{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:01:21Z","timestamp":1778788881570,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"publisher","award":["5241"],"award-info":[{"award-number":["5241"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"publisher","award":["5241"],"award-info":[{"award-number":["5241"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"publisher","award":["5241"],"award-info":[{"award-number":["5241"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"publisher","award":["5241"],"award-info":[{"award-number":["5241"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s12530-024-09593-6","type":"journal-article","created":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T15:01:55Z","timestamp":1717858915000},"page":"1865-1880","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep reinforcement learning with positional context for intraday trading"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8849-0097","authenticated-orcid":false,"given":"Sven","family":"Golu\u017ea","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomislav","family":"Kova\u010devi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tessa","family":"Bauman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zvonko","family":"Kostanj\u010dar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"issue":"5","key":"9593_CR1","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1090\/noti1105","volume":"61","author":"DH Bailey","year":"2014","unstructured":"Bailey DH, Borwein JM, de Prado ML et al (2014) Pseudomathematics and financial charlatanism: the effects of backtest over fitting on out-of-sample performance. Not AMS 61(5):458\u2013471. https:\/\/doi.org\/10.1090\/noti1105","journal-title":"Not AMS"},{"issue":"6","key":"9593_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0156338","volume":"11","author":"J Chan Phooi M\u2018ng","year":"2016","unstructured":"Chan Phooi M\u2018ng J, Mehralizadeh M (2016) Forecasting east asian indices futures via a novel hybrid of wavelet-pca denoising and artificial neural network models. PloS One 11(6):e015633. https:\/\/doi.org\/10.1371\/journal.pone.0156338","journal-title":"PloS One"},{"issue":"5","key":"9593_CR3","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/MSP.2011.941548","volume":"28","author":"R Cont","year":"2011","unstructured":"Cont R (2011) Statistical modeling of high-frequency financial data. IEEE Signal Process Mag 28(5):2\u201316. https:\/\/doi.org\/10.1109\/MSP.2011.941548","journal-title":"IEEE Signal Process Mag"},{"issue":"3","key":"9593_CR4","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","volume":"28","author":"Y Deng","year":"2016","unstructured":"Deng Y, Bao F, Kong Y et al (2016) Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans Neural Netw Learn Syst 28(3):653\u2013664. https:\/\/doi.org\/10.1109\/TNNLS.2016.2522401","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9593_CR5","doi-asserted-by":"crossref","unstructured":"Golu\u017ea S, Bauman T, Kova\u010devi\u0107 T, et\u00a0al (2023) Imitation learning for financial applications. In: 2023 46th MIPRO ICT and Electronics Convention (MIPRO), pp 1130\u20131135","DOI":"10.23919\/MIPRO57284.2023.10159778"},{"issue":"3","key":"9593_CR6","doi-asserted-by":"publisher","first-page":"7","DOI":"10.21511\/bbs.13(3).2018.06","volume":"13","author":"I Gurrib","year":"2018","unstructured":"Gurrib I et al (2018) Performance of the average directional index as a market timing tool for the most actively traded usd based currency pairs. Banks Bank Syst 13(3):7\u201358. https:\/\/doi.org\/10.21511\/bbs.13(3).2018.06","journal-title":"Banks Bank Syst"},{"issue":"1","key":"9593_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1540-6261.2010.01624.x","volume":"66","author":"T Hendershott","year":"2011","unstructured":"Hendershott T, Jones CM, Menkveld AJ (2011) Does algorithmic trading improve liquidity? J Financ 66(1):1\u201333. https:\/\/doi.org\/10.1111\/j.1540-6261.2010.01624.x","journal-title":"J Financ"},{"key":"9593_CR8","unstructured":"Huang CY (2018) Financial trading as a game: A deep reinforcement learning approach Preprint at arXiv: https:\/\/arxiv.org\/abs\/1807.02787"},{"issue":"2","key":"9593_CR9","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1093\/jjfinec\/nbab007","volume":"21","author":"D Huddleston","year":"2023","unstructured":"Huddleston D, Liu F, Stentoft L (2023) Intraday market predictability: a machine learning approach. J Financ Econom 21(2):485\u2013527. https:\/\/doi.org\/10.1093\/jjfinec\/nbab007","journal-title":"J Financ Econom"},{"key":"9593_CR10","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization Preprint at arXiv: https:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"4","key":"9593_CR11","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1111\/mafi.12413","volume":"33","author":"PN Kolm","year":"2023","unstructured":"Kolm PN, Turiel J, Westray N (2023) Deep order flow imbalance: extracting alpha at multiple horizons from the limit order book. Math Financ 33(4):1044\u2013108. https:\/\/doi.org\/10.1111\/mafi.12413","journal-title":"Math Financ"},{"key":"9593_CR12","doi-asserted-by":"crossref","unstructured":"Kova\u010devi\u0107 T, Golu\u017ea S, Mer\u0107ep A et\u00a0al (2022) Effect of labeling algorithms on financial performance metrics. In: 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), pp 980\u2013984","DOI":"10.23919\/MIPRO55190.2022.9803522"},{"key":"9593_CR13","doi-asserted-by":"publisher","first-page":"108014","DOI":"10.1109\/ACCESS.2019.2932789","volume":"7","author":"Y Li","year":"2019","unstructured":"Li Y, Zheng W, Zheng Z (2019) Deep robust reinforcement learning for practical algorithmic trading. IEEE Access 7:108014\u2013108022. https:\/\/doi.org\/10.1109\/ACCESS.2019.2932789","journal-title":"IEEE Access"},{"key":"9593_CR14","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s40747-023-01183-4","volume":"10","author":"X Li","year":"2023","unstructured":"Li X, Khishe M, Qian L (2023) Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system. Complex Intell Syst 10:595\u2013611. https:\/\/doi.org\/10.1007\/s40747-023-01183-4","journal-title":"Complex Intell Syst"},{"key":"9593_CR15","doi-asserted-by":"publisher","DOI":"10.3905\/jfds.2019.1.015","author":"B Lim","year":"2019","unstructured":"Lim B, Zohren S, Roberts S (2019) Enhancing time-series momentum strategies using deep neural networks. J Financ Data Sci. https:\/\/doi.org\/10.3905\/jfds.2019.1.015","journal-title":"J Financ Data Sci"},{"key":"9593_CR16","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu Q, Zhao H et\u00a0al (2020) Adaptive quantitative trading: an imitative deep reinforcement learning approach. In: Proceedings of the AAAI conference on artificial intelligence, pp 2128\u20132135","DOI":"10.1609\/aaai.v34i02.5587"},{"key":"9593_CR17","doi-asserted-by":"publisher","first-page":"107952","DOI":"10.1016\/j.asoc.2021.107952","volume":"113","author":"F Liu","year":"2021","unstructured":"Liu F, Li Y, Li B et al (2021) Bitcoin transaction strategy construction based on deep reinforcement learning. Appl Soft Comput 113:107952. https:\/\/doi.org\/10.1016\/j.asoc.2021.107952","journal-title":"Appl Soft Comput"},{"issue":"4","key":"9593_CR18","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1109\/72.935097","volume":"12","author":"J Moody","year":"2001","unstructured":"Moody J, Saffell M (2001) Learning to trade via direct reinforcement. IEEE Trans Neural Netw 12(4):875\u201388. https:\/\/doi.org\/10.1109\/72.935097","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"9593_CR19","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jfineco.2011.11.003","volume":"104","author":"TJ Moskowitz","year":"2012","unstructured":"Moskowitz TJ, Ooi YH, Pedersen LH (2012) Time series momentum. J Financ Econ 104(2):25\u2013228. https:\/\/doi.org\/10.1016\/j.jfineco.2011.11.003","journal-title":"J Financ Econ"},{"key":"9593_CR20","volume-title":"Technical analysis of the financial markets: a comprehensive guide to trading methods and applications","author":"JJ Murphy","year":"1999","unstructured":"Murphy JJ (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. New York Institute of Finance, New York, NY, USA"},{"key":"9593_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/9781118662717","volume-title":"Inside the black box: a simple guide to quantitative and high frequency trading","author":"RK Narang","year":"2013","unstructured":"Narang RK (2013) Inside the black box: a simple guide to quantitative and high frequency trading, vol 846. Wiley, Hoboken, NJ, USA"},{"issue":"1","key":"9593_CR22","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/0304-405X(88)90021-9","volume":"22","author":"JM Poterba","year":"1988","unstructured":"Poterba JM, Summers LH (1988) Mean reversion in stock prices: evidence and implications. J Financ Econ 22(1):27\u201359. https:\/\/doi.org\/10.1016\/0304-405X(88)90021-9","journal-title":"J Financ Econ"},{"key":"9593_CR23","unstructured":"Schulman J, Moritz P, Levine S et\u00a0al (2015) High-dimensional continuous control using generalized advantage estimation Preprint at arxiv: https:\/\/arxiv.org\/abs\/1506.02438"},{"key":"9593_CR24","unstructured":"Schulman J, Wolski F, Dhariwal P et\u00a0al (2017) Proximal policy optimization algorithms Preprint at arXiv: https:\/\/arxiv.org\/abs\/1707.06347"},{"key":"9593_CR25","doi-asserted-by":"crossref","unstructured":"Si W, Li J, Ding P et\u00a0al (2017) A multi-objective deep reinforcement learning approach for stock index future\u2019s intraday trading. In: 2017 10th International symposium on computational intelligence and design (ISCID), pp 431\u2013436","DOI":"10.1109\/ISCID.2017.210"},{"key":"9593_CR26","doi-asserted-by":"crossref","unstructured":"Sun S, Xue W, Wang R et\u00a0al (2022) Deepscalper: a risk-aware reinforcement learning framework to capture fleeting intraday trading opportunities. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp 1858\u20131867","DOI":"10.1145\/3511808.3557283"},{"key":"9593_CR27","volume-title":"Reinforcement learning: an introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT press, Cambridge, MA, USA"},{"key":"9593_CR28","doi-asserted-by":"crossref","unstructured":"Vojtko R, Padysak M (2020) Continuous futures contracts methodology for backtesting Preprint at https:\/\/ssrn.com\/abstract=3517736","DOI":"10.2139\/ssrn.3517736"},{"key":"9593_CR29","doi-asserted-by":"publisher","first-page":"10173","DOI":"10.1016\/j.najef.2022.101733","volume":"62","author":"Z Wen","year":"2022","unstructured":"Wen Z, Bouri E, Xu Y et al (2022) Intraday return predictability in the cryptocurrency markets: momentum, reversal, or both. North Am J Econ Financ 62:10173. https:\/\/doi.org\/10.1016\/j.najef.2022.101733","journal-title":"North Am J Econ Financ"},{"key":"9593_CR30","doi-asserted-by":"crossref","unstructured":"Yang H, Liu XY, Zhong S et\u00a0al (2020) Deep reinforcement learning for automated stock trading: An ensemble strategy. In: Proceedings of the first ACM international conference on AI in finance, pp 1\u20138","DOI":"10.1145\/3383455.3422540"},{"key":"9593_CR31","doi-asserted-by":"publisher","DOI":"10.3905\/jfds.2020.1.030","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Zohren S, Stephen R (2020) Deep reinforcement learning for trading. J Financ Data Sci. https:\/\/doi.org\/10.3905\/jfds.2020.1.030","journal-title":"J Financ Data Sci"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-024-09593-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-024-09593-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-024-09593-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T20:03:45Z","timestamp":1724443425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-024-09593-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,8]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["9593"],"URL":"https:\/\/doi.org\/10.1007\/s12530-024-09593-6","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,8]]},"assertion":[{"value":"22 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}