Adversarial-Robust Deep Reinforcement Learning for High-Frequency Cryptocurrency Trading with Explainable AI Framework

Adversarial-Robust Deep Reinforcement Learning for High-Frequency Cryptocurrency Trading with Explainable AI Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adversarial-Robust Deep Reinforcement Learning for High-Frequency Cryptocurrency Trading with Explainable AI Framework JOY SINHA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8214644/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract High-frequency trading (HFT) in cryptocurrency markets has increasingly adopted deep reinforcement learning (DRL) algorithms to capitalize on microsecond-level price move- ments and market inefficiencies. However, the susceptibility of DRL models to adversarial attacks poses significant security risks, potentially leading to substantial financial losses and market manipulation. This paper introduces a novel adversarial- robust DRL framework specifically designed for high-frequency cryptocurrency trading, integrated with explainable AI (XAI) mechanisms to ensure regulatory compliance and transparency. We propose a multi-scale adversarial training methodology that addresses unique cryptocurrency market microstructure char- acteristics, including 24/7 trading, extreme volatility, and frag- mented liquidity across exchanges. Our framework incorporates five distinct attack vectors—FGSM, PGD, C&W, order book manipulation, and latency-based attacks—to comprehensively evaluate robustness. Defense mechanisms include adversarial training, defensive distillation, and a novel dynamic adapta- tion strategy that adjusts defenses based on real-time market conditions. The explainability component integrates SHAP for global feature importance and LIME for local decision in- terpretation, maintaining sub-10ms latency to meet HFT re- quirements. Experimental results on Bitcoin, Ethereum, and major altcoin datasets demonstrate that our adversarial-robust framework achieves 94.3% of baseline trading performance while successfully defending against 89.7% of adversarial attacks. The explainability framework provides transparent insights into trading decisions with 8.3ms average latency. This research con- tributes the first comprehensive adversarial defense framework for cryptocurrency HFT, advancing the state-of-the-art in secure and interpretable algorithmic trading systems. Finance Artificial Intelligence and Machine Learning High-frequency trading deep reinforcement learning adversarial robustness explainable AI cryptocurrency markets market manipulation detection Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8214644","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551296177,"identity":"83fbaf94-88ec-4e45-95c3-5355e0e23be4","order_by":0,"name":"JOY SINHA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACxgYgkcBwgIGfIYHxAIMBKVokG0CkgYEEsZYBFR8AaWEgQgtze4/xh4c77sgbH08+cOBDwZ86fgbmh49u4HNYzxkzicQzzwy3nXmWcHAG0GGSDWzGxjn4tMzIMWNIbDvMuO1GjsFhHqAWgwM8bNIEtBh/AGqx3zwj/wNYiz0RWgwkgFoSN0jkMEBsYSCkpedYGUhL8owzzwyAfjGWnHGYgF8M25s3f/zZdti2vz354YMPf+T4+dubHz7Gq6WBAz3GmfEoBwF5BvYHBJSMglEwCkbBiAcAj8hThHP73PgAAAAASUVORK5CYII=","orcid":"","institution":"Deloitte USI","correspondingAuthor":true,"prefix":"","firstName":"JOY","middleName":"","lastName":"SINHA","suffix":""}],"badges":[],"createdAt":"2025-11-26 15:42:54","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8214644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8214644/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97136938,"identity":"832247ee-69d6-4d06-b04c-40618068f6f4","added_by":"auto","created_at":"2025-12-01 09:57:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":207843,"visible":true,"origin":"","legend":"","description":"","filename":"finamanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8214644/v1_covered_55dae301-6f11-48a0-b8e0-128899bdf348.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdversarial-Robust Deep Reinforcement Learning\u003c/p\u003e\n\u003cp\u003efor High-Frequency Cryptocurrency Trading with\u003c/p\u003e\n\u003cp\u003eExplainable AI Framework\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Deloitte USI","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-frequency trading, deep reinforcement learning, adversarial robustness, explainable AI, cryptocurrency markets, market manipulation detection","lastPublishedDoi":"10.21203/rs.3.rs-8214644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8214644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-frequency trading (HFT) in cryptocurrency markets has increasingly adopted deep reinforcement learning (DRL) algorithms to capitalize on microsecond-level price move- ments and market inefficiencies. 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The explainability component integrates SHAP for global feature importance and LIME for local decision in- terpretation, maintaining sub-10ms latency to meet HFT re- quirements. Experimental results on Bitcoin, Ethereum, and major altcoin datasets demonstrate that our adversarial-robust framework achieves 94.3% of baseline trading performance while successfully defending against 89.7% of adversarial attacks. The explainability framework provides transparent insights into trading decisions with 8.3ms average latency. 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