Can I go all-in on an aggressive mix?

You can, but be honest about the downside. All-growth has the highest median, yet a rough year can halve it. The question isn't whether you want high returns — it's whether you'll still hold when the account is down 50%. Sell, and it was all for nothing.

Should young people just be 100% stocks?

A long horizon does favour stocks, but '100% stocks' and '100% tech/semis' are different things. The first is aggressive-but-diversified; the second is a concentrated bet. Youth isn't a reason to concentrate — it's the capacity to ride out volatility.

How do I adjust as I age?

The rule is: the closer you are to needing the money, the more you shift toward stability. Come back every few years, shorten the horizon, dial expected return down, and watch the best mix change — then ease the growth sleeve down accordingly.

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Aggressive Growth: The Price of Maxing Out Return

With 20-plus years before you need the money and the nerve to hold through crashes, you can tilt hard toward growth. But 'aggressive' isn't 'reckless' — this page shows exactly how deep the downside gets when you chase the highest return.

Portfolio Allocation Optimizer

Two assets, one question: how much in each? Set their return and volatility, and let the simulation find your optimal split.

Capital & horizon

Starting capital

Time horizon

yrs

Asset A

Preset

Expected return (CAGR)

Volatility

How wildly yearly returns swing around the expected return. A broad stock index sits around 15%; single sectors and leveraged products are far higher.

Asset B

Preset

Expected return (CAGR)

Volatility

How wildly yearly returns swing around the expected return. A broad stock index sits around 15%; single sectors and leveraged products are far higher.

Advanced

How the two move together

Correlation measures whether the two assets rise and fall together. The less they move in lockstep, the more a blend cuts risk — that's the whole point of diversification.

Rebalance yearlyEach year, sell the winner and top up the laggard to restore your target split. This is the standard assumption behind the efficient frontier and is what makes a blend’s lower risk durable. Turn it off to see buy-and-hold drift toward the higher-return asset.

Limit max drawdownCap how deep a peak-to-trough fall the recommended mix may risk. Each strategy then picks the best split that stays within the cap, pushing the answer toward the steadier asset. A cap looser than the calmest mix changes nothing.

Drawdown cap

Optimize for

Picks the split with the best return per unit of risk (CAGR ÷ volatility). Usually an interior blend, since diversification lowers risk faster than it lowers return.

This is a risk-adjusted ratio (the Sharpe ratio), NOT a win rate. It measures how much annual growth you get per unit of volatility you endure — higher means a more efficient trade-off. A blend often scores highest because diversification cuts volatility faster than it cuts return.

Optimal allocation

50% A / 50% B

A = QQQ — Nasdaq 100 · B = SOXX — Semiconductors

Median 47.4M · sim. CAGR 27.9% · volatility 23.6%

40–60% QQQ — Nasdaq 100 is near-optimal — the exact split barely matters here.

Unlucky (P10)

10.2M

Typical (P50)

47.4M

Lucky (P90)

220.3M

Max drawdown

Median worst peak-to-trough drop along the simulated paths for this mix — the deepest loss you'd typically endure before recovering.

−20.6%

Optimal mix

100% A

100% B

Median ending

47.4M

11.1M

156.5M

Downside (P10)

10.2M

3.3M

22.6M

Volatility

23.6%

19.0%

30.0%

Max drawdown

−20.6%

−18.0%

−26.9%

Ending wealth by allocation

Median10–90% rangeOptimalNear-optimal

Based on 168000 simulated paths. A simplified lognormal model — not a forecast. Real markets have fatter tails and shifting correlations; treat these as rough odds, not promises.

What this scenario works out to

Under these assumptions, the best mix lands near 50% QQQ / 50% SOXX. After 25 years that blend's median outcome is about 46.1M, with a rough-patch (10th-percentile) value near 10.7M.

These figures use this scenario's example returns and volatility — swap in your own holdings' numbers in the calculator above.

How to read this result

This scenario pairs two high-growth, high-volatility equity sleeves (Nasdaq 100 and semiconductors) and targets the highest median, so the optimiser pushes toward whichever has the higher long-run return. The number to study is the P10 (rough-patch) cell: the median looks great, but the downside is deep. Whether you actually capture that median depends on whether you can hold through a -50% year.

Good fit: people around 30 with a 20-year-plus horizon, steady cash flow, who have already lived through one big crash without selling.

Watch out: semiconductors and tech are highly concentrated, and brutal when the sector turns. The historical returns here run hot — dial expected return down and look again. The closer you are to needing the money, the less of this you should hold.

Common questions

Can I go all-in on an aggressive mix?: You can, but be honest about the downside. All-growth has the highest median, yet a rough year can halve it. The question isn't whether you want high returns — it's whether you'll still hold when the account is down 50%. Sell, and it was all for nothing.
Should young people just be 100% stocks?: A long horizon does favour stocks, but '100% stocks' and '100% tech/semis' are different things. The first is aggressive-but-diversified; the second is a concentrated bet. Youth isn't a reason to concentrate — it's the capacity to ride out volatility.
How do I adjust as I age?: The rule is: the closer you are to needing the money, the more you shift toward stability. Come back every few years, shorten the horizon, dial expected return down, and watch the best mix change — then ease the growth sleeve down accordingly.

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How should you split capital between two assets? Set each one's CAGR and volatility, choose their correlation, and simulate thousands of paths to find the allocation that maximizes risk-adjusted return, median wealth, or downside protection.

Built by indigo.la.ringo · AppicLab · Last updated: June 25, 2026