[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82370-en":3,"doc-seo-82370-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82370,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild","Centralized cryptocurrency exchanges (CEXes) support rapid off-chain conversions across many coins, yet the algorithmic trading patterns they host remain poorly understood. Measuring one-way arbitrage (OWA) is difficult because CEX public trade data is anonymized, lacking addresses or trader identifiers for linkage. This work introduces a methodology to infer OWA sequences from anonymized spot trades, finding 402 thousand likely OWA sequences on Binance and nearly 2 million on Kraken over multi-year windows.","A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild  \nEugenio Nerio Nemmi  \n[nemmi@di.uniroma1.it](nemmi@di.uniroma1.it)[ ](nemmi@di.uniroma1.it)Sapienza University of Rome Rome, Italy  \nTobias Lauinger  \nNew York University New York, USA  \nPaz Grimberg New York University  \nNew York, USA  \nMassimo La Morgia  \n[lamorgia@di.uniroma1.it](lamorgia@di.uniroma1.it)[ ](lamorgia@di.uniroma1.it)Sapienza University of Rome Rome, Italy  \nDamon McCoy New York University  \nNew York, USA  \nAlessandro Mei  \n[mei@di.uniroma1.it](mei@di.uniroma1.it)[ ](mei@di.uniroma1.it)Sapienza University of Rome Rome, Italy  \narXiv :2607 .0949 1v 1 [ cs .CE] 10 Jul 2026  \nAbstract  \nCentralized 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 CEXesis 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 inanonymized 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 kon 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.  \n1 Introduction  \nAs cryptocurrencies have proliferated, traders have sought low-friction, high-speed ways to exchange between them. On-chain or cross-chain transactions can be relatively slow and require technical sophistication. In contrast, centralized cryptocurrency exchanges (CEXes) such as Binance and Kraken offer easy-to-use apps, web interfaces and APIs that enable low-latency, high-volume online trading off the chain. Many CEXes have long surpassed decentralized exchanges (DEXes) or on-chain transactions in terms of volume [24] . While there has been extensive work measuring blockchainsand DEXes (e.g., to trace payment flows [12, 13, 16, 18, 23] or detect arbitrage [7, 11, 15, 22, 25]), we are not aware of prior work that has measured which algorithmic trading patterns have emerged on CEXes.  \nFigure 1: One-way arbitrage (OWA) [vs. direct](vs. direct) conversion. When prices are misaligned, the indirect conversion can yield a larger output than a direct conversion.  \nIn this paper, we propose a methodology to measure arbitrage in historical spot trade data from CEXes (i.e., trades with immediate settlement) . We focus on one-way arbitrage (OWA) because in our preliminary data exploration, we found it much more prevalent than the better known triangular arbitrage. OWA is a trading strategy that aims to obtain a better price for a conversion 􀀖 → 􀀗 by executing instead an indirect conversion 􀀖 → 􀀞 → 􀀗 when there are advantageous price discrepancies. As an example, consider Figure 1 . When 1 BTC can buy 30 ETH or 582012 DOGE, then a DOGE-to-ETH price of 0.00005155 or above would make it cheaper to convert BTC → DOGE → ETH instead of BTC → ETH directly (excluding transaction fees) . In efficient markets, prices are normally aligned so that such OWA opportunities do not arise (law of one price) .  \nHowever, no market is perfect, and since Deardorff defined the term as a theoretical concept in 1979 [8], it has been an open question whether and how arbitrageurs actually carry out OWA in practice. Prior work has measured the prevalence of other forms of arbitrage. For example, aline of work has detected arbitrage executed on DEXes (i.e., blockchains) [7, 22, 25] . In the c","cbCaioexCCuFeftt","https://ap.wps.com/l/cbCaioexCCuFeftt","pdf",1965025,3,1,19,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is one-way arbitrage (OWA) in cryptocurrency trading?\",\"answer\":\"OWA uses an indirect conversion path (e.g., BTC → DOGE → ETH) when price discrepancies make it yield a better output than a direct conversion (BTC → ETH).\"},{\"question\":\"Why is measuring OWA on centralized exchanges difficult?\",\"answer\":\"CEX trade data is anonymized and does not include addresses or trader identifiers, so trades cannot be directly linked across the intermediate conversion steps at large scale.\"},{\"question\":\"How does the paper detect OWA sequences from anonymized spot trade data?\",\"answer\":\"It infers OWA using constraints based on trading timing and matched exchanged quantities, assuming rational profit-driven behavior, and tunes parameters to prioritize precision.\"}]",1784179980,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-truckload-of-satoshis-detecting-and-measuring-one-way-arbitrage-in-the-wild","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/a-truckload-of-satoshis-detecting-and-measuring-one-way-arbitrage-in-the-wild/82370/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is one-way arbitrage (OWA) in cryptocurrency trading?","Question",{"text":75,"@type":76},"OWA uses an indirect conversion path (e.g., BTC → DOGE → ETH) when price discrepancies make it yield a better output than a direct conversion (BTC → ETH).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is measuring OWA on centralized exchanges difficult?",{"text":80,"@type":76},"CEX trade data is anonymized and does not include addresses or trader identifiers, so trades cannot be directly linked across the intermediate conversion steps at large scale.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper detect OWA sequences from anonymized spot trade data?",{"text":84,"@type":76},"It infers OWA using constraints based on trading timing and matched exchanged quantities, assuming rational profit-driven behavior, and tunes parameters to prioritize precision.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]