DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe

DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe | 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 DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe Bao Bui-Quang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8169039/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 In this work, we introduce a bio‑inspired encoding framework for forecasting the direction of financial time series. Motivated by the limitations of linear models and the opacity of many deep learning approaches, we draw an analogy to genetics: observable micro‑patterns are encoded into symbolic "Financial DNA" sequences. These sequences are then analyzed using a probabilistic state‑transition mechanism to estimate the likelihood of subsequent market directions. We evaluate the approach on Bitcoin hourly OHLCV data with a rolling backtest. Among the horizons considered, modeling transitions from current Financial DNA patterns to the 4‑hour‑ahead price direction yields the strongest results, achieving a win ratio of 0.729. The findings suggest that compact, interpretable symbolic representations can capture salient, recurring structures in noisy, non‑stationary markets and support effective directional forecasts. Financial Mathematics Computational Mathematics quantitative time series forecasting data encoding Full Text Additional Declarations The authors declare no competing interests. Supplementary Files data.csv data.csv 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-8169039","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548449726,"identity":"c50912f2-3372-4f05-a530-420a889b69eb","order_by":0,"name":"Bao Bui-Quang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwYGNjAJBIwPSNbCDKIkiNQCAWwSRGkxZz987DFPgXVi/+z2a9WFe2zq+KUbWDd8wKPFsict3ZjHID1xxp0zZbdnPEuTkJxzgO3mDHwOO5BjJs1jcDix4UZO2m2eA4clDG4ksN3mwafl/BuIlvlALcVwLX/wabkBtWXDjfRjzHAt+LxvOeNZuuEcg3TjjTdymKVnHEiTnDkjse1mDx4t5vzJxx68+WMtO+9G+sPPBQds+Pklko/d+IHPGghgBmIeA2YIh7GBsAaIFvYHzMQoHQWjYBSMgpEHAHKiUWd2mogeAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-3627-672X","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Bao","middleName":"","lastName":"Bui-Quang","suffix":""}],"badges":[],"createdAt":"2025-11-21 03:19:28","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-8169039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8169039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96802017,"identity":"e11c34df-625b-450a-86eb-336a1f907cdf","added_by":"auto","created_at":"2025-11-26 08:48:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":330195,"visible":true,"origin":"","legend":"","description":"","filename":"AGlimpseIntoTheNext4HourTimeframe.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8169039/v1_covered_64d10dd3-a168-4b52-8b14-f5c8aabfd572.pdf"},{"id":96802016,"identity":"f206445c-656d-4336-9535-cc0f2fb668c1","added_by":"auto","created_at":"2025-11-26 08:48:26","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7392775,"visible":true,"origin":"","legend":"\u003cp\u003edata.csv\u003c/p\u003e","description":"","filename":"data.csv","url":"https://assets-eu.researchsquare.com/files/rs-8169039/v1/6aef3c1f29a74ece8623d745.csv"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"quantitative, time series, forecasting, data encoding","lastPublishedDoi":"10.21203/rs.3.rs-8169039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8169039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this work, we introduce a bio‑inspired encoding framework for forecasting the direction of financial time series. 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