[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125554-en":3,"doc-seo-125554-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125554,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Interpretable trading pattern designed for machine learning applications","Financial markets provide non-stationary multidimensional time series, where each instrument exhibits distinct evolving properties, making analysis difficult. This study introduces a volume-price-based market representation designed to fit machine learning pipelines and evaluates it using statistical methods tied to multiple research questions. Results show improved classification of financial time-series patterns over a baseline and over price-level patterns, especially for more liquid instruments. The work further assesses SHAP feature interactions and compares them with interactions derived from tree-based models, finding strong similarity and supporting SHAP reliability for this setting.","University of Birmingham  \nInterpretable trading pattern designed for machine learning applications  \nSokolovsky, Artur; Arnaboldi, Luca; Bacardit, Jaume; Gross, Thomas  \nDOI:  \n10.1016/j.mlwa.2023.100448  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nSokolovsky, A, Arnaboldi, L, Bacardit, J & Gross, T 2023, ' Interpretable trading pattern designed for machine learning applications', Machine Learning with Applications, vol. 11, 100448.  \n[https://doi.org/10.1016/j.mlwa.2023.100448](https://doi.org/10.1016/j.mlwa.2023.100448)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Aug. 2026  \nInterpretable trading pattern designed for machine learning applications Artur Sokolovsky a,∗, Luca Arnaboldib, Jaume Bacardita, Thomas Gross a  \na Newcastle University, School of Computing, 1 Science Square, Newcastle upon, Tyne NE4 5TG, UK b University of Edinburgh, School of Informatics, 10 Crichton St, Newington, Edinburgh EH8 9AB, UK  \nA R T I C L E I N F O  \nKeywords:  \nApplied ML  \nVolume profiles Boosting trees Explainable ML Computational finance  \nA B S T R A C T  \nFinancial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing-over-time properties, making its analysis a complex task. Hence, improvement of understanding and development of more informative, generalisable market representations are essential for the successful operation in financial markets, including risk assessment, diversification, trading, and order execution.  \nIn this study, we propose a volume-price-based market representation for making financial time series more suitable for machine learning pipelines. We use a statistical approach for evaluating the representation. Through the research questions, we investigate, i) whether the proposed representation allows any improvement over the baseline (always-positive) performance; ii) whether the proposed representation leads to increased performance over the price levels market pattern; iii) whether the proposed representation performs better on the liquid markets, and iv) whether SHAP feature interactions are reliable to be used in the considered setting.  \nOur analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than the pr","cbCais4wnvYh0t7i","https://ap.wps.com/l/cbCais4wnvYh0t7i","pdf",835593,1,17,"English","en",105,"# Introduction\n## Motivation for interpretable trading patterns\n## Explainability and accountability in automated trading\n# Proposed representation and evaluation approach\n## Statistical evaluation of the volume-price representation\n# Results and comparisons\n## Baseline, price-level, and liquidity effects\n## SHAP interactions vs tree-based interactions","[{\"question\":\"What representation does the study propose for financial time series?\",\"answer\":\"It proposes a volume-price-based market representation intended to make financial time series more suitable for machine learning pipelines.\"},{\"question\":\"How does the proposed method perform compared with baseline and price-level patterns?\",\"answer\":\"The analysis shows successful classification of financial time-series patterns and better classification performance than price levels-based methods, with stronger results on more liquid instruments.\"},{\"question\":\"Are SHAP feature interactions reliable in this financial trading setting?\",\"answer\":\"Yes. The paper derives feature interactions from tree-based models and compares them with SHAP, finding significant similarity and concluding that SHAP interactions are reliable here.\"}]","Interpretable trading pattern designed for machine learning applications | PDF",1785899824,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interpretable-trading-pattern-designed-for-machine-learning-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretable-trading-pattern-designed-for-machine-learning-applications/125554/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What representation does the study propose for financial time series?","Question",{"text":75,"@type":76},"It proposes a volume-price-based market representation intended to make financial time series more suitable for machine learning pipelines.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method perform compared with baseline and price-level patterns?",{"text":80,"@type":76},"The analysis shows successful classification of financial time-series patterns and better classification performance than price levels-based methods, with stronger results on more liquid instruments.",{"name":82,"@type":73,"acceptedAnswer":83},"Are SHAP feature interactions reliable in this financial trading setting?",{"text":84,"@type":76},"Yes. 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