# Canonical: https://www.uddalak.xyz/hyperliquid-academy/article/106-how-to-measure-slippage-and-market-impact
# Author: Uddalak Das
# Last source: https://www.uddalak.xyz/hyperliquid-academy/article/106-how-to-measure-slippage-and-market-impact

---
title: "How to Measure Slippage and Market Impact on Hyperliquid"
description: "Execution analysis compares decision price, pre-trade midpoint, realized VWAP, and post-trade prices so spread cost, slippage, and lasting impact can be separated."
pillar: "Markets & Trading"
track: "Markets & Trading"
cluster: "Markets & Trading"
level: "Advanced"
primary_icp: ["trader", "institutional decision-maker"]
secondary_icp: ["market maker"]
content_type: "guide"
reading_time: "3 min"
last_reviewed: "2026-09-07"
last_verified: "2026-09-07"
volatile: false
prerequisites: ["69-spread-slippage-depth-market-impact", "104-how-to-read-hyperliquid-l2-order-book"]
related: ["105-how-to-measure-liquidity-on-hyperliquid", "68-market-making-on-hyperliquid"]
---

# How to Measure Slippage and Market Impact on Hyperliquid

Slippage is useful only after you specify the benchmark. Market impact is useful only after you specify the time horizon. A rigorous Hyperliquid execution study stores the decision timestamp, book before execution, all fills, and prices after the trade. That lets you determine whether cost came from crossing the spread, walking depth, latency, or moving the market itself.

## Decision price

Record the price when the strategy decided to trade. This captures implementation shortfall caused by waiting, network latency, and order logic before the first fill.

## Arrival midpoint

The midpoint immediately before the order reaches the market is a cleaner benchmark for pure execution mechanics. Takers normally expect some cost relative to midpoint because they cross the spread.

## Realized VWAP

Weight each fill price by size. Compare VWAP with decision and arrival benchmarks in basis points. For multi-minute executions, also calculate time-weighted or schedule-aware benchmarks if relevant.

## Post-trade mark-out

Measure midpoint or mark at 1 second, 10 seconds, 1 minute, and longer horizons. If price consistently moves against you immediately after a fill, adverse selection is significant. If it remains displaced after a large order, your flow may have lasting impact.

## Separate direction

A buy naturally has higher prices than a sell in a rising market. Normalize signs so positive cost means worse execution regardless of side.

Then segment by size, volatility, time of day, and order type.

## Use the result

If cost comes mostly from spread, seek more maker execution. If it comes from walking depth, slice size. If it comes from adverse selection, improve timing or fair-value estimation. If it comes from latency, optimize infrastructure.

Execution analytics should change the strategy, not merely produce a dashboard.

## How this connects

Start with [Spread, Slippage, Depth, and Market Impact on Hyperliquid](/hyperliquid-academy/article/69-spread-slippage-depth-market-impact), [How to Read Hyperliquid's L2 Order Book](/hyperliquid-academy/article/104-how-to-read-hyperliquid-l2-order-book) if the underlying concept is unfamiliar. Related reading: [How to Measure Liquidity on Hyperliquid](/hyperliquid-academy/article/105-how-to-measure-liquidity-on-hyperliquid), [Market Making on Hyperliquid](/hyperliquid-academy/article/68-market-making-on-hyperliquid).

## Sources

- https://hyperliquid.gitbook.io/hyperliquid-docs/for-developers/api/info-endpoint
- https://hyperliquid.gitbook.io/hyperliquid-docs/trading/market-making