tuning I mentioned is one factor, and the original itself is a relatively aggressive approach for a tactical strategy since it goes all-in on a single sector at any given moment.

Still, I think it’s a compelling framework. At the very least, it’s a nice example of how macro regime thinking can be translated into a concrete, rules-based sector rotation model.

You can check out the strategy yourself here. Hopefully it gives you a few ideas to explore in your own research.

Since the last update, the portfolio slipped -3.3%, modestly underperforming SPY’s -2.9% over the same period.

On the composition front, I made some notable changes. Two spells got retired — SVIX 4TLT and Jackal’s Mean Reversion + BND vs. SH Canary — both of which had been showing signs of degradation recently. In their place, I introduced a new volatility spell called Volatility Long/Short. The historical backtest on this one is genuinely exciting, though the track record is too short to be trustworthy. Time will tell whether this swap ages like wine or like milk.

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On a more serious note — I sincerely hope the conflict in the Middle East finds resolution soon, first and foremost for the people caught in it. And yes, selfishly, for our portfolios too. 🕊️

What’s been most puzzling is the way the market’s been pulling in opposite directions at the same time. Consumer staples have been surging so aggressively that their valuations are starting to look tech-like, while a lot of SaaS has been getting punished — a bit of a “SaaSpocalypse” that caught plenty of investors (me included) off guard. It’s the kind of regime shift that makes you question your priors.

Holdings remain unchanged with one exception: I tweaked COIN or Not to make it a bit less idle when bitcoin momentum goes missing — which, given the prolonged downturn, has been more often than not lately.

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On a separate note: tax season is approaching. It’s a ritual I never look forward to. Still, I’d much rather write a larger check because of realized gains than hope for a refund born of losses. That’s a trade I’ll make every year.

Here’s to frictionless filings — and markets that eventually make sense again.🤞

🏆 Winners Beyond the Backtest

Trending Alternatives

So far, this one has delivered a strong return that’s stayed pretty uncorrelated to the broader market. And that’s even after removing the now-delisted ETF (HNW). Which does raise an awkward question: was taking it out of my portfolio a bit premature?

Defense First

Another spell that’s held its ground impressively. Even with the recent pullback, its performance has stayed solid.

GBTC/GLD

It’s been sitting in cash since October, but zooming out: I’m totally fine with how this has played out. It did what it was supposed to do — participate when conditions were favorable, then step aside when they weren’t.

IAU/USO

This spell uses a completely different signal from GBTC/GLD, yet the pattern looks strikingly similar. Performance has tracked right in line with its historical results.

Corporate Bond Short-term Momentum Canary

Another standout — strong returns with remarkably low volatility. Hard to ask for much more.

📣 Quick reminder: This is just a peek under the hood of my systematic strategies — not investment advice. Always do your own homework and talk to a pro if you’re uncertain.

🕰️ Too Early to Tell

SPHB/SPLV

At first glance, you might think this one did fine. But look closer — the signal never actually flipped. It sat in QLD for the entire out-of-sample period:

So the jury’s still out. We haven’t really seen what this spell does when it matters.

The Volatility Edge

Same story here. The negative eVRP scenario simply hasn’t materialized during the period, so we’re still waiting for a real test.

RSI Ensemble

Mostly stayed in cash. It’s honestly surprising how few overbought or oversold signals triggered, given how noisy the headlines have been lately.

Wrapping Up

No duds to report this time around, which is great. But let’s keep some perspective: the market hasn’t really gone through a sustained drawdown in recent months. The correction over the last few days is a good reminder that the real stress test may still be ahead of us. That’s when we’ll really see what these spells are made of.

The pullback over the last few days might be nothing… or it might be the opening scene of a longer correction. Either way, I’ll keep tracking these and will post another hindsight update once we’ve had a bit more regime variety to chew on.

The Original Flavor

As you can see, implementation in QuantMage is dead simple. I used annual rebalancing with VTI for stocks. Over the last 18 years, it delivered an annual return of 7.4%. Not exactly a “print money” headline—but the surprising part is the quality of the ride: a Sharpe of 1.02 from such a basic allocation is nothing to ignore.

⚠️ Just a heads-up: None of this is financial advice. Just one mage tinkering with spells. Always DYOR and consult a qualified advisor if needed.

Jazzing It Up

For those who want more action, we can add a momentum filter and ditch the cash. Each asset gets checked against its 200-day simple moving average—only those with confirmed breakouts make the cut. This takes advantage of how QuantMage handles empty branches under equal weighting. I also swapped in QLD (2x leveraged QQQ) for extra juice, and tweaked the momentum check durations across asset classes to polish the historical results. Fair warning: more tuning means more overfitting risk. Also a threshold rebalancing is used this time.

The simulated results? A 21.7% CAGR over 18 years. Hitting that kind of return while keeping volatility in check (Sharpe and MAR both above 1.0) is no small feat:

Digging into the stats, you might think TLT (bonds) is slacking—just a 1.2% return while active more than half the time. But its real job? Playing defense. Take it out, and the portfolio’s risk-adjusted return noticeably suffers:

Final Thoughts

This little experiment is a nice reminder that you can get a surprisingly decent risk-adjusted return with a portfolio that fits on a sticky note—just three or four ETFs.

No, it’s not invincible. A year like 2022 can still hurt—when stocks and bonds struggle and gold doesn’t bail you out cleanly, both versions can take a hit. But over nearly two decades, the overall track record is still… not something I’d sneeze at.

Curious what you think:

• Are momentum filters here “signal” or mostly “curve-fit glitter”?

• Does TLT-as-crash-padding still make sense in a world where bonds can have equity-like drawdowns?

• Would you rather keep cash (protection) or replace it with “sit out when trend is down” (optionality)?

Big shoutout to Martin for sparking this idea with his recent article.

You’ll notice a change in the lineup: I’ve retired the KMLM Canary Combo and replaced it with what I’m calling the Uncorrelated Combo. The appeal? Near-zero correlation to the broader market, paired with high-teen CAGR over 18 years of backtesting. On paper, it’s exactly what I want. In practice? The proof is in the pudding—stay tuned.

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On a broader note, multiple concerning conflicts continue to unfold around the world, both internal and external. Here’s hoping common sense and peace prevail this year and beyond.

Gold had a banner year in 2025. And if you’re feeling smug about SPY’s 18% return, Ray Dalio would like a word—he pointed out in his year-end recap that in gold terms, SPY actually lost 28%. Ouch.

Here’s the thing: gold is about as “hard” as money gets when it comes to preserving purchasing power over the long haul. Yes, after such a strong run, we might see some consolidation. But with major economies drowning in debt and fiscal discipline nowhere in sight, I still like gold’s odds against most fiat currencies over time. The diversification angle is compelling too—gold’s low correlation to stocks makes it a solid portfolio stabilizer.

So rather than debate narratives, here are three tactical, rules-based approaches (“spells”) you can use to manage exposure.

Cross-Asset Price-Based Regimes for Gold

I picked up this idea from a recent Quantpedia article. The concept: use 12-month momentum signals from both gold and treasury bonds to determine when to hold gold.

The setup is straightforward. GLD represents gold, IEF (an intermediate-term treasury bond ETF) provides the bond signal. You only hold GLD when both assets show positive momentum—otherwise, you park in SHY (cash). I’m using 250-day cumulative returns as my momentum measure, checked over ten consecutive days.

The results? Over ~20 years, this trails GLD buy-and-hold in raw return, but shows a small edge in risk-adjusted performance. It also handled ugly gold years like 2013 and 2015 more gracefully (when gold saw meaningful drawdowns).

Intuition: you’re basically asking for “gold strength + bond strength” before you take gold risk—otherwise you default to something cash-like.

TIP for GLD

Another article-inspired strategy, and it shares DNA with something I’ve covered before—both use inflation expectations (via TIP) as the primary signal.

The rule: go long gold (IAU this time) when TIP’s 100-day return stays positive for three consecutive days. When that condition fails, rotate into a defensive 50/50 split between SHY and IEF.

This one delivers comparable absolute returns to buy-and-hold over a similar timeframe, but again with noticeably better risk-adjusted performance.

Intuition: you’re letting a “macro-sensitive” asset (TIPS) act as a gatekeeper for when you want gold exposure, and otherwise you hold a conservative bond/cash blend.

Oil vs. Gold

For the final strategy, we’re pitting gold against “liquid gold.”

This approach uses price ratio and percentile ranking to measure IAU’s momentum against IEO (iShares U.S. Oil & Gas Exploration & Production ETF). When oil is winning the momentum battle, you rotate into USO (United States Oil Fund, LP).

The backtest over 19 years shows more exciting returns—though with volatility to match. Fair warning: that flat stretch from 2012 to 2015 would have tested anyone’s patience.

Intuition: sometimes gold and oil trade like competing “macro hedges.” This spell tries to own whichever one is acting stronger right now, instead of committing to gold as the only hedge.

📣 Quick disclaimer: This post is for informational purposes only and isn’t financial advice. Please do your own research and consider talking to a professional before making any investment decisions.

Final Thoughts

Gold isn’t just for doomsday preppers and central banks. It can be a smart, tactical piece of a modern portfolio—especially when paired with regime-based logic to avoid the dead zones.

So what’s your 2026 gold outlook? Bullish, bearish, or just waiting for Powell to flinch? Got any smart signals or ideas I should test next? I’d love to hear your take.

Best Spells

I looked at how each of my live spells performed for the year, counting only those fully invested the entire period. That means BDRY-signaled SSO/EUM with RSI Pops, KMLM Canary Combo, Price Ratio Momentums, and SPHB/SPLV with RSI Ensemble didn’t make the cut. On a time-weighted basis, the top three were:

1. WOOD/XLU/BDRY Canaries Leverage

2. SVIX 4TLT

3. JJ’s Trinity Canary Leverage v1.0

A quick “what-if”: BDRY-signaled SSO/EUM with RSI Pops (entered on 1/7/2025) likely would’ve taken the top spot if it had been invested just one week earlier.

By risk-adjusted return (MAR), the leaderboard shifts slightly:

1. WOOD/XLU/BDRY Canaries Leverage

2. EM 4TLT

3. JJ’s Trinity Canary Leverage v1.0

Worst Spells

On the other end of the spectrum, a few spells made themselves… memorable:

1. COIN or Not

2. Short SPY + The Best Macro Indicator

3. Jackal’s Mean Reversion + BND vs. SH Canary

Also worth calling out: KMLM Canary Combo (entered in March) has been a rough ride so far as well.

Post Stats

I published 32 posts in 2025—short of my one-per-week goal, mostly because I lost momentum in Q4. The three most-viewed pieces were:

Major QuantMage Features Added

QuantMage also got a solid set of upgrades this year:

• Price Ratios

• Partial Cash-Out

• Uncorrelated Tickers

• Percentile Ranking

• Stochastic

Looking Ahead to 2026

I’m not satisfied with the current performance. I’m hunting for more uncorrelated sources of return, and I already have a few spells on the shortlist to retire or replace.

2026 will also bring a big personal change for me and my family, which likely means less time for investment research than I had in 2025—so I’m going to be more intentional about what I keep, what I cut, and what I automate.

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Happy New Year, everyone—here’s to a more prosperous 2026!

Against that backdrop, my portfolio climbed 2% since the last update, edging out SPY’s 1.3% gain. Not bad, though I’ll admit I was hoping to punch through the $900k level again. Maybe next time.

Looking back, this year’s returns haven’t exactly been fireworks-worthy—but hey, green is green. With two weeks left on the clock, we’ll see if the market has any surprises in store. Santa Rally, you’re up. 🎅🏼⬆️

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Crypto didn’t get the memo about “Uptober” or “Moonvember” either, and my COIN or Not strategy extended its losing streak. The bright spot: the new entry, SPHB/SPLV with RSI Ensemble, has been behaving nicely so far and offering at least a bit of performance relief.

On the creative side, I’ve been going through another bout of writer’s block and haven’t been very productive on the newsletter front. I did ship a new feature in QuantMage recently, though, which makes me feel slightly less useless.

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Hard to believe 2025 is already heading into the final stretch. Here’s hoping both my portfolio and yours can still pull off a happy ending to the year! 🤞📈

You might notice I made an unexpected call to retire Trending Alternatives, the live strategy I recently shared. One of its core holdings, HNW, was delisted—making this the second time that spell had to be reworked due to a delisting. Rather than patch it again, I shelved the spell and rotated into SPHB/SPLV with RSI Ensemble. I used the change as an opportunity to rebalance the whole portfolio.

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Between recurring tariff brinkmanship and expanding state capitalism, it’s been a tricky market for systematic strategies to navigate. I’m feeling pretty fortunate about how things have played out so far this year. Here’s to a strong finish before year-end!

🏆 Winners Beyond the Backtest

Inflation-signaled Gold/Treasury

This one traveled well out of the lab, delivering an uncorrelated return over a six‑month out‑of‑sample window. Half a year is not proof of persistent edge, but it’s a good first chapter.

Yen Carry Trade Unwind

Another solid performer. It held up particularly well during the Liberation Day drop, which speaks to its defensive character during sudden regime shifts.

BDRY Signal

This dodged much of the Liberation Day air‑pocket, but it missed part of the early rebound. Net‑net, performance was roughly in line with the benchmark over seven months, which is respectable.

Credit Rotation via Utilities

Another spell that sidestepped the drop. Absolute return is modest over six months, but that’s by design—low‑risk / low‑return—and it behaved exactly like that.

🕰️ Too Early to Tell

Catching Crypto Trends

The Bitcoin spell has gone mostly sideways over five months. Given muted BTC action in that window, I’m inclined to give it more time before judging the signal.

🪦 The Duds (So Far)

Monthly Seasonality

This one struggled out of the gate. If you look at the first chart above, you can see the trend deviation right away. Without a strong causal story for the month‑by‑month picks, the in‑sample “win” looks more like data mining than durable edge.

⚠️ Reminder: This isn’t investment advice. Just one mage’s systematic scribbles. Always do your own research (DYOR), and consider talking to a qualified advisor before investing.

Conclusion

• Rearview mirrors aren’t windshields. Past performance helps with context, not certainty.

• Signal pain is ambiguous. It’s hard to tell a rough patch from a broken edge; your thesis‑level conviction is what gets you through the former.

• Don’t rent conviction. No pundit—no matter how data‑driven—can hand you belief. Kick the tires yourself and own the decision.

TL;DR: QuantMage lets you run a single “über spell” that encodes your whole portfolio as one strategy or a portfolio of independent spells. I prefer the portfolio approach for transparency, diversification controls, and cadence flexibility—but I still keep an über spell around as a handy simulator and record‑keeper.

Let’s talk portfolio strategy. If you’re using QuantMage, you’ve probably wrestled with this question: Should you bundle everything into one mega-strategy (the “über spell”), or keep your strategies independent?

I’ve landed firmly in the “keep them separate” camp—but with a twist. Here’s why.

The Allure of the All-in-One Approach

Using a single über spell sounds great on paper. Here’s why some users go that route:

• Hands‑off automation. No need to rebalance manually or swap spells—just tweak the über spell’s internal logic, and you’re done.

• Instant aggregate backtests. You get a nice clean backtest of your entire portfolio’s historical performance, which can give you a reasonable benchmark (though, of course, past performance ≠ future results).

Why I’m Not Sold

Here’s where the über spell loses its magic for me:

• Flying blind on individual strategies. You lose the ability to see each strategy’s live behavior like I show in my portfolio updates. And trust me, you’ll miss it when you need to diagnose what’s working and what isn’t.

• Kiss advanced rebalancing goodbye. Want to use QuantMage’s Inverse Volatility weighting with that sweet Low Correlation Boost? Not happening with an über spell.

• Shorter backtests. Your historical data gets chopped down to match your newest asset. If one strategy only has two years of history, that’s all you get for the whole portfolio.

• No correlation insights. Those handy correlation matrices that show whether you’re actually diversified as you can see in this post? Not available. I find this matrix critical to avoid hidden risks.

• One-size-fits-all trading. Different strategies often need different rhythms. Maybe you want monthly rebalancing for one strategy and threshold trading for another. With an über spell, everyone marches to the same beat.

For me, these limitations are deal-breakers. The flexibility and insight I get from separate strategies far outweigh the convenience of bundling.

My Hybrid Solution

Despite all that, I still keep a über spell in my toolkit—but I treat it like a simulation sandbox, not the main event. Here’s how I use it:

• Sanity‑check the portfolio’s simulated performance in one place.

• Test different weightings and see how they move the needle historically.

• Document the weights I used during the last rebalance (so I don’t forget).

As you can see, the history is short. Also, my live portfolio will drift from this simulation because different spells run on different cadences and weights naturally wander between rebalances. Plus, every time I tweak a strategy or rebalance, I need to update the über spell to keep things synchronized. It’s maintenance, but worth it for the insights.

The Bottom Line

There’s no universal right answer here. The best approach depends on your trading style, how much control you want, and whether you value convenience over granularity.

My approach—separate strategies with an über spell shadow—might seem like extra work, but it gives me both detailed control and big-picture perspective.

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What’s your take? Are you team über spell, team portfolio, or somewhere in between like me?

Drop a comment below—I’d love to hear how you manage your own systematic portfolio.

TL;DR

I’m testing a live “side-quest” strategy meant to zig when my core portfolio zags: a diversified basket of alt asset/strategy ETFs selected by 180‑day momentum (hold the top 3). A conditional short‑vol overlay (via SVOL) kicks in only when HY credit spreads are elevated. Backtests look exciting—but history is short, risks are real, and I’m keeping conviction appropriately modest.

I’ve long hunted for a spell that’s uncorrelated to the rest of my book yet still trends up and to the right. The idea: lean on alternative asset/strategy ETFs that dance to different drummers than broad equity beta.

The Core Strategy: Alternative Asset Momentum

I use six ETFs:

• PDBC — broad commodities

• KMLM — managed futures

• KRBN — carbon credits

• BTAL — market‑neutral, anti‑beta tilt

• HNW — catastrophe bond proxy

• GLDM — gold

Quick note on HNW—it’s not a pure catastrophe bond play (there wasn’t one available when I started this while one is available now), but it does allocate about 20% to insurance-linked securities alongside other high-income bonds. Close enough for our purposes.

The strategy is beautifully simple: I measure each fund’s momentum using 180-day Trend Clarity, then invest in the top three performers. Here’s how it’s been playing out:

Now, I’ll be the first to admit that four years isn’t exactly a lengthy track record. But the numbers are hard to ignore: a 22.8% CAGR with just a 9.7% max drawdown. Even better? Check out that low correlation to SPY:

That’s exactly what we’re after—returns that don’t depend on whether the S&P is having a good day.

Taking It Up a Notch: The Short Volatility Overlay

Here’s where things get interesting (and yes, a bit spicier).

Through some experimentation, I discovered that layering in a mean-reversion play using short volatility can seriously boost performance. The trigger? When high-yield credit spreads climb above a certain threshold, it often signals a volatility spike that’s ripe for harvesting. I use SVOL for this overlay:

The results speak for themselves: CAGR jumps to 36.4% while max drawdown barely budges at 8.8%. That impressive bounce around Liberation Day? Probably some timing luck, if we’re being honest. The trade-off is a bump in correlation with the broader market:

Let me be crystal clear: this short vol overlay isn’t for the faint of heart. Want proof? Here’s what happens when you isolate just the overlay component and use the longer-history SVXY instead of SVOL:

See those stomach-churning drops over a 13-year period? That’s why I opted for SVOL, which is essentially a deleveraged version of short volatility exposure.

I also need to flag something: my credit spread threshold of 4.5 is somewhat arbitrary. It works now, but regime changes could easily break this relationship. Markets have a funny way of evolving just when you think you’ve figured them out.

Give the spell a try here.

🚨 The Necessary Fine Print: This is me sharing what I’m doing, not telling you what to do. Past performance is about as reliable as a weather forecast beyond next week. This strategy could evolve, break, or get completely overhauled at any moment. Please do your homework before putting real money at risk.

Final Thoughts

I’ll level with you—my conviction on this strategy is moderate at best. With only four years of history, we’re walking a tightrope between genuine edge and statistical noise. Overfitting and recency bias are real concerns that keep me up at night.

But that’s part of what makes this interesting, right? We’re exploring less-traveled paths in search of uncorrelated returns. Sometimes that means accepting more uncertainty in exchange for potential diversification benefits.

So what’s your take? Have you found any alternative assets or strategies that successfully break free from market beta? I’m always curious to hear what others are experimenting with in this space.

🤦🏻‍♂️ me a line with your thoughts—especially if you think I’m missing something obvious or heading for a cliff I haven’t spotted yet.

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Update 9/26/2025: Apparently HNW is being delisted today: https://pioneerinvestments.com/content/dam/pioneer/en/documents/closed-end-fund/notices/CEF-Press-Release-09-25-2025.pdf. I removed it from the spell. What a timing! 🤦🏻‍♂️

With September’s rate cut practically carved in stone and the economy still showing surprising resilience (despite mounting noise from the usual macro skeptics), we’re watching the market casually set new all-time highs like it’s no big deal. Times like these, I find myself returning to research pieces like this one to stay anchored. It’s easy to get swept up in the euphoria when everything’s green.

As for portfolio moves? I’m sitting on my hands for now. Sure, I’ve got a few spells that are testing my patience, but I’m fighting the urge to tinker. System beats sentiment, and the rules say hold.

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I’ll level with you – the writing well has been a bit dry lately. Haven’t posted anything new since the last update, not for lack of looking. I didn’t have an idea that cleared the usefulness bar, and I’d rather ship signal than filler. If something truly interesting surfaces – or my motivation perks up – you’ll see it here first.

Until then, all-time highs will have to do. 😄 Let’s enjoy the ride while staying mindful that trees don’t grow to the sky.

The market seems pretty relaxed about the lingering tariff overhang, pricing in a rate cut in September like it’s already signed and sealed. We’ll see.

No major moves on my end this time. Some of my anti-beta spells aren’t exactly shining, but I suspect they’re quietly doing their job in this kind of environment. I’m resisting the urge to tinker just for the sake of short-term performance. Discipline > dopamine.

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Here’s hoping your portfolios are capturing more of this rally than mine! Whether you’re fully invested or keeping some powder dry, these are interesting times to be in the market.

P.S. If you’re battling a bit of FOMO, Howard Marks’ latest memo is a great antidote.

Ever notice how most investment strategies are like that friend who’s always “on”—constantly pushing for growth until something goes wrong? Well, I recently stumbled upon a fascinating paper (and a related post) that completely flips this approach on its head.

The strategy? It’s called “Defense First,” and instead of charging ahead with risk-on assets until they stumble, it does something refreshingly counterintuitive: it stays defensive by default and only ventures into equities when the safe havens start looking shaky.

Here’s the defensive dream team the strategy employs:

1. TLT - Long-Term Treasuries (your classic safety net)

2. GLD - Gold (the timeless hedge)

3. DBC - Commodities (for when things get real)

4. UUP - U.S. Dollar Index (cash is still king, sometimes)

Each gets evaluated using dual momentum (relative + absolute), and if their mojo fades, the portfolio shifts into SPY—not because it’s exciting, but because it’s the “least weak alternative.”

Here’s how it works in the original paper:

Because no research rabbit hole goes un‑explored around here, I wired the whole thing into QuantMage—but with one twist: I used threshold rebalancing instead of a simple calendar‑month flip.

Implementing Dual Momentum in QuantMage

To mirror the dual momentum approach, I started with a Filtered incantation using 13612U Momentum as the ranking indicator with weights from the original paper.

To handle the absolute momentum (i.e., when to stick with a defensive asset vs. rotate into SPY), I leaned on price ratio momentums with short-term T-Bills:

The results? A respectable 9.5% CAGR with a 12.7% max drawdown over 16 years. Not too shabby, and pretty much in line with what the paper found.

Note: Unlike the paper’s approach, which ranked each raw asset, ranking here is done for each absolute-momentum subspell under the Filtered as a whole.

Enter: Indicator Percentile

QuantMage recently rolled out a slick new toy: Indicator Percentile. It lets you ask, “How strong is this indicator vs. its recent history?”—a natural fit for momentum systems.

So instead of checking if the price ratio’s momentum is just above zero (like with SMA12), I asked: Is the 10-day moving average of the price ratio in the top half of its last 100 days?

If yes, we keep the asset. If not, we rotate to SPY:

Boom! We’re looking at 12.2% CAGR with a 13.3% max drawdown. That’s a meaningful jump just from tweaking our momentum signals.

⚠️ Heads‑Up: None of this is investment advice. It’s a sandbox for strategy nerds. Do your own homework (and maybe call your favorite fiduciary) before pulling any triggers.

Inverse Volatility Weighting

Next, I wondered what would happen if we ditched the relative momentum component entirely and went with inverse volatility weighting instead. Think of it as giving more weight to the calm kids in class:

At first glance, the 11.4% CAGR and 18.2% max drawdown looked like a step backward. But wait for it...

Turning Up the Heat

Replacing SPY with SPXL (3× leveraged S&P 500) turned the 🚴‍♂️ ride into a 🏎️ joy‑ride:

We’re talking 25.6% CAGR with a 26.0% max drawdown! The Sharpe Ratio hits 1.23 with 20.2% annualized volatility. And here’s the plot twist—this leveraged version actually performs better with inverse volatility weighting than with the original relative momentum filter.

Want to take this bad boy for a spin? 👉 Check it out here.

So, What’s the Verdict?

My take on “Defense First” may not be a pixel-perfect replica of the paper, but it captures the key concepts—dual momentum, defensive default posture, dynamic SPY fallback—and delivers comparable or even stronger performance.

Does staying risk-off until proven otherwise appeal to your investing instincts? How do you feel about percentile-based signals vs. traditional momentum? Got a tweak or variation of your own?

Drop a comment—I’d love to hear how you’d enchant this spell differently.

The market’s strength has been surprisingly persistent. Geopolitical noise? Macro headwinds? The market doesn’t seem to care. Both of my main canary signals are flashing green, and a few others I monitor are also in clear bullish territory. For now, risk-on is the name of the game.

I also gave COIN or Not a much-needed revamp. It had been lagging, and I debated sunsetting it entirely. In the end, I opted for a major simplification—stripping it back to the core thesis. Let’s see if leaner performs better.

As for the rest of the portfolio—no major changes. Sometimes, not tinkering is the best move.

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And as for this bull train? Something will derail it eventually. But the fiscal train? That one barrels on, seemingly unstoppable. Something to chew on as you think about risk—and what kind of tracks we’re all riding.

Stay nimble out there.

Lately I stumbled across a post (somewhere in the internet rabbit hole—credit to the mystery author!) that compared two ETFs I only half‑remembered: SPHB, which holds the 100 highest‑beta names in the S&P 500, and SPLV, which holds the 100 lowest‑volatility names. The idea felt obvious in hindsight: when the raciest stocks are outrunning the sleepiest ones, the market is probably feeling frisky. Why not track the price ratio of the two as a canary?

📣 Usual disclaimer: None of this is financial advice. Kick the tires yourself and talk to a pro before committing real money.

The Basic Momentum Play

I dropped the ratio into QuantMage, slapped a simple 12‑month SMA‑momentum rule on it, and—voilà:

Not too shabby! We’re looking at a 9.9% annual return with just an 11% max drawdown over 13 years – and the strategy was only in the market 54.5% of the time. Getting a Sharpe Ratio above 1.0 with a single binary signal? That’s like finding a $20 bill in your old jacket pocket.

The logic here is beautifully simple: when the wild children (high beta) are outperforming the steady eddies (low vol), it’s party time in the market.

Making It More Reliable

Adding a simple twist—requiring the momentum signal to persist for two consecutive days—significantly smoothed the ride:

Better risk-adjusted return, fewer whipsaws. Sometimes, patience pays.

Time Travel with Correlated Tickers

Unfortunately, SPHB and SPLV don’t go far enough back to cover the 2008 GFC. So I used QuantMage’s Correlated Tickers tool to find proxies:

With these substitutes, I could stretch the backtest to 17 years:

The results weren’t quite as sparkly, but still solid enough to keep me interested. More importantly, it gave me confidence that this wasn’t just a post-2010 phenomenon.

Building a Risk‑Off Sleeve: Gold

Routing capital to IAU (gold) when the signal is risk‑off nudged returns higher—at the cost of a bit more bumpiness:

Turning Up the Heat

Want to live dangerously? Replace SPY with QQQ (the tech-heavy Nasdaq ETF):

Still not spicy enough? Try QLD, which is basically QQQ on steroids (2x levered):

Now we’re cooking with gas – 33.1% annual returns with a 24.5% max drawdown over 13 years. Of course, betting on continued tech dominance might be a bit like assuming your favorite sports team will keep winning forever. But hey, it’s been working so far!

Give it a spin yourself here.

The Power Combo

Pair the canary with my recently shared RSI Ensemble (a mean‑reversion filter) and the curve really takes off:

CAGR 59.5 % against 22.2 % drawdown—proof that uncorrelated edges can be rocket fuel.

Try the combo strategy here.

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Bottom Line

• Core thesis: When high‑beta leads low‑vol, the crowd is reaching for risk. The ratio is a neat single‑line risk‑on/off toggle.

• Durability: Will it keep its edge? Only the future knows. Longer, out‑of‑sample regimes (think 2000–02 or 1970s style stagflation) would be instructive.

• Overfit watch: The fancier combos (QQQ, QLD, RSI mash‑ups) pack extra assumptions. Treat them as hypotheses, not gospel.

• Your turn: What uncorrelated filters or asset sleeves would you bolt on—commodities, managed futures, something weird? How would you tame that drawdown without killing the juice?