A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild
2026-07-10 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors studied trading patterns on centralized cryptocurrency exchanges (CEXes), where many coins can be quickly traded. They created a new way to spot one-way arbitrage (OWA) trades, which involve exploiting price differences, even though the trades don't show who made them. Their analysis of Binance and Kraken data found millions of likely OWA trades, but most earned less than $1 after fees. Over time, these arbitrage opportunities have become faster but less profitable. The authors suggest more research is needed to understand when these opportunities can be successfully used.
centralized exchangecryptocurrencyarbitrageone-way arbitragetrade volumetrading feesBinanceKrakenspot trade data
Authors
Eugenio Nerio Nemmi, Tobias Lauinger, Paz Grimberg, Massimo La Morgia, Damon McCoy, Alessandro Mei
Abstract
Centralized cryptocurrency exchanges (CEXes) enable fast off-chain conversions between hundreds of coins. It is an open question which algorithmic trading patterns occur on these platforms. A major challenge to measuring CEXes is that their public trade data does not contain addresses or trader identifiers allowing linkage. We propose a novel methodology to infer one-way arbitrage (OWA) trading in anonymized spot trade data from CEXes. We identify 402 M likely OWA sequences in 5 years of trading on Binance (and almost 2 M during 9 years on Kraken), accounting for 0.94 % and 0.13 % of the total traded volume, respectively. While we estimate total profits of $31.2 M on Binance and $975 k on Kraken, profits from individual OWA sequences are less than $1 on average after accounting for trading fees. We also observe that OWA has become faster over time, while the profitability of individual sequences has decreased. Our findings highlight that pricing discrepancies regularly occur in CEXes, and raise questions for future work to identify the precise circumstances that enable profitable OWA.