Don't Let MEV Slip: The Costs of Swapping on the Uniswap Protocol
- URL: http://arxiv.org/abs/2309.13648v2
- Date: Wed, 17 Apr 2024 14:12:56 GMT
- Title: Don't Let MEV Slip: The Costs of Swapping on the Uniswap Protocol
- Authors: Austin Adams, Benjamin Y Chan, Sarit Markovich, Xin Wan,
- Abstract summary: We present the first empirical characterization of the costs of trading on a decentralized exchange (DEX)
Using quoted prices from the Uniswap Labs interface, we evaluate the efficiency of trading on DEXs.
Our results provide preliminary evidence that DEXs offer a compelling trust-less alternative to centralized exchanges for trading digital assets.
- Score: 0.17999333451993949
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present the first in-depth empirical characterization of the costs of trading on a decentralized exchange (DEX). Using quoted prices from the Uniswap Labs interface for two pools -- USDC-ETH (5bps) and PEPE-ETH (30bps) -- we evaluate the efficiency of trading on DEXs. Our main tool is slippage -- the difference between the realized execution price of a trade, and its quoted price -- which we breakdown into its benign and adversarial components. We also present an alternative way to quantify and identify slippage due to adversarial reordering of transactions, which we call reordering slippage, that does not require quoted prices or mempool data to calculate. We find that the composition of transaction costs varies tremendously with the trade's characteristics. Specifically, while for small swaps, gas costs dominate costs, for large swaps price-impact and slippage account for the majority of it. Moreover, when trading PEPE, a popular 'memecoin', the probability of adversarial slippage is about 80% higher than when trading a mature asset like USDC. Overall, our results provide preliminary evidence that DEXs offer a compelling trust-less alternative to centralized exchanges for trading digital assets.
Related papers
- Cross-Rollup MEV: Non-Atomic Arbitrage Across L2 Blockchains [6.892626226074608]
This study quantifies the potential non-atomic MEV on Layer-2 (L2) blockchains by measuring the arbitrage opportunities between cross-rollup and DEX-CEX.
By analyzing the costs of swap on L2s and price discrepancies cross-rollup and DEX-CEX, we identify more than 500 000 unexplored arbitrage opportunities.
arXiv Detail & Related papers (2024-06-04T10:03:23Z) - Trading Volume Maximization with Online Learning [3.8059763597999012]
We investigate how the broker should behave to maximize the trading volume.
We model the traders' valuations as an i.i.d. process with an unknown distribution.
If only their willingness to sell or buy at the proposed price is revealed after each interaction, we provide an algorithm achieving poly-logarithmic regret.
arXiv Detail & Related papers (2024-05-21T17:26:44Z) - Quantifying Arbitrage in Automated Market Makers: An Empirical Study of Ethereum ZK Rollups [6.892626226074608]
This work systematically reviews arbitrage opportunities between Automated Market Makers (AMMs) on ZK rollups, and Centralised Exchanges (CEXs)
We propose a theoretical framework to measure such arbitrage opportunities and derive a formula for the related Maximal Arbitrage Value (MAV)
Overall, the cumulative MAV from July to 2023 on the USDC-ETH SyncSwap pool amounts to $104.96k (0.24% of trading volume)
arXiv Detail & Related papers (2024-03-24T10:26:34Z) - A Bargaining-based Approach for Feature Trading in Vertical Federated
Learning [54.51890573369637]
We propose a bargaining-based feature trading approach in Vertical Federated Learning (VFL) to encourage economically efficient transactions.
Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties.
arXiv Detail & Related papers (2024-02-23T10:21:07Z) - Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement
Learning [19.916721360624997]
Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi)
This paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust price ranges.
Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio.
arXiv Detail & Related papers (2023-09-18T20:10:28Z) - Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology.
One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds.
This introduces the problem of finding an optimal strategy for choosing price intervals.
We formalize this problem as an online learning problem with non-stochastic rewards.
arXiv Detail & Related papers (2023-02-01T17:21:40Z) - Optimal Settings for Cryptocurrency Trading Pairs [2.0536599169058554]
The goal of cryptocurrencies is decentralization.
There is no default currency of denomination (fiat)
It is impractical to set up a trading market between every two currencies.
arXiv Detail & Related papers (2022-10-20T02:37:01Z) - Multivariate Probabilistic Forecasting of Intraday Electricity Prices
using Normalizing Flows [62.997667081978825]
In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern.
This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
arXiv Detail & Related papers (2022-05-27T08:38:20Z) - Price DOES Matter! Modeling Price and Interest Preferences in
Session-based Recommendation [55.0391061198924]
Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence.
It is nontrivial to incorporate price preferences for session-based recommendation.
We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
arXiv Detail & Related papers (2022-05-09T10:47:15Z) - Data Sharing Markets [95.13209326119153]
We study a setup where each agent can be both buyer and seller of data.
We consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money)
arXiv Detail & Related papers (2021-07-19T06:00:34Z) - A Deep Reinforcement Learning Framework for Continuous Intraday Market
Bidding [69.37299910149981]
A key component for the successful renewable energy sources integration is the usage of energy storage.
We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market.
An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency.
Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
arXiv Detail & Related papers (2020-04-13T13:50:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.