All Weather — Canadian ETF Recreation

Data: Yahoo Finance adjusted close, fetched live on each visit How it's calculated →

Heads up — this is an experiment, not a recommendation. This page is an unofficial attempt to recreate the All Weather portfolio using Canadian-listed ETFs, built out of curiosity to see how close a CAD-listed version can get. Several sleeves are approximations — Canadian bonds stand in for US Treasuries, and the commodity sleeve is folded into gold — and none of this has been formally reviewed. Don't use it as the basis for an investment decision without doing your own proper due diligence.

What is this portfolio?

The All Weather portfolio is Ray Dalio's "all seasons" allocation, popularized in Tony Robbins' Money: Master the Game. Built by the founder of Bridgewater — one of the world's largest hedge funds — it's a simplified, retail take on the risk-parity idea: instead of loading up on stocks, spread risk so no single economic environment can sink the whole portfolio. Bonds carry a heavy weight precisely because they're calmer than stocks.

Spread risk, not dollars The signature move is heavy bonds: 40% long-term Treasuries plus 15% intermediate Treasuries. Bonds are far less volatile than stocks, so it takes more of them to balance the portfolio's risk. The dollar weights look bond-heavy on purpose.

Cover all four seasons Dalio frames markets by growth and inflation, each rising or falling. Stocks for rising growth, long bonds for falling growth and deflation, gold for rising inflation. Whatever the weather, something is positioned for it. (The original also holds commodities for inflation — folded into gold in this CAD version, see below.)

The canonical weights The original is 30% US stocks, 40% long Treasuries, 15% intermediate Treasuries, 7.5% gold, 7.5% commodities. This CAD recreation keeps the 30 / 40 / 15 frame but folds gold + commodities into one 15% gold sleeve and swaps US Treasuries for Canadian bonds. Still a defensive, low-volatility profile — built for a smooth ride, not the highest return.

Rebalance and stay put Risk parity only holds if you rebalance back to target when sleeves drift. Leave it alone and stocks slowly take over, quietly eroding the "all weather" balance the design depends on.

Risk notice: All Weather trades upside for smoothness — with 55% in bonds it tends to lag a stock-heavy portfolio badly in bull markets, and 2022 was brutal: long bonds and stocks fell together for one of its worst years on record. The design assumes bonds zig when stocks zag, which usually holds but breaks down in inflation shocks. This CAD recreation also drops the commodity sleeve (folded into gold) and uses Canadian rather than US Treasuries, so it's only a rough echo of the original. This is educational — see the disclaimer at the bottom of the page.

The four sleeves (Canadian-listed)

Four CAD-listed ETFs, so every figure is in Canadian dollars. The original's two inflation sleeves (gold + commodities) are folded into one 15% gold position, since no decent CAD broad-commodity ETF exists. Confirm current MERs on the provider pages (linked in Sources).

Sleeve	Weight	Ticker	~MER	Role · note
US large-cap (S&P 500)	30%	VFV.TO	~0.09%	Rising growth — unhedged
Long federal bonds	40%	ZFL.TO	~0.20%	Falling growth / deflation — Cdn govt
Aggregate bonds	15%	ZAG.TO	~0.09%	Rate ballast — Cdn aggregate, not US Treasuries
Gold (hedged)	15%	CGL.TO	~0.55%	Inflation — absorbs the original's commodity sleeve

Three compromises: total US market becomes the S&P 500 (VFV); US Treasuries become Canadian government and aggregate bonds (ZFL, ZAG); and the 7.5% commodity sleeve is folded into gold (no CAD broad-commodity ETF with history). A rough echo of the idea, not a copy.

A note on currency. Every sleeve is CAD-listed, so all figures are in Canadian dollars. The bonds (ZFL, ZAG) are Canadian; gold (CGL) is CAD-hedged; only VFV (US stocks) is unhedged and carries CAD/USD swings. Prices are dividend-adjusted; the backtest models no taxes. Educational only — not advice.

Sources

All Weather framework + allocation. Bridgewater: The All Weather Story (Ray Dalio); retail weights popularized in Tony Robbins, Money: Master the Game (2014).
Canadian-listed substitutes (this recreation). VFV (S&P 500), ZFL (long federal bond), ZAG (aggregate bond), CGL (gold, CAD-hedged) — with the original's commodity sleeve folded into gold. Approximations, not replicas.
ETF MERs. Provider fund pages: Vanguard Canada (VFV), BMO ETFs (ZFL, ZAG), iShares Canada (CGL). Confirm the current MER on the fund page before relying on it.

Figures accessed 2026-05-28. Tax rules change; verify against canada.ca and the fund prospectus before acting.

Portfolio configuration

Saved presets

Portfolio assets — up to 10. Leave unused slots empty.

Asset 1

Asset 2

Asset 3

Asset 4

Asset 5

Asset 6

Asset 7

Asset 8

Asset 9

Asset 10

Filled weights must sum to 100%. Current total: 100% (4 assets)

Backtest mechanics

Starting capital ($)

Start date

Rebalance frequency

Rebalance tolerance (%)

Commission + slippage (%)

Risk-free rate (%, annual)

Benchmark 1 (buy & hold)

Benchmark 2 (buy & hold)

Cash flow — contributions & withdrawals

Amount per period ($)

Direction

Frequency

Cash goes into

VFV.TOVanguard S&P 500 (CAD) · ZFL.TOBMO Long Federal Bond · ZAG.TOBMO Aggregate Bond · CGL.TOiShares Gold Bullion (hedged) Data through: —

Return

Risk

Quick reality check before you get excited. This is a learning tool, not financial advice. Every number on this page came straight from the inputs you set above — change the start date or growth target and the whole picture shifts. Play with the dials, see what moves, but don't mistake a backtest for a guarantee.

Heads up for Canadian readers. All returns are in Canadian dollars — every sleeve is CAD-listed. Remember this is a curiosity-only recreation built with approximated funds (see the experiment note at the top): the long-bond sleeve is Canadian government bonds rather than US Treasuries, and the original's commodity sleeve is folded into gold. The backtest also ignores taxes — bond and dividend income and every rebalance in a non-registered account are taxable events.

Next rebalance signal

Portfolio vs. benchmarks

Start-date sensitivity — rolling CAGR by start date

Drawdown from peak

Annual returns

Allocation over time

Cumulative contributions vs. portfolio value

What follows are simulations, not predictions. The market doesn't know we ran them — and neither do taxes, fees, or how you'll actually feel when your account is down 50%. Use these to get a sense of the risk, not to plan around a specific outcome.

Monte Carlo — 10-year forward projection (2,000 paths)

What is this telling me?

We ran 2,000 imaginary versions of the next 10 years by replaying real historical daily return patterns in random order. Each "path" is one possible future. Together they show the full range — from lucky outcomes to unlucky ones — so you can see what this portfolio might realistically do going forward.

Green line (median path)

Half of all 2,000 futures ended above this line, half below. Think of it as the single most representative outcome — not a guarantee, just the middle of the range.

Orange bands

The darker band shows where the middle 50% of futures landed. The lighter outer band covers 90% of all simulated futures. A wider band means more uncertainty.

Median CAGR

The typical yearly growth rate across all simulations — like a compound interest rate. A 30% CAGR means your money roughly triples every ~4 years in the median scenario.

Risk of Ruin

The % of futures where your portfolio fell below 50% of what you started with. This is the worst-case catastrophic loss risk — you should be comfortable with this number before investing.

Probability of Loss

The % of futures where you ended 10 years with less money than you started. Even over a long horizon, some paths go negative — this tells you how often that happened.

Mean Max Drawdown

On average across all futures, how far did the portfolio fall from its peak before recovering? For example, -60% means at some point you'd be watching your account drop by more than half. This is the emotional pain number.

⚠️ Important: This is based on historical patterns and does not predict the future. Diversification reduces risk but never eliminates it — in a severe market crash, correlations rise and most assets can fall together. Past performance does not guarantee future results.

Percentile fan chart — portfolio value (block-bootstrap, not assuming normality)

50th pct (median) 25th–75th pct band 5th–95th pct band

Stress tests — portfolio performance during market crises

What is this telling me?

Unlike the Monte Carlo which simulates imaginary futures, stress tests look at what actually happened during real historical market crises. Because this portfolio spreads across assets that don't all move together and rebalances back to target weights, crashes affect it differently than holding any single asset alone.

Return %

How much the total portfolio gained or lost from the start to the end of that crisis window. A -39% return means if you had $100,000 at the start, you'd have about $61,000 at the end of that period.

Max Drawdown

The worst single drop from a peak during that period — the lowest your account would have looked. This is deeper than the overall return because it captures the scariest moment before any recovery.

Days

How many trading days the period covered. Shorter crashes (like COVID in 2020) recovered faster. Longer downturns (like 2022) dragged on and gave the portfolio less opportunity to average down.

Why some periods are missing

A crisis period can only be shown when every asset in the portfolio has real price data covering it. If one of your chosen ETFs launched after a given crisis (for example, the 2008 Financial Crisis), that period is hidden — there's no real data to backtest it.

💡 Tip: Large negative returns during crashes are expected with this portfolio. The key is whether the portfolio recovers — look at the Post-COVID Recovery row to see how dramatically the portfolio can rebound after a crash.

Return % per crisis period

Stress period detail

Period	Return	Max DD	Days

Date	Action	Pre total	Max drift	Trade $	Post total	Allocation after

Date	Type	Amount	Into	Before	After	Allocation after

Metric	Portfolio

Year	Inflation	Portfolio
Year	Inflation	Return	Balance

How these numbers are calculated

Data source

Prices come from Yahoo Finance via yfinance with auto_adjust=True, so closes are dividend-adjusted (every fund's distribution stream is included). All default tickers are CAD-listed, so prices and every figure on this page are in Canadian dollars. The bonds are Canadian and gold is CAD-hedged; only VFV (US stocks) is unhedged and carries CAD/USD moves.

CAGR

(end_value / start_value)^(1/years) − 1, where years = trading_days / 252.

Annualized volatility

std(daily_returns) × √252. Population std (divisor = n). Assumes daily returns are independent and identically distributed; in practice returns show mild autocorrelation and fat tails, so treat the annualized figure as an approximation.

Sharpe ratio

(mean_daily − rfr_daily) / std_daily × √252, where rfr_daily = (1 + rfr_annual)^(1/252) − 1. The risk-free rate is whatever you set in the config above. Default 2% is a rough time-weighted average for 2015–2022 (T-bill yield was near 0% in 2015–2021, ~5% in 2023+). For period-accurate ratios, override to match your chosen backtest window. Also reported with a standard error band: SE(Sharpe) = √((1 + 0.5·Sharpe²) / T_years) (Lo 2002, IID approximation).

Sortino ratio

(mean_daily − rfr_daily) × 252 / downside_dev_annualized, where downside deviation only counts days with returns below rfr_daily, summed-of-squares divided by total n (Sortino & Price 1994 convention).

Maximum drawdown

Largest peak-to-trough percentage loss using daily close prices only. Intraday lows are not captured, so actual lived drawdowns may be a few percent worse.

Monte Carlo

Block-bootstrap resampling of historical daily portfolio returns. Default: 2,000 paths, 10-year horizon, 5-day blocks, RNG seed = 42 on first render (click "Re-run with new seed" to see how stable the bands are). Block-bootstrapping preserves short-term autocorrelation and volatility clustering — it does not assume normal/Gaussian returns. Risk of Ruin = % of paths ending below 50% of the anchor value; Probability of Loss = % ending below the anchor value.

The MC projection anchor is configurable via the toggle above the section. Default: initial capital (matches a fresh investor's mental model: "if I start today with $X, what range of outcomes might I see in 10 years?"). The alternative is anchoring on the backtest's ending value (useful when asking "if I'm already at $X, what's next?").

Limitation: 5-day blocks preserve weekly autocorrelation but not regime/crisis dependencies. Heavier-tailed events than what's in the historical sample won't appear.

Stress tests

Hardcoded historical windows (2018 vol, COVID 2020, post-COVID recovery, 2022 rate hikes, 2022 tech crash, plus GFC 2008 if data exists). Only windows with ≥5 trading days of overlapping data are shown. With the default tickers the backtest starts in early 2013 (capped by VFV's late-2012 launch), so pre-2013 windows like the 2008 financial crisis won't render — the Canadian-listed funds didn't exist yet. COVID 2020 and the 2022 selloff are the main stress tests here.

Transaction frictions

Commission + slippage is applied symmetrically — the sell side receives price × (1 − f), the buy side pays price × (1 + f). Default 0.10% per side ≈ 0.20% round-trip, realistic for discount brokers on highly-liquid ETFs.

What's NOT modeled

Taxes — every rebalance is a taxable event in a non-registered account
Currency / FX — all returns are USD, ignoring CAD/USD volatility
Foreign withholding tax on US ETF distributions (treatment depends on account type)
Inflation — returns are nominal, not real (subtract ~2–3% per year for real CAGR)
Settlement delays — assumes T+0 cash availability
Bid-ask spread variability — uses closing price, not actual fill price

Source code

All calculations are open and inspectable in the public repository — Python tools (Monte Carlo, stress tests) and JS in this page itself.

The bottom line: this is a learning tool, full stop. Every number depends on the inputs you chose, so different choices give you different results. Even diversified portfolios can lose 20–40% in a bad year, and no backtest changes that. Foreign withholding tax, currency drag, and tax-wrapper choice can all shift the real-world outcome meaningfully from what the chart shows. Before you put real money on the line, sit down with a registered Canadian advisor. That's what they're there for.