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Unfamiliar with the concept of an oracle? Check out the Ethereum Foundation's oracle overview first.

All Uniswap v3 pools can serve as oracles, offering access to historical price and liquidity data. This capability unlocks a wide range of on-chain use cases.

Historical data is stored as an array of observations. At first, each pool tracks only a single observation, overwriting it as blocks elapse. This limits how far into the past users may access data. However, any party willing to pay the transaction fees may increase the number of tracked observations (up to a maximum of 65535), expanding the period of data availability to ~9 days or more.

Storing price and liquidity history directly in the pool contract substantially reduces the potential for logical errors on the part of the calling contract, and reduces integration costs by eliminating the need to store historical values. Additionally, the v3 oracle's considerable maximum length makes oracle price manipulation significantly more difficult, as the calling contract may cheaply construct a time-weighted average over any arbitrary range inside of (or fully encompassing) the length of the oracle array.


Observations take the following form:

struct Observation {
// the block timestamp of the observation
uint32 blockTimestamp;
// the tick accumulator, i.e. tick * time elapsed since the pool was first initialized
int56 tickCumulative;
// the seconds per liquidity, i.e. seconds elapsed / max(1, liquidity) since the pool was first initialized
uint160 secondsPerLiquidityCumulativeX128;
// whether or not the observation is initialized
bool initialized;

Observations may be retrieved via the observations method on v3 pools. However, this is not the recommended way to consume oracle data. Instead, prefer observe:

function observe(uint32[] calldata secondsAgos)
returns (int56[] memory tickCumulatives, uint160[] memory secondsPerLiquidityCumulativeX128s);

Each time observe is called, the caller must specify an array containing any number of seconds ago, denoting the times to return observations from. Note that each of the given times must be more recent (or as old as) the oldest stored observation. Note: if the times don't correspond exactly to a block in which an observation was written, a counterfactual observation will be constructed, removing the need for the caller to interpolate manually. This is one of the primary reasons to use observe over observations.

Note that because the oracle is only updated at most once every block, calling observe with a secondsAgo value of 0 will return the most recently written observation, which can only be as recent as the beginning of the current block (or older).

Tick Accumulator

The tick accumulator stores the cumulative sum of the active tick at the time of the observation. The tick accumulator value increases monotonically and grows by the value of the current tick - per second.

To derive the arithmetic mean tick over an interval, the caller needs to retrieve two observations, one after the other, take the delta of the two values, and divide by the time elapsed between them. Calculating a TWAP from the tick accumulator is also covered in the whitepaper. Note that using an arithmetic mean tick to derive a price corresponds to a geometric mean price.

See OracleLibrary for an example of how to use the tick accumulator.

Liquidity Accumulator

The liquidity accumulator stores the value of seconds / in-range liquidity at the time of the observation. The liquidity accumulator value increases monotonically and grows by the value of seconds / in-range liquidity - per second.

To derive the harmonic mean liquidity over an interval, the caller needs to retrieve two observations, one after the other, take the delta of the two values, then divide the time elapsed by this value. Calculating TWAL is addressed in finer detail in the whitepaper.


The in-range liquidity accumulator should be used with care. Because the current tick and the current in-range liquidity can be entirely uncorrelated, there are scenarios in which taking the arithmetic mean tick and the harmonic mean liquidity over the same interval in a pool can inaccurately characterize this pool relative to another. For example, if the current tick on pool A is 0 for 5 seconds, and 100 for 5 seconds, the tick accumulator will be 50. If over this same interval, the in-range liquidity was 5000 and 50, the harmonic mean liquidity will be ~99. Compare this to pool B (composed of the same assets) where the tick was 50 and the in-range liquidity was ~99 for 10 seconds. The accumulator values will be identical, but the underlying behavior is of course quite different.

Deriving Price From A Tick

When we use "active tick" or otherwise to refer to the current tick of a pool, we mean the lower tick boundary that is closest to the current price.

When a pool is created, each token is assigned to either token0 or token1 based on the contract address of the tokens in the pair. Whether or not a token is token0 or token1 is meaningless; it is only used to maintain a fixed assignment for the purpose of relative valuation and general logic in the pool contract.

Deriving an asset price from the current tick is achievable due to the fixed expression across the pool contract of token0 in terms of token1.

An example of finding the price of WETH in a WETH / USDC pool, where WETH is token0 and USDC is token1:

You have an oracle reading that shows a return of tickCumulative as [70_000, 1_070_000], with an elapsed time between the observations of 10 seconds.

We can derive the average tick over this interval by taking the difference in accumulator values (1_070_000 - 70_000 = 1_000_000), and dividing by the time elapsed (1_000_000 / 10 = 100_000).

With a tick reading of 100_000, we can find the value of token1 (USDC) in terms of token0 (WETH) by using the current tick as i in the formula p(i) = 1.0001**i (see 6.1 in the whitepaper).

1.0001**100_000 ≅ 22015.5 USDC / WETH

Ticks are signed integers and can be expressed as a negative number, so for any circumstances where token0 is of a lower value than token1, a negative tick value will be returned by tickCumulative and a relative value of < 0 will be returned by a calculation of token0 in terms of token1.