The Tool Desk
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Start by identifying the exact Aave deployment (network, pool and version), then read the current position and reserve parameters. Model the applicable liquidation size, compare that size with real pool and order-book depth, and repeat the exercise under lower prices, higher debt and thinner liquidity. Treat the result as a set of assumptions, not as a universal “safe” health-factor number.
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Why a health factor is not enough
In Aave V3, liquidation eligibility begins when a position’s health factor falls below 1. The health factor is calculated from collateral values, each asset’s liquidation threshold and the debt value; market prices and accrued interest can change it continuously. A simplified expression is:
health factor = (sum of collateral value × liquidation threshold) ÷ debt value
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A value above 1 means the protocol’s threshold calculation currently covers the debt. It does not prove that a liquidator can turn the collateral into enough debt-repayment funds at, or close to, the oracle-marked value. A liquidator selling a large amount into a shallow pool may move the price, receive less than expected and incur transaction costs. That execution gap can make a position economically riskier than its displayed health factor suggests.
Aave’s risk documentation treats asset liquidity, market depth and liquidation price impact as material risk factors, rather than assigning one universal slippage or liquidation-time number to every asset. See Aave’s risk documentation and the Chaos Labs analysis of liquidation parameters and value at risk for that framework.
The practical question is therefore two-dimensional:
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- Execution test: how much collateral could be sold, through which routes, at what price impact and under what market conditions?
Both tests must pass for a credible risk assessment.
Confirm the exact Aave market before calculating anything
Parameters are reserve-specific and can differ by network, pool and protocol version. Record the deployment before copying any number into a spreadsheet or script.
Deployment checklist
- Network or chain (for example, the particular Layer 1 or Layer 2 deployment).
- Aave pool and version, such as V3 or a deployment using V4 mechanics.
- Collateral asset addresses and debt-asset addresses, not just ticker symbols.
- Collateral balances, debt balances and whether each supplied asset is enabled as collateral.
- Oracle source and the timestamp of the prices used by the interface or your data feed.
Do not carry a liquidation threshold, bonus, cap or close-factor rule from one chain to another. Governance can change reserve parameters, and two assets with the same symbol can have different contracts and settings.
Where to obtain account and reserve data
Aave’s Pool interface documentation describes the account-data and reserve-data functions exposed by the pool. Use the deployed pool address for the target market and capture the returned collateral value, debt value, average liquidation threshold, loan-to-value, available borrowing and health factor together with the reserve configuration. The web interface is convenient for a human check, but contract reads or a trusted indexer are preferable for a repeatable risk report.
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For each collateral reserve, record at least:
| Parameter | Why it matters to a slow-sale assessment |
|---|---|
| Loan-to-value (LTV) | Sets borrowing capacity at origination; lower headroom means a smaller price move can put the account under stress. |
| Liquidation threshold | Determines when the collateral is considered insufficient for liquidation calculations and feeds the health factor. |
| Liquidation bonus | Incentivizes a liquidator to buy seized collateral; it may or may not compensate for slippage, fees and operational risk. |
| Supply and borrow caps | Constrain how much can be supplied or borrowed and can affect how much inventory is available for a liquidation route. |
| Isolation or silo settings | Can restrict which debts may be borrowed against the asset and change the set of positions and liquidation paths that are possible. |
| Collateral and debt eligibility flags | Show whether the asset is enabled for use as collateral or debt in this reserve and market. |
The live values, not a historical parameter page, belong in the calculation. Aave’s risk-parameters repository is useful for definitions and governance context, but verify the deployed configuration for the specific market.
Estimate the liquidation amount under the applicable rules
Liquidity analysis is meaningless until you have a plausible sale quantity. Start with the debt that could be repaid and the collateral that could be seized, then test a range rather than one precise forecast.
V3 close-factor conditions
Aave’s FAQ describes these V3 conditions: up to 50% of total debt can be liquidated when the health factor is above 0.95 and both collateral and debt values are at least $2,000; up to 100% of debt can be liquidated when the health factor is 0.95 or below, or when either collateral or debt value is below $2,000. These are protocol conditions published in the Aave FAQ, not a promise that every transaction will repay exactly that amount. Check the target deployment’s implementation, available collateral and transaction constraints.
For a V3 scenario, calculate at least two bounds:
- Partial-close case: the maximum debt repayment allowed by the applicable close factor, converted into the amount of collateral a liquidator would receive after applying the reserve’s liquidation bonus.
- Full-close case: the debt and collateral that could be involved when the health factor is at or below 0.95, or when the stated value conditions permit up to 100% repayment.
Then divide the seized collateral across assets according to the liquidation transaction’s available collateral and the liquidator’s chosen route. A position with several collaterals may not be liquidated in the same proportions as its deposits; route availability and the liquidator’s transaction choice matter.
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Do not apply V3 close factors to V4
Aave’s V4 overview describes a different liquidation design, including a target-health-factor approach, a variable bonus and dust controls. Those concepts should not be merged with the V3 FAQ rules. Read the V4 documentation and the deployed contracts before choosing a liquidation-size formula. If the version is uncertain, report both the uncertainty and the two possible rule sets rather than presenting a V3 percentage as universal.
Convert debt repayment into a sale quantity
For each candidate liquidation amount:
- Value the debt to be repaid in the protocol’s accounting currency at the current oracle prices.
- Apply the reserve’s liquidation bonus and the protocol’s collateral-seizure rules to estimate the collateral units transferred to the liquidator.
- Split those units by asset and identify which asset is likely to be sold first.
- Add a range for accrued interest and price movement between the account-data read and the liquidation transaction.
Use a range because liquidation is permissionless and timing is uncertain. A single “expected” sale amount hides the tail case in which the debt grows or prices move before execution.
Measure executable liquidity instead of total value locked
Total value locked, market capitalization and a displayed 24-hour volume are context, not proof that the liquidation quantity can be sold. The relevant question is how much can be executed now, through an available route, without moving the price beyond the assumptions used in the health-factor calculation.
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On-chain pool depth
For every plausible liquidation quantity, inspect the reserves along the actual swap route on the relevant chain. Calculate the quoted output for the full size and for smaller slices. A concentrated-liquidity pool may show substantial liquidity near the current price but very little outside a narrow range; a large sale can cross several price bands. Include swap fees, protocol fees, gas and any transfer restrictions in the net proceeds.
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If the asset trades on an order-book venue, record executable bid depth rather than the last traded price. Note the spread, depth at successive price levels, withdrawal or bridge requirements and whether the venue is accessible to the liquidator in the same time window. External liquidity can reduce pressure on an on-chain pool, but it is not interchangeable automatically: funds may need to be bridged, deposited or routed through another chain.
Route availability and concentration
List the independent sale routes and their failure points. If one pool supplies almost all visible depth, treat its liquidity as concentrated rather than diversified. Check whether the debt asset can be obtained directly, whether an intermediate pair is required, and whether the route depends on a single oracle, bridge or market maker. A route that exists on a dashboard but cannot settle in the target block should not be counted as executable.
Price-impact worksheet
| Measurement | What to record | Interpretation |
|---|---|---|
| Spot or oracle price | Timestamp, source and chain | Reference value only; it is not the liquidation execution price. |
| Quote for 1% of the estimated sale | Net output after fees | Shows local depth near the current price. |
| Quote for the full estimated sale | Output, average execution price and impact versus reference | Tests whether the liquidation size itself moves the market. |
| Split-route quote | Output when the sale is divided across independent pools or venues | Reveals whether diversification materially reduces impact. |
| Stress quote | Same sizes after removing a portion of visible liquidity | Approximates a volatile or partially withdrawn market. |
No official cross-asset statistic establishes a universal liquidation time or slippage percentage. Report the measured assumptions for the asset, chain, size and timestamp instead of importing a number from another market.
Stress the gap between oracle value and sale value
Build scenarios that change both the protocol variables and the market-execution variables. A liquidation can become harder to complete at the same moment that the health factor is deteriorating.
Price and debt scenarios
- Collateral decline: lower each collateral price by a chosen shock and recompute the health factor and liquidation quantity.
- Debt growth: add accrued interest and any variable-rate increase expected during the monitoring window.
- Borrowed-asset rally: increase the debt-asset value relative to collateral, especially when the two assets are not positively correlated.
- Correlation breakdown: model collateral falling while the debt asset remains stable or rises; historical correlation is not a guarantee during stress.
Liquidity scenarios
- Depth haircut: remove a portion of displayed pool or order-book liquidity and re-quote the sale.
- Spread widening: increase the bid-ask spread and swap cost to reflect a disorderly market.
- Single-route failure: exclude the deepest venue and test whether the remaining routes can absorb the sale.
- Sequential selling: model several liquidations or a split sale rather than assuming one atomically favorable trade.
Timing and operations
Include a delay assumption for monitoring, transaction submission and confirmation, and state it explicitly. Network congestion, failed transactions, oracle-update timing or a liquidator’s inventory constraints can make the realized sale differ from a snapshot. These are scenario assumptions, not guaranteed outcomes, so present their effect on the result rather than claiming a fixed delay.
For each scenario, retain four outputs: health factor, debt repayment permitted, collateral units to be sold and net debt-asset proceeds after estimated impact and fees. The scenario fails the execution test when proceeds are insufficient for the assumed debt repayment or when the required price impact is materially larger than the liquidation incentive can absorb.
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Interpret liquidation bonus in relation to execution risk
The liquidation bonus is the protocol’s incentive for a liquidator to acquire collateral while repaying debt. It is not a guaranteed profit margin. A liquidator must account for:
- price impact from selling the seized collateral;
- swap, exchange, gas and financing costs;
- inventory and hedging risk while the position is being unwound;
- bridge, withdrawal or settlement delays;
- the chance that another liquidator executes first or that the available route disappears.
Compare the bonus with the estimated all-in execution cost at the plausible sale size. A bonus that looks generous for a small trade may be inadequate for a full-close case in a shallow market. Conversely, a deep market can make a smaller incentive workable. Do not label an asset liquidatable merely because its bonus exceeds a nominal swap fee; use the full route and stress quotes.
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Compare collateral choices with one consistent framework
When deciding whether one collateral asset or position is more exposed than another, compare the same dimensions and the same stress horizon.
| Comparison axis | Questions to answer |
|---|---|
| Protocol headroom | What are the LTV and liquidation threshold, and how much price decline reaches the threshold at the current debt? |
| Volatility | How quickly can the collateral move relative to the monitoring and liquidation window? |
| Correlation | Could collateral and debt move against the position together during market stress? |
| Executable depth | Can the estimated partial-close and full-close quantities be sold on the target chain? |
| Price impact | What is the average execution price for the whole quantity and for a split route? |
| Incentive versus cost | Does the liquidation bonus leave room for slippage, fees and operational risk? |
| Market constraints | Are supply caps, isolation or silo rules, asset eligibility, chain differences or version-specific mechanics binding? |
This framework prevents a high-threshold, volatile asset with thin liquidity from appearing safer than a lower-threshold asset with deep, diversified markets simply because its current health factor is higher.
Set monitoring rules that reflect slow execution
A single health-factor alert is too coarse when collateral takes time to sell. Create alerts for both protocol and market conditions.
Account alerts
- Health factor crossing your internal warning levels, with a separate alert below 1 for V3 liquidation eligibility.
- Collateral or debt balances changing because of deposits, withdrawals, borrowing, repayment or interest accrual.
- Reserve-parameter changes affecting LTV, liquidation threshold, bonus, caps or collateral eligibility.
Market alerts
- Available depth at the estimated liquidation size falling below the required amount.
- Price impact or spread exceeding the assumption in your liquidation model.
- A major sale route, pool or order book becoming unavailable.
- Collateral and debt prices moving in opposite directions or volatility increasing sharply.
Recalculate after a parameter vote, a large collateral deposit or withdrawal, a debt-rate change, an oracle event or a material liquidity change. Keep the timestamp and chain for every snapshot so an operator can distinguish stale data from a genuine improvement.
Common assessment mistakes and how to correct them
“The health factor is 1.2, so the position is safe.”
Aave’s help documentation states: “There is no universally ‘safe’ health factor, as this depends on the volatility and correlation of your assets.” The page is available at Aave’s Health Factor & Liquidations guide. Replace the absolute label with a scenario result that includes price, debt and liquidity assumptions.
Using total value locked as sellable liquidity
TVL can include positions outside the relevant price range, assets that cannot be routed into the debt asset and liquidity that may be withdrawn. Quote the actual liquidation size through executable routes and report net output.
Applying a parameter from another chain or version
Reserve settings and liquidation designs are deployment-specific. Record the network, pool address and version, then verify the live reserve data before using a close factor or bonus.
Assuming the liquidation bonus covers any slippage
The bonus is an incentive, not a guaranteed cushion. Subtract all execution and operational costs from the bonus at the modeled sale size.
Quoting a universal liquidation time or slippage figure
No such cross-asset figure is established in the cited official materials. Give a timestamped estimate for the asset, route, quantity and stress case, and identify what would invalidate it.
Ignoring a full-close tail case
For V3, the FAQ permits up to 100% debt liquidation at a health factor of 0.95 or below, or when either collateral or debt value is below $2,000. Test that larger quantity even if a partial liquidation appears adequate.
A worked assessment template (with variables, not invented market data)
The following template can be implemented in a spreadsheet or monitoring service. Replace every variable with a timestamped value from the target deployment.
- Identify: enter
chain,pool_version,pool_address, collateral and debt contract addresses. - Snapshot: enter
collateral_value,debt_value,health_factor, reserve LTV, liquidation threshold, bonus, caps and isolation/silo flags. - Choose liquidation bounds: for V3, calculate the applicable 50% and 100% debt-repayment cases using the FAQ conditions; for V4, use the deployed target-health-factor and bonus logic instead.
- Convert to units: calculate
collateral_units_to_sellfor each asset after the protocol’s seizure and bonus rules. - Quote routes: obtain net output for each quantity through every executable on-chain and external route, including fees and gas.
- Stress: rerun after collateral-price declines, debt growth, depth reductions, wider spreads and removal of the deepest route.
- Report: state the lowest health factor, largest modeled sale, net proceeds, price impact, assumptions, timestamp and the condition that would trigger a new calculation.
A concise risk statement should read like: “On chain X, pool version Y, the position’s health factor is Z at time T. Under the specified liquidation rule, the modeled sale is Q units. Available routes return R debt units with P% estimated impact in the base case and R2 with P2% impact in the stressed case. The conclusion depends on the listed prices, depth and timing assumptions.” This format makes uncertainty visible without pretending to forecast an exact transaction.
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Conclude only after the protocol and execution tests have been run together. A position may have a comfortable current health factor but fail a slow-sale stress because the modeled liquidation quantity overwhelms available depth. Another position may be closer to its threshold yet remain more executable because several independent markets can absorb the sale.
State the exact deployment, data timestamp, liquidation rule, sale-size range, routes tested, price-impact estimate and stress assumptions. Say which observation would change the result: a parameter update, a health-factor move, a depth loss, a route outage or a correlation shift. That is more useful than assigning a generic “safe” label, and it remains valid when market conditions change.
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