A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild
This work provides the first large-scale measurement of one-way arbitrage in centralized exchanges, revealing the prevalence and diminishing profitability of such trading patterns for researchers and regulators.
The authors propose a novel methodology to infer one-way arbitrage (OWA) trading from anonymized spot trade data on centralized exchanges, identifying 402 million sequences on Binance and 2 million on Kraken, accounting for 0.94% and 0.13% of total volume, with estimated profits of $31.2M and $975k, respectively, though individual profits average less than $1 after fees.
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.