Diversified alternatives focused portfolio with strong yield and modest drawdowns over a short history
Report created on May 25, 2026Risk profile
4/7
Diversification profile
5/5
Positions
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DB iMGP DBi Managed Futures Strategy ETF
DBMF - US53700T8273 60.00% -
HF Unlimited HFEQ Equity Long/Short ETF
HFEQ 20.00% -
AL SPDR Bridgewater All Weather ETF
ALLW - US78470P6300 10.00% -
HF Unlimited HFGM Global Macro ETF
HFGM 10.00%
This portfolio is built entirely from four alternative-style ETFs, with 60% in managed futures, 20% in equity long/short, and the rest split between global macro and an “all weather” multi-asset ETF. That means exposure is driven more by trading strategies than by simple stock or bond indexes. This structure is different from a classic stock‑and‑bond mix and is designed to behave differently from broad markets. Because the track record is only about 10 months, any patterns in how these strategies combine are still early and could change as markets go through different cycles.
Growth
Over roughly 10 months, $1,000 in this portfolio grew to about $1,280, implying a 33.2% compound annual growth rate (CAGR). CAGR is like average speed on a road trip — it smooths the ride into one yearly number. Over this short window, the portfolio beat both the US and global market benchmarks, while also showing a smaller maximum drawdown of about -6.8% versus roughly -9% for the benchmarks. Only 13 days drove 90% of returns, which is a reminder that a few strong days mattered a lot. With less than a year of data, these strong results should be viewed as provisional, not a long-term pattern.
Projection
The forward projection uses a Monte Carlo simulation, which basically takes the recent return and volatility patterns, scrambles them thousands of times, and builds a range of possible 15‑year outcomes. In these simulations, the median result turns $1,000 into about $2,236, with a wide range between weaker and stronger paths. The average simulated annual return is 5.82%, modestly above the assumed cash line. Because the underlying history is only about 10 months, the model is leaning heavily on a very short and unusually strong period. That makes these numbers more “what‑if illustrations” than robust forecasts of how this strategy might behave over many different market environments.
Asset classes
- Stocks 43%
- Bonds 43%
- Other 14%
By asset class, the portfolio splits roughly 43% stocks, 43% bonds, and 14% in “other” exposure, driven by the alternative strategies. On paper, that split looks similar to a balanced allocation, but the way those exposures are accessed is very different from holding plain index funds. The managed futures and global macro components can shift exposures over time based on their models, so today’s stock/bond breakdown may change. This structure can help diversification because returns are influenced by more than just equity markets. With less than a year of history, though, it’s too early to say how consistently this mix behaves across different interest rate or equity cycles.
Sectors
- Technology 9%
- Financials 7%
- Industrials 5%
- Consumer Discretionary 4%
- Health Care 4%
- Basic Materials 3%
- Telecommunications 3%
- Energy 2%
- Consumer Staples 2%
- Utilities 1%
- Real Estate 1%
This breakdown covers the equity portion of your portfolio only.
Sector data shows exposure spread across many areas, with technology at 9%, financials at 7%, and most other sectors in the low single digits. That indicates no obvious sector “bet” within the equity sleeve; it looks relatively balanced compared with broad market norms where a few sectors often dominate. This is a positive sign for diversification inside the stock portion. At the same time, the bulk of risk in this portfolio will likely come from the strategies themselves — managed futures, long/short, and macro — rather than sector swings. With such a short track record, it’s hard to judge how these sector weights interact with the strategy overlays in more stressed markets.
Regions
- North America 24%
- Europe Developed 7%
- Asia Emerging 5%
- Asia Developed 2%
- Japan 1%
This breakdown covers the equity portion of your portfolio only.
Geographically, about 24% of look‑through exposure is in North America, with additional slices in developed Europe, emerging Asia, developed Asia, and Japan. That’s a broadly global footprint relative to many portfolios that lean heavily on a single region. This global reach supports the diversification score of 5/5 and can help reduce reliance on any one economy or currency. However, these numbers reflect a snapshot and don’t fully capture the dynamic positioning of macro and managed futures strategies, which can tilt toward or away from different regions over time. Given only 10 months of data, it’s too early to tell how stable this geographic pattern really is.
Market capitalization
- No data 60%
- Mega-cap 14%
- Large-cap 12%
- Mid-cap 10%
- Small-cap 3%
- Micro-cap 2%
This breakdown covers the equity portion of your portfolio only.
Market cap data shows exposure across the spectrum: mega‑caps at 14%, large‑caps at 12%, mid‑caps at 10%, and smaller companies making up the rest. A large “no data” bucket (60%) is expected here because many positions come from derivatives or non‑traditional exposures inside the alternative funds. The visible part suggests some breadth across company sizes, which can spread risk between more established firms and smaller, potentially more volatile ones. That said, for this portfolio, overall behavior will likely be driven more by the long/short and macro strategies than by classic small‑cap versus large‑cap dynamics, especially over such a limited observation window.
True holdings
-
Part of fund(s):
- iMGP DBi Managed Futures Strategy ETF
-
FG First American Funds Inc. - Government Obligations Fund 7.99%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
- Unlimited HFGM Global Macro ETF
-
VT Vanguard Index Funds - Vanguard Value ETF 3.57%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
XL The Select Sector SPDR Trust - The Materials Select Sector SPDR Fund 2.63%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
XL The Select Sector SPDR Trust - The Industrial Select Sector SPDR Fund 1.47%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
XB SPDR Series Trust - SPDR S&P Biotech ETF 1.43%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
XR SPDR Series Trust - SPDR S&P Retail ETF 1.06%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
XL The Select Sector SPDR Trust - The Energy Select Sector SPDR Fund 0.76%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
XL The Select Sector SPDR Trust - The Utilities Select Sector SPDR Fund 0.75%
Part of fund(s):- Unlimited HFEQ Equity Long/Short ETF
-
MC iShares Trust - iShares MSCI China ETF 0.70%
Part of fund(s):- Unlimited HFGM Global Macro ETF
- Top 10 total 35.25%
This breakdown covers the equity portion of your portfolio only.
Looking through to the top holdings, coverage is about 55.5% of the portfolio, meaning nearly half of exposures sit outside the visible top‑10 lists. Within what can be seen, there is some concentration in cash‑like vehicles and sector ETFs such as value, materials, industrials, biotech, and retail. Because these appear via multiple strategy funds rather than as direct holdings, overlap can create hidden concentration — for example, if several ETFs lean into similar sectors or regions. The note that overlap is understated underscores this: the true concentration may be higher than it looks. With limited history, it’s hard to know how often these strategies cluster into similar positions at the same time.
Factors
Factor exposures are estimated using statistical models based on historical data and measure systematic (market-relative) tilts, not absolute portfolio characteristics. Results may vary depending on the analysis period, data availability, and currency of the underlying assets.
Factor exposure shows very high tilts to low volatility (100%) and yield (87%), along with a high tilt to momentum (75%), and a very low tilt to size (0%). Factors are like underlying “traits” — such as low volatility or high yield — that help explain why investments behave the way they do. A strong low‑volatility tilt aligns with the observed smaller drawdowns, suggesting the portfolio has favored steadier return patterns so far. The very high yield tilt fits with the elevated distribution yields reported by the ETFs. The strong momentum tilt suggests it has tended to lean into recent winners. Over only 10 months, though, these tilts might reflect short‑term positioning rather than a stable, long‑run profile.
Risk contribution
-
iMGP DBi Managed Futures Strategy ETF
Weight: 60.00% 52.7% -
Unlimited HFEQ Equity Long/Short ETF
Weight: 20.00% 25.7% -
Unlimited HFGM Global Macro ETF
Weight: 10.00% 15.8% -
SPDR Bridgewater All Weather ETF
Weight: 10.00% 5.8%
Risk contribution measures how much each holding drives the portfolio’s ups and downs, which can differ from simple weights. Here, the 60% managed futures ETF contributes about 53% of total risk, slightly less than its weight, while the 20% long/short and 10% global macro ETFs together contribute over 40% of risk. The all‑weather ETF is 10% by weight but only about 6% of risk, acting as a relatively stabilizing component. The top three holdings account for more than 94% of overall risk, meaning portfolio behavior is dominated by those strategies. This concentrated risk picture is important context when interpreting past returns, especially over such a short period.
Risk vs. return
This chart shows the Efficient Frontier, calculated using your current assets with different allocation combinations. It highlights the best balance between risk and return based on historical data. "Efficient" portfolios maximize returns for a given risk or minimize risk for a given return. Portfolios below the curve are less efficient. This is informational and not a recommendation to buy or sell any assets.
Click on the colored dots to explore allocations.
The risk‑versus‑return chart shows the current portfolio sitting on or very close to the efficient frontier, which is the set of portfolios that deliver the best expected return for each risk level using these same holdings. The optimal and minimum‑variance mixes have slightly lower risk and somewhat lower returns, but higher Sharpe ratios, meaning better risk‑adjusted performance in the model. Sharpe ratio is like “miles per gallon” for risk — how much return you get per unit of volatility. The current mix already looks efficient by this measure, which is a positive sign. Still, this optimization is built entirely on 10 months of data, so it mostly reflects how the portfolio behaved in one specific market phase.
Dividends
- iMGP DBi Managed Futures Strategy ETF 5.10%
- SPDR Bridgewater All Weather ETF 4.30%
- Unlimited HFEQ Equity Long/Short ETF 9.50%
- Unlimited HFGM Global Macro ETF 9.60%
- Weighted yield (per year) 6.35%
The portfolio’s overall yield is estimated at about 6.35%, with individual ETFs showing yields between roughly 4.3% and 9.6%. Yield here represents cash distributions as a percentage of price over the period; it can come from interest, dividends, or strategy‑specific payouts. That’s relatively high compared with broad equity indexes, and lines up with the strong yield factor tilt. High yield can be attractive for investors who value regular cash flow, but distributions can vary over time as strategies change and markets move. Because the history is only about 10 months, the current yield level may not represent a stable, long‑term income pattern.
Ongoing product costs
- iMGP DBi Managed Futures Strategy ETF 0.85%
- Weighted costs total (per year) 0.51%
Based on the available data, the portfolio’s total ongoing fee (TER) is around 0.51% per year, with the largest holding, the managed futures ETF, at 0.85%. TER, or Total Expense Ratio, is the annual cost charged by the funds to run the strategies. For a portfolio built entirely from actively managed and alternative ETFs, these costs are on the moderate side and notably lower than many hedge‑fund‑style products. Lower fees mean more of the portfolio’s gross return is kept. Over long periods, even a few tenths of a percent can add up, but with only 10 months of history, the full compounding impact — positive or negative — hasn’t really had time to show up yet.
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