How is post-trade price different from effective price?

Post-trade or marginal price is the spot price of the pool immediately after your swap, computed from the new reserves. Effective price is the average price your whole trade got. Post-trade price is always worse than effective price, which is worse than the original spot.

What makes price impact bigger?

Price impact grows with the ratio of your trade size to the pool reserves. A trade that is one percent of the pool barely moves it; a trade that is 30 percent of the input reserve moves the price sharply because the constant-product curve steepens as reserves drain.

Does the fee change the price impact?

The fee reduces the input that actually reaches the curve, so it slightly lowers the realised output and the post-trade price shift. The tool deducts the fee from the input before applying the constant-product formula, matching on-chain behaviour.

Why do MEV researchers care about this?

Price impact is the opportunity that sandwich attacks exploit. A bot front-runs a high-impact swap to push the price, lets the victim trade at the worse price, then sells back. Quantifying impact shows how much value a large naive swap leaves on the table.

Is this the same as Uniswap V3?

No. This models the V2 style constant-product curve with full-range liquidity. V3 concentrates liquidity in price ranges, so impact depends on where the active tick sits. Use this for V2 pools and as an upper-bound intuition for concentrated pools.

What is the AMM Price Impact Calculator?

Input pool reserves following x times y equals k and a trade amount to compute pre-trade and post-trade price, price impact percentage, and output amount after the protocol fee. Shows how pool depth affects trade quality for DeFi traders, aggregators, and MEV researchers. It runs free in your browser on Gera Tools, with nothing uploaded.

AMM Price Impact Calculator

Name: AMM Price Impact Calculator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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On a constant-product AMM your trade literally bends the price curve. This calculator takes pool reserves and a trade size and reports the spot price, the post-trade marginal price, the percentage impact, and the output you receive after the protocol fee.

How it works

The pool obeys x * y = k. Swapping dx of the input token (after fee f):

dx_eff   = dx * (1 - f)
new_x    = x + dx_eff
new_y    = k / new_x          where k = x * y
dy       = y - new_y          (output received)

spot price (out per in)       = y / x
post-trade price (out per in) = new_y / new_x
price impact                  = 1 - post-trade / spot

Because k is fixed, draining the output reserve raises the price of each remaining unit — the curve is convex, so impact accelerates with trade size.

Worked example

Suppose the ETH/USDC pool has 500 ETH and 1,000,000 USDC, so k = 500,000,000.

Spot price: 1,000,000 / 500 = 2,000 USDC per ETH
Trade: buy 50 ETH with USDC (0.3% fee applied to USDC input)
To receive 50 ETH: new_y = 450 ETH, so new_x = k / 450 = 1,111,111 USDC
USDC in (before fee): 1,111,111 - 1,000,000 = 111,111 USDC input (before accounting for fee exactly)
Post-trade price: 1,111,111 / 450 = 2,469 USDC per ETH
Price impact: (2,469 - 2,000) / 2,000 = ~23.4%

A 10% reduction in the output reserve of a constant-product pool causes roughly 11% price impact; a 33% reduction causes roughly 50% impact. The relationship is non-linear.

How pool depth determines impact

The same trade size has wildly different impact depending on pool depth (total reserves):

Pool size (each side)	Trade size	Approx. price impact
1,000 tokens	100 tokens	~18%
10,000 tokens	100 tokens	~2%
100,000 tokens	100 tokens	~0.2%
1,000,000 tokens	100 tokens	~0.02%

This is why liquidity depth is so critical to AMM quality — deeper pools give better execution to the same trade.

Strategies to reduce impact

Split across pools: If multiple pools exist for the same token pair, routing through several of them at lower sizes per pool reduces impact on each.
Use a TWAP or limit order: Time-weighted average price orders spread a large swap across many blocks, reducing per-block impact.
Wait for liquidity events: Fees attract more liquidity providers over time; executing a large trade after a significant LP deposit reduces impact.
Aggregators: DEX aggregators like 1inch or Paraswap route through multiple pools and use pathfinding to minimise impact automatically.

Why MEV researchers use this

Price impact is the quantity that makes sandwich attacks profitable. A searcher’s bot identifies a pending high-impact swap in the mempool, front-runs it to buy the output token cheaply, lets the victim’s swap push the price up, then immediately sells at the higher post-trade price. The profit to the sandwicher equals roughly the price impact their front-run caused times the size of their position. This calculator shows exactly how much value a large naive swap leaves exposed.