Dynamic Mutation-Driven Random Forest for Robust Algorithmic Trading in Volatile Markets

Dynamic Mutation-Driven Random Forest for Robust Algorithmic Trading in Volatile Markets | 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 Dynamic Mutation-Driven Random Forest for Robust Algorithmic Trading in Volatile Markets Danish Nawaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7410335/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 Algorithmic trading in volatile financial markets presents significant challenges due to abrupt price fluctuations, regime shifts, and noise-driven anomalies. Conventional machine learning models often fail to adapt dynamically, resulting in reduced profitability and heightened risk exposure. Prior studies leveraging Random Forests, Support Vector Machines, and Deep Neural Networks demonstrate predictive capabilities but suffer from overfitting, static parameterization, and poor generalization in rapidly changing market conditions. Moreover, existing ensemble models lack adaptive mutation mechanisms to recalibrate under stress scenarios. We introduce Dynamic Mutation-Driven Random Forest (DMRF), a novel ensemble learning framework that integrates evolutionary-inspired mutation strategies into tree construction. By dynamically mutating feature subsets and split thresholds during training, DMRF enhances diversity, reduces bias, and increases robustness against volatility-induced noise. Experiments were conducted on high-frequency intraday trading data from the S&P 500 index (2015–2023), encompassing 1-minute OHLCV (Open, High, Low, Close, Volume) records alongside derived technical indicators. Data normalization, volatility clustering detection, rolling-window feature engineering, and noise reduction using wavelet transforms were applied to ensure stable learning signals. DMRF achieved superior predictive accuracy (94.7%), F1-score (0.92), and Sharpe ratio (2.14) compared to baseline Random Forests and LSTM-based models. Notably, drawdown risk was reduced by 18.6%, underscoring the model’s resilience in highly volatile conditions. This work contributes a mutation-driven adaptive ensemble paradigm tailored for financial markets, demonstrating improved predictive stability, profitability, and risk management. The proposed framework advances robust algorithmic trading strategies under uncertainty. DMRF lays the foundation for adaptive, mutation-augmented machine learning architectures in finance, with potential extensions to portfolio optimization and real-time risk assessment. Theoretical Computer Science Computer Architecture and Engineering Algorithmic Trading Random Forest Mutation-Driven Learning Volatility Modeling High-Frequency Trading Ensemble Methods Full Text Additional Declarations The authors declare potential competing interests as follows: 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. We do this by developing innovative software and high quality services for the global research community. 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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-7410335","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502679059,"identity":"6914ce0c-f2d8-435f-a002-c78bd8e32ab8","order_by":0,"name":"Danish Nawaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDCCA0DE2ABmsv/4UAGiGBigAoS1MEjOOMPAwMNMhBa4CmneNiK08B0/e/Dgzx135A2OH39gOHOeTeJ+ZuaDD2cw2Mnp4tAneSYv4TDvmWeGG84kJCR83JaW2MPMlmy4gSHZ2OwAdi0GB3IMDjO2HWbcdiDhwMGZ2w4DtfCYST5gOJC4DZeW828MDv5sO2y/7fzDxmbeOcRouZFjcIC37XDithvJzMy8DVAtG/BokbzxxuAwUEvy/hvP2BhnHEsz7jkM9MsMA9x+4TufY/wR6DDbmf3pzxg+1NjItrc3H3zYU2Enh0sLLmBAmvJRMApGwSgYBagAAFrba5ztXluzAAAAAElFTkSuQmCC","orcid":"","institution":"University of Lahore","correspondingAuthor":true,"prefix":"","firstName":"Danish","middleName":"","lastName":"Nawaz","suffix":""}],"badges":[],"createdAt":"2025-08-19 15:54:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-7410335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7410335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89468373,"identity":"a968e98b-c21a-4f7f-ac43-f4e46f9b5db4","added_by":"auto","created_at":"2025-08-20 09:06:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":668814,"visible":true,"origin":"","legend":"","description":"","filename":"Mypaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7410335/v1_covered_e3d85dc6-8800-4c98-b6c1-a98a1362255e.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDynamic Mutation-Driven Random Forest for Robust Algorithmic Trading in Volatile Markets\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Lahore","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":"Algorithmic Trading, Random Forest, Mutation-Driven Learning, Volatility Modeling, High-Frequency Trading, Ensemble Methods","lastPublishedDoi":"10.21203/rs.3.rs-7410335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7410335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlgorithmic trading in volatile financial markets presents significant challenges due to abrupt price fluctuations, regime shifts, and noise-driven anomalies. Conventional machine learning models often fail to adapt dynamically, resulting in reduced profitability and heightened risk exposure.\u003cstrong\u003e \u003c/strong\u003ePrior studies leveraging Random Forests, Support Vector Machines, and Deep Neural Networks demonstrate predictive capabilities but suffer from overfitting, static parameterization, and poor generalization in rapidly changing market conditions. Moreover, existing ensemble models lack adaptive mutation mechanisms to recalibrate under stress scenarios.\u003cstrong\u003e \u003c/strong\u003eWe introduce Dynamic Mutation-Driven Random Forest (DMRF), a novel ensemble learning framework that integrates evolutionary-inspired mutation strategies into tree construction. By dynamically mutating feature subsets and split thresholds during training, DMRF enhances diversity, reduces bias, and increases robustness against volatility-induced noise. Experiments were conducted on high-frequency intraday trading data from the S\u0026amp;P 500 index (2015–2023), encompassing 1-minute OHLCV (Open, High, Low, Close, Volume) records alongside derived technical indicators. Data normalization, volatility clustering detection, rolling-window feature engineering, and noise reduction using wavelet transforms were applied to ensure stable learning signals.\u003cstrong\u003e \u003c/strong\u003eDMRF achieved superior predictive accuracy (94.7%), F1-score (0.92), and Sharpe ratio (2.14) compared to baseline Random Forests and LSTM-based models. Notably, drawdown risk was reduced by 18.6%, underscoring the model’s resilience in highly volatile conditions.\u003cstrong\u003e \u003c/strong\u003eThis work contributes a mutation-driven adaptive ensemble paradigm tailored for financial markets, demonstrating improved predictive stability, profitability, and risk management. 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