DeFi Simulation Examples

The easiest way to understand a DeFi strategy is to test it before using real money.

These examples show how a retail investor might use DeFiRiskSim to review common DeFi setups. The numbers in your own simulation will depend on your selected assets, market prices, protocol settings and assumptions.

Use these examples as starting points, not as recommendations.

Example 1: Testing an ETH/USDC concentrated liquidity position

A user wants to provide liquidity to an ETH/USDC pool on a concentrated liquidity DEX.

The position may look attractive because the pool has steady volume and fee income. But the user still needs to answer a few questions before depositing:

• How wide should the price range be?
• What happens if ETH moves outside the range?
• How much impermanent loss could appear if ETH rises or falls?
• Are the estimated fees enough to justify the risk?

Inside DeFiRiskSim, the user can enter the token pair, choose a price range and compare possible outcomes. A narrow range may produce better capital efficiency while the price stays inside the range, but it can stop earning fees sooner. A wider range may be more forgiving, but it may generate lower fee efficiency.

The goal is not to find a perfect range. The goal is to avoid choosing a range blindly.

Example 2: Borrowing stablecoins against ETH

A user holds ETH and wants access to stablecoins without selling the ETH.

This can make sense in some situations, but borrowing creates a new risk: liquidation. If ETH falls far enough, the collateral may no longer support the loan.

With the Hold & Borrow model, the user can test:

• how much stablecoin debt is being taken;
• what the estimated liquidation price may be;
• how the health factor changes if ETH drops;
• how much safety buffer remains.

This example is useful for investors who want to understand whether their loan is conservative or too aggressive before they open it.

Example 3: Building a stablecoin leverage loop

A user sees a lending market where deposits earn yield and borrowing costs are lower than expected rewards.

The strategy may involve depositing collateral, borrowing against it, depositing again and repeating the process. On the surface, the final APY may look better than a simple deposit.

But the loop also increases total debt and makes the position more sensitive to rate changes, collateral rules and liquidation thresholds.

DeFiRiskSim shows the loop step by step. The user can compare the simple version of the strategy against the looped version and decide whether the extra APY is worth the added fragility.

Example 4: Stress testing a liquid restaking position

A user holds a liquid restaking token and wants to use it in a yield strategy.

The expected return may include staking rewards, restaking rewards and possibly extra incentives. But the position can also be exposed to depeg risk, changing rewards, slashing assumptions and borrowing costs.

In the simulator, the user can test what happens if the liquid restaking token trades below its expected ETH value. This helps show whether the strategy still survives under stress or only works when everything goes right.

This is especially important when restaking is combined with borrowing or looping.

Example 5: Creating a delta-neutral ETH strategy

A user owns ETH but does not want the entire strategy to depend on ETH going up.

One possible approach is to keep the spot ETH position and open a short position on a perpetual venue. The short position may help offset price movement, while funding rates and other costs affect the final result.

DeFiRiskSim helps estimate:

• how large the hedge may need to be;
• what funding assumptions do to the result;
• where the short position may become risky;
• how the strategy behaves if ETH moves sharply.

The purpose of this example is not to remove all risk. A hedge can create new risks of its own. The purpose is to make those trade-offs visible before the user acts.

Example 6: Comparing a Hyperliquid vault allocation

A user is considering a vault because the historical return looks attractive.

Before allocating, the user needs to separate the headline number from the assumptions behind it:

• what return is being assumed;
• whether performance or leader fees reduce the depositor result;
• how much drawdown the user could tolerate;
• what happens if volume falls or vault growth slows.

The Hyperliquid vault simulator helps compare those cases without treating vault APY as a fixed yield. This is useful when a user wants exposure to a strategy but still needs to understand bad-case behavior.