Optimization of Multi-Threshold Trading Strategies in the Directional Changes Paradigm

Optimization of Multi-Threshold Trading Strategies in the Directional Changes Paradigm | 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 Optimization of Multi-Threshold Trading Strategies in the Directional Changes Paradigm Ozgur Salman, Themistoklis Melissourgos, Michael Kampouridis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5332104/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 The objective of this paper is to investigate the potential of using the Directional Changes (DC) paradigm for financial forecasting. DC is an event-based approach that differs from the traditional physical time data, which employs fixed time intervals and uses a physical time scale. The DC method records price movements when specific events occur instead of using fixed intervals. The determination of these events relies on a threshold, which captures significant changes in price of a given asset. This work employs eight trading strategies that are developed based on directional changes. These strategies were profiled using varying values of thresholds to provide a comprehensive analysis of their effectiveness. In order to enhance the performance of both the thresholds and trading strategies, a genetic algorithm was utilized to optimize their respective weights. This approach provided a more comprehensive analysis and enriched the information available to our trading strategy. To analyze our model in our experiment, we utilized 200 stocks listed on the New York Stock Exchange. Furthermore, the method proposed in this study successfully generated profitable trading strategies that exhibited superior performance when compared to specific DC-based benchmarks commonly utilized in the existing literature. Additionally, the proposed method outperformed conventional strategies based on technical indicators such as ADX, Aroon, CCI, EMA, MACD, RSI, and WilliamR. Directional Changes Genetic algorithm Trading strategies Stock forecasting Full Text Additional Declarations No competing interests reported. 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. 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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-5332104","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373319562,"identity":"9bba5785-7b82-48ee-ab51-7f58c6080b9f","order_by":0,"name":"Ozgur Salman","email":"","orcid":"","institution":"University of Essex","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ozgur","middleName":"","lastName":"Salman","suffix":""},{"id":373319563,"identity":"91e6c716-c8f2-4473-ac1b-2479b3dc5abf","order_by":1,"name":"Themistoklis Melissourgos","email":"","orcid":"","institution":"University of Essex","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Themistoklis","middleName":"","lastName":"Melissourgos","suffix":""},{"id":373319564,"identity":"4adf5513-821e-4efd-90e7-bbd11c7b06d9","order_by":2,"name":"Michael Kampouridis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDACHjBpgyFCUEsa6VoOk6BFt+fw080FFefl5PsPH5NgqLFjMDhzAL8Ws7NtZrdnnLltbHAjLdmA4Vgyg8HZBgJazjOY3eZtu524QYLH8AED2wEGg/MEHGZ2nv0bUMu5+vn95z8cYPhHjJazPSBbDiQwHMhhfMDYdoAIh505Uwb0S7LhhhtpxgaJfck8kgS9fyZ92+2CCjt5YIg9k/jwzU6O70wCAZcBATOclUBERKJpGQWjYBSMglGADQAAOztF8BNtHg8AAAAASUVORK5CYII=","orcid":"","institution":"University of Essex","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Kampouridis","suffix":""}],"badges":[],"createdAt":"2024-10-25 11:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5332104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5332104/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68334163,"identity":"c1a81955-57aa-4932-b88c-ee7ec8f6dcb9","added_by":"auto","created_at":"2024-11-06 07:39:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":669110,"visible":true,"origin":"","legend":"","description":"","filename":"AIR2024Salman.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5332104/v1_covered_cac9d39e-9449-476c-b2e7-151632c4f2bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of Multi-Threshold Trading Strategies in the Directional Changes Paradigm","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":"Directional Changes, Genetic algorithm, Trading strategies, Stock forecasting","lastPublishedDoi":"10.21203/rs.3.rs-5332104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5332104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The objective of this paper is to investigate the potential of using the Directional Changes (DC) paradigm for financial forecasting. 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