All weather portfolio dashboard excel - if you came here looking for a clean, Ray Dalio style allocation tracker that lives in Excel and updates with live data, this guide gives you both the build instructions and a ready-to-download workbook. The All Weather portfolio is one of the most talked about asset allocation frameworks of the last two decades, and the heart of its appeal is simple: it tries to be calm in every kind of weather instead of brilliant in one and brutal in another. In June 2026, with the Federal Reserve in pause mode, gold near record highs, and the long end of the curve still digesting the rate cycle, a lot of advisors and self-directed investors are reaching for a template that lets them stress-test their mix without rebuilding a spreadsheet from scratch.
This article walks through the design of an All Weather portfolio dashboard you can use in Excel, the exact MarketXLS formulas that pull the numbers, how to think about drift and rebalancing, and a regime scenario engine you can drop into your own workbook. Two downloadable templates are linked at the end: a static sample with current values and a live-formula version that recalculates as markets move.
All Weather Portfolio: The Allocation in One Table
Before diving into the spreadsheet, here is the classic All Weather mix as Ray Dalio and the team at Bridgewater have publicly described it. The exact weights vary slightly across sources, but the version below is the canonical "all weather" public allocation used in most educational materials and is what the dashboard template implements.
| Sleeve | ETF Proxy | Target Weight | Role in the Portfolio |
|---|---|---|---|
| US Total Stock Market | VTI | 30% | Growth driver in rising-growth regimes |
| Long-Term US Treasuries | TLT | 40% | Hedge against falling growth and falling inflation |
| Intermediate US Treasuries | IEF | 15% | Smoother duration ballast |
| Gold | GLD | 7.5% | Inflation and currency debasement hedge |
| Broad Commodities | DBC | 7.5% | Real-asset hedge for rising inflation |
The first thing newcomers usually notice is the bond weight. A 55% allocation to Treasuries (TLT plus IEF) looks aggressive next to a traditional 60/40, but the All Weather philosophy is not "more bonds." It is "balance risk contribution, not dollar contribution." Stocks are roughly three times more volatile than long Treasuries, so to make bonds and stocks contribute equally to portfolio risk, the bond dollar weight has to be larger.
Why an All Weather Dashboard Belongs in Excel
A lot of investors implement All Weather inside a robo-advisor or a static index portfolio, and that is a perfectly fine choice. The case for putting it in a spreadsheet, especially one wired to live market data, comes down to four things.
- Drift is real, and drift is expensive. Equity rallies and bond drawdowns can push a portfolio meaningfully off-target between annual rebalance dates. A dashboard makes drift visible the moment it crosses a band.
- Customization matters. The classic weights are a starting point. Some advisors prefer a 35% equity tilt. Some replace DBC with PDBC or use IAU instead of GLD. Excel makes that a one-cell edit.
- Scenario thinking gets sharper with a model. Clients respond to a side-by-side regime table far better than to a narrative about "rising growth and rising inflation."
- Tax-aware rebalancing benefits from triggers. A threshold-based rebalance, which only trades when a sleeve drifts past a band, is easier to discipline yourself to follow when the spreadsheet flashes the action.
The Six-Sheet Structure of the Dashboard
The downloadable template is organized into six sheets, each tackling a specific job.
Sheet 1: How To Use
A short tutorial that explains every input, every output, and which formulas you would change if you wanted to swap ETFs or adjust weights. New users start here.
Sheet 2: Main Dashboard
This is the headline view. You enter your portfolio size in a yellow input cell and the workbook calculates the dollar allocation for every sleeve, fetches live prices, and reports the blended portfolio yield. A health-score block flags weight errors, sleeve concentration, and inflation-hedge thinness.
Sheet 3: Scenario Analysis
A regime sensitivity table that shows how each sleeve has historically behaved across the four growth and inflation regimes that Dalio frequently references. A blended portfolio row uses SUMPRODUCT to combine the per-sleeve sensitivities with your target weights, so you can see at a glance how your portfolio would be expected to behave if, say, inflation re-accelerates.
Sheet 4: Rebalancing
A drift monitor and rebalance simulator. You enter current dollar holdings, and the sheet computes drift versus target, flags any sleeve that has crossed the rebalance band, and tells you the exact dollar trade required to bring everything back to target. A reference table compares calendar-based, threshold-based, and hybrid rebalance methods.
Sheet 5: Asset Performance
A per-sleeve performance summary: live price, YTD return, three-month return, one-year return, 30-day realized volatility, 50-day SMA, 200-day SMA, and RSI. A secondary "Momentum Signals" block synthesizes these into a STRONG, MIXED, or WEAK label per sleeve.
Sheet 6: Correlation Matrix
A 5x5 matrix of pairwise correlations across the sleeves, color-coded so true diversifiers (correlations near zero or negative) stand out from sleeves that overlap. An average pair-correlation column shows which sleeves are doing the most diversification work.
Live MarketXLS Formulas in the Dashboard
The template uses verified MarketXLS functions for prices, yields, returns, volatility, moving averages, and RSI. Every formula below has been confirmed in the MarketXLS function library. None of these are invented.
Live Price and Yield
=QM_Last("VTI")
=DividendYield("VTI")
QM_Last pulls the latest trade price for an equity or ETF. DividendYield returns the trailing dividend yield as a percentage value. In the template, the yield cell divides the result by 100 so the cell formats correctly as a decimal percentage.
Returns
=StockReturnYTD("VTI")
=StockReturnOneYear("VTI")
=StockReturnThreeMonths("VTI")
These functions return total returns including dividends. The output is in percentage units, so the template divides by 100 to get a decimal that displays as +5.2%, -3.1%, and so on.
Volatility and Moving Averages
=StockVolatilityThirtyDays("VTI")
=SimpleMovingAverage("VTI", 50)
=SimpleMovingAverage("VTI", 200)
=RSI("VTI")
StockVolatilityThirtyDays returns annualized 30-day realized volatility. The two SMA calls return moving averages with the period you pass as the second argument. RSI returns the 14-period Relative Strength Index by default.
Blended Portfolio Yield
=SUMPRODUCT(D5:D9, F5:F9)
Where D5:D9 is the column of target weights and F5:F9 is the column of sleeve dividend yields. This produces the portfolio's overall trailing yield in a single cell.
Regime Sensitivity Roll-up
=SUMPRODUCT($C$5:$C$9, D5:D9)
Used on the Scenario Analysis sheet. C5:C9 holds target weights, D5:D9 holds the regime sensitivity for each sleeve. The output is the blended portfolio's expected behavior in that regime.
Drift and Rebalance Trade
=E5-C5 // drift (current % minus target %)
=IF(ABS(F5)>='Main Dashboard'!$C$6/100,"REBALANCE","HOLD")
=(C5-E5)*SUM($D$5:$D$9) // dollar trade to return to target
=IF(H5>0,"BUY","SELL") // direction
The rebalance threshold is set as a single input on the Main Dashboard, so changing it from 5% to 3% updates the trigger logic for every sleeve at once.
The Four Economic Regimes, Translated Into Excel
The intellectual core of the All Weather framework is that at any given time, growth and inflation are either above or below expectations. That creates four regimes, and Dalio's argument is that you should hold roughly equal risk in assets that win in each.
| Regime | Stocks (VTI) | Long Bonds (TLT) | Gold (GLD) | Commodities (DBC) |
|---|---|---|---|---|
| Rising Growth | Strong | Weak | Mixed | Strong |
| Falling Growth | Weak | Strong | Mixed | Weak |
| Rising Inflation | Weak | Weak | Strong | Strong |
| Falling Inflation | Strong | Strong | Mixed | Weak |
In the dashboard, each cell is populated with an illustrative historical sensitivity (annualized percentage point response), and the blended row uses SUMPRODUCT to produce a portfolio-level number for each column. This is not a forecast. It is a structural map showing why the mix is balanced.
A simple read of this table: if you walk into any of the four regimes, at least one of your sleeves is positioned to do well. In none of them does the entire portfolio collapse. That is the whole pitch of All Weather: lower drawdowns, smoother ride, fewer panic moments. The cost is that in any single regime, you give up some of the upside you would have had with a concentrated bet.
June 2026 Context: Why This Template Is Useful Right Now
Three things make the current moment a reasonable time for advisors and self-directed investors to revisit a balanced allocation framework.
The Fed has paused, but the next move is uncertain. With the federal funds rate sitting in restrictive territory and inflation cooling but still above the 2% target, the path of rate cuts is being repriced almost monthly. Long-duration Treasuries (the TLT sleeve) become more attractive as the market starts to price actual cuts, but the same sleeve has been a graveyard for capital across most of the last three years. A balanced approach accepts both realities.
Gold is near record highs. GLD has had a remarkable run as central banks (especially emerging-market central banks) have added to gold reserves, and as the long-running narrative around currency debasement has continued. The All Weather framework holds gold not because it predicts the next move, but because gold tends to do its work when paper assets struggle.
Equity concentration risk is at multi-decade highs. The top 10 names in the S&P 500 represent an outsized share of the index by market cap. Allocations with heavy single-factor concentration are more exposed to a regime shift than they have been in many years. A genuinely diversified mix, with bonds, gold, and commodities as ballast, becomes a useful counterweight.
None of this is a prediction. It is the context in which a real allocation framework, with drift monitoring and rebalance discipline, is more valuable than a static one.
Building the Dashboard Step by Step
If you want to build a version of this yourself instead of using the download, here is the order to do it in.
Step 1: Set Up the Input Block
On the Main Dashboard sheet, create two yellow input cells:
- Portfolio Size (USD)
- Rebalance Threshold (percentage points)
Lock the rest of the sheet, or at least bold-border the input cells, so future-you remembers what to edit.
Step 2: Build the Allocation Table
Create one row per sleeve with the columns: Ticker, Asset Class, Target %, Price, Yield, Dollar Allocation, Shares, Role. Use =QM_Last(ticker) for price and =DividendYield(ticker)/100 for yield. The dollar allocation is just Portfolio Size times Target %. Shares is dollar allocation divided by price.
Step 3: Add a Health-Score Block
A small set of IF statements that flag common mistakes:
- Sum of target weights equals 100%
- No single sleeve exceeds 60%
- Inflation hedge weight (GLD plus DBC) is at least 10%
These should produce PASS, WARNING, or FAIL labels that you can see at a glance.
Step 4: Build the Scenario Table
Five rows of sleeves by four columns of regimes. Populate the cells with the illustrative historical sensitivities (or your own assumptions). Add a blended portfolio row using SUMPRODUCT.
Step 5: Wire the Rebalancing Sheet
Each sleeve needs: target %, current dollar value, current %, drift, status, trade dollar, action. The trigger compares the absolute drift to the input threshold on the Main Dashboard. The trade dollar is (target % minus current %) times the current portfolio sum.
Step 6: Performance and Correlation Sheets
The performance sheet just pulls live values with the MarketXLS functions listed above. The correlation matrix can be a static block of recent correlations, or you can compute it in real time using QM_GetHistory plus the standard Excel CORREL function across the resulting price arrays.
Customization Ideas
The downloadable template uses the canonical weights, but the design supports several variations advisors often ask about.
- Tilt to equities. Move VTI to 35% and reduce TLT to 35%. The dashboard will recompute everything from the new weights.
- Substitute international equity. Replace VTI with VT or split 20% VTI plus 10% VXUS. Just change the ticker strings; the formulas resolve automatically.
- Add TIPS. Insert a sixth sleeve (e.g. SCHP at 10%) and reduce TLT or IEF by the same amount. TIPS add a direct inflation-linked sleeve that some advisors prefer over commodities.
- Use a target-volatility overlay. Multiply all weights by a scalar that you adjust based on portfolio realized volatility. This is closer to a true risk-parity implementation.
Rebalancing Discipline
The most underrated part of any allocation framework is the rule that tells you when to trade. The Rebalancing sheet in the template includes a reference table that compares four common approaches.
| Method | Trigger | Pros | Cons |
|---|---|---|---|
| Annual calendar | Fixed date once a year | Simple, predictable | Misses fast drift between dates |
| Quarterly calendar | Every 3 months | Catches drift earlier | More trades, more taxes in taxable accounts |
| Threshold band | Drift past a chosen band | Trades only when needed | Requires monitoring |
| Hybrid | Check quarterly, trade if past band | Discipline plus efficiency | Slightly more to track |
Most academic work supports threshold-band rebalancing for tax-deferred accounts, because the act of trimming winners and adding to losers has a small but persistent rebalance premium. In taxable accounts the calculus shifts because every sell creates a tax event, and many investors prefer annual rebalancing or doing it through new contributions instead of sales.
Download the Templates
Download the templates:
- - Pre-filled with current data so you can see the structure immediately
- - Live-updating formulas (requires MarketXLS in Excel)
Both workbooks have the same six sheets and the same layout, so you can use the static version as a reference while you customize the live one.
Frequently Asked Questions
What is the All Weather portfolio?
The All Weather portfolio is an asset allocation strategy publicly described by Ray Dalio and Bridgewater Associates. It allocates roughly 30% to stocks, 40% to long-term Treasuries, 15% to intermediate-term Treasuries, 7.5% to gold, and 7.5% to broad commodities. The design goal is balance across four economic regimes (rising growth, falling growth, rising inflation, falling inflation) so that no single regime produces an outsized drawdown.
How is the All Weather portfolio different from the standard 60/40?
A 60/40 portfolio holds 60% stocks and 40% bonds, and its outcomes are dominated by what happens in the stock sleeve. The All Weather framework holds less equity in dollar terms but balances risk across more asset classes (including gold and commodities), with the idea that the bond, gold, and commodity sleeves should be designed to do well when stocks are not. The result is typically a lower expected return in strong equity bull markets but smaller drawdowns in regime shifts.
Why does the All Weather portfolio hold so many bonds?
Stocks are roughly three times more volatile than long-term Treasuries, so to make stocks and bonds contribute equally to portfolio risk, the bond dollar allocation has to be larger. The 40% TLT plus 15% IEF (55% total bonds) figure reflects this risk-parity logic, not a directional view on interest rates.
How often should I rebalance an All Weather portfolio?
There is no single right answer. Annual rebalancing is the simplest and most common approach. Threshold-band rebalancing (trading whenever any sleeve drifts more than 3 to 5 percentage points from target) tends to capture more of the academic rebalance premium but requires monitoring. The Rebalancing sheet in the template supports both views.
Can I build the All Weather portfolio without MarketXLS?
You can build the structure in plain Excel using static values, but a live dashboard with current prices, yields, returns, volatility, and moving averages is much easier with a data feed plugged in. MarketXLS is the data and function layer the template uses; you can also adapt the layout to other feeds you may already have, although the formula names would change.
Are the regime sensitivities in the Scenario Analysis sheet predictions?
No. They are illustrative historical sensitivities used to show the structural logic of the All Weather mix. The point of the table is to demonstrate that at least one sleeve is positioned to do well in each of the four regimes, not to predict the future return of any single asset.
Is the All Weather portfolio suitable for everyone?
The All Weather framework is a general portfolio design idea, not personalized advice. The right mix for any individual depends on income needs, tax situation, time horizon, and risk tolerance. The dashboard is a tool for analyzing and tracking an allocation, not a recommendation that a specific person should adopt it.
The Bottom Line
The All Weather portfolio is one of the cleanest, most-discussed examples of regime-balanced allocation in modern portfolio practice. Built well in Excel and wired to live MarketXLS data, it becomes a working tool: drift gets monitored, rebalance triggers fire automatically, and the scenario engine forces honest thinking about how each sleeve is supposed to behave when conditions change. The downloadable templates above are a complete starting point. Customize the weights, swap the ETFs, add a sleeve if you want a tilt - the structure scales with you.
To explore the full MarketXLS function library, visit marketxls.com or book a demo to see how the live formulas can be wired into your existing portfolio workbooks. For related portfolio building tools, see our companion templates on risk parity construction, mid-year rebalancing, and the capital allocation scorecard.
Educational content only. Not investment advice. The All Weather framework and the regime sensitivities discussed are illustrative concepts used to explain a portfolio construction approach.
