All-Weather (Dalio)
Ray Dalio's All-Weather portfolio balances risk across economic regimes: growth, recession, inflation, and deflation. It holds equities, long-term bonds, inflation-linked bonds, gold, and commodities.
Allocation
What if you add Bitcoin?
Adding Bitcoin changes the risk-return profile. Here is how different allocations compare, reducing other positions proportionally:
| Portfolio | Return | Volatility | Sharpe |
|---|---|---|---|
| Base (All-Weather (Dalio)) | 5.06% | 7.41% | 0.26 |
| With 5% Bitcoin | 5.56% | 7.92% | 0.31 |
| With 10% Bitcoin | 6.06% | 8.89% | 0.33 |
Returns are geometric (compound). Sharpe ratio uses 3.10% risk-free rate (US Cash, JPM LTCMA 2026). Forward-looking estimates, not predictions.
The idea: balance risk, not dollars
Ray Dalio's All-Weather portfolio starts from a different question than most allocations. Instead of asking how much to hold in each asset, it asks how much risk each asset contributes. Because stocks are far more volatile than bonds, a conventional 60/40 is really a 90/10 portfolio in risk terms — equities dominate the outcome. All-Weather deliberately rebalances that, leaning on government bonds, inflation-linked bonds, gold, and commodities to spread risk more evenly.
The framework maps assets to four economic environments: rising growth, falling growth, rising inflation, and falling inflation. Something in the portfolio is built to do well in each. The version here uses 30% global equities, 30% government and inflation-linked bonds, 15% in gold and commodities, and the balance in cash.
What to expect from All-Weather in 2026
All-Weather trades return for stability. On J.P. Morgan's 2026 assumptions its expected return sits below an equity-heavy portfolio, but so does its volatility — the numbers above show the trade. The point was never to win in any single year; it's to avoid the deep drawdowns that force investors to sell at the worst possible time.
Its weak spot is a sustained equity bull market: with only 30% in stocks, All-Weather will visibly lag a 60/40 or all-equity portfolio when stocks run. That underperformance is the premium you pay for the smoother ride — and it's the most common reason investors abandon the strategy right before they'd have needed it.
The 2022 stress test
2022 was a hard year for All-Weather. The strategy leans on long-duration bonds, and when inflation spiked and rates rose, those bonds fell alongside equities — the same stock-bond correlation break that hurt the 60/40. Gold and commodities cushioned the blow, but the year exposed the portfolio's sensitivity to rising rates.
The lesson isn't that risk parity failed; it's that no portfolio is truly all-weather when the shock is an inflation-driven rate spike that hits stocks and bonds together. It's also why the gold and commodity sleeves matter, and why some investors add a small Bitcoin position as another non-correlated diversifier — the variant table above shows the effect.
Who All-Weather is for
All-Weather suits an investor whose priority is sleeping soundly: someone near or in retirement, anyone with low tolerance for large drawdowns, or an investor who knows they're prone to panic-selling and wants a portfolio that rarely tests their nerve. It's also a reasonable core for people who simply don't want to forecast which regime is coming.
It's a poor fit for a young investor with decades to compound — the bond-heavy mix leaves too much long-run growth unclaimed — or for anyone who will measure it against the S&P 500 each year and feel the lag. Judged on its own terms of risk-adjusted return and drawdown control, it does its job; judged against a bull market in stocks, it will always look slow.
How these numbers are calculated
Expected returns and volatilities come from J.P. Morgan's 2026 Long-Term Capital Market Assumptions (30th edition). Portfolio risk is computed using the full 27x27 correlation matrix, not simple weighted averages. The Sharpe ratio uses 3.10% (US Cash) as the risk-free rate.
For full methodology details, see the methodology page.
Customize this portfolio
Adjust weights, add constraints, try different optimization methods.