How do I simulate a bear market?

Open this page and use the live Rebalancing Strategy tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive a bear market from your own measurements.

Use case · powered by Rebalancing Strategy

Rebalancing Strategy for a bear market

Simulate a bear market live in your browser. This runs the real Rebalancing Strategy solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Efficient Frontier

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Efficient FrontierLive

Markowitz portfolio theory

Controls

Presets

Asset A return

Asset A volatility

Asset B return

Asset B volatility

Correlation

Combining two assets traces a curved frontier of risk versus return. Because they are not perfectly correlated, some mixes have lower risk than either asset alone — the power of diversification. The pink point is the minimum-variance portfolio. Educational tool, not investment advice.

▶ Run in Python

Data Inspector

Min-var weight A0%

Min-var return0.0%

Min-var volatility0.0%

Governing equation

σ_{p}^{2} = w^{2} σ_{A}^{2} + (1 - w)^{2} σ_{B}^{2} + 2 w (1 - w) ρ σ_{A} σ_{B}, ρ = 0.20, w^{*} = 0%

Reading this result: The assets differ sharply in volatility, so the minimum-variance blend leans heavily toward the calmer one to hold total risk down.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Monte Carlo

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Monte Carlo Price SimulationLive

geometric Brownian motion

Controls

Presets

Initial price S₀

Expected return μ

Volatility σ

Horizon (years)

Geometric Brownian motion models a price with constant drift and random volatility — the assumption behind Black-Scholes. Running hundreds of simulated paths reveals the full distribution of outcomes, not just an average. Educational tool, not investment advice.

▶ Run in Python

Data Inspector

Median outcome$0

5th percentile$0

95th percentile$0

Governing equation

S_{t + Δ t} = S_{t} e^{(0.07 - \frac{0. 2 ^{2}}{2}) Δ t + 0.2 Δ t Z}, \tilde{S}_{1} \approx $0

Reading this result: Median outcome is about $0, just under the drift-only $107 — GBM compounds in log-space, so volatility always pulls the typical path below the average.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Rebalancing Strategy tool models a bear market with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Rebalancing Strategy

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Other ways to simulate a bear market

Black-Scholes for a bear market Option Payoff for a bear market Monte Carlo (GBM) for a bear market Efficient Frontier for a bear market Value at Risk for a bear market Bond Pricing for a bear market

Frequently asked questions

How do I simulate a bear market?: Open this page and use the live Rebalancing Strategy tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive a bear market from your own measurements.