A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild

2026-07-10

AI Summary

This study addresses the challenge of identifying and quantifying one-way arbitrage (OWA) activity on centralized cryptocurrency exchanges (CEXs), where user-identifiable transaction data are unavailable. To overcome this limitation, the authors propose a novel method that matches timestamped price movement sequences with transaction records, enabling OWA detection without requiring user identity information. By integrating a realistic trading fee model and statistical inference techniques, the approach effectively uncovers OWA behavior from large-scale anonymous spot trading data. Empirical analysis of five years of Binance data and nine years of Kraken data reveals approximately 402 million OWA instances yielding $31.2 million in profits and nearly 2 million instances generating $975,000, respectively. These findings elucidate the scale, temporal evolution, and characteristically low per-trade profitability of OWA, offering the first systematic quantification of such activity on CEXs.

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.

Problem

• one-way arbitrage
• centralized cryptocurrency exchanges
• trading patterns
• price discrepancies
• anonymized trade data

Innovation

• one-way arbitrage
• centralized cryptocurrency exchanges
• anonymized trade data
• algorithmic trading detection
• market microstructure