{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T05:59:36Z","timestamp":1674799176798},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683706","type":"print"},{"value":"9781643683713","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,23]]},"abstract":"<jats:p>This paper presents a reinforcement learning approach for foreign exchange trading. Inspired by technical analysis methods, this approach makes use of technical indicators by encoding them into Gramian Angular Fields and searches for patterns that indicate price movements using convolutional neural networks (CNN). In addition to the policy that determines the action to take, an extra regression head is utilized to determine the size of market orders. This paper also experimentally shows that maximizing the return of individual trade or cumulative reward in a finite time window results to better performance.<\/jats:p>","DOI":"10.3233\/faia220494","type":"book-chapter","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T13:55:33Z","timestamp":1674741333000},"source":"Crossref","is-referenced-by-count":0,"title":["Scalp the Foreign Exchange Market with Deep Reinforcement Learning"],"prefix":"10.3233","author":[{"given":"Marina","family":"Tropmann-Frick","sequence":"first","affiliation":[{"name":"University of Applied Sciences Hamburg, Berliner Tor 7, 20099 Hamburg, Germany. marina.tropmann-frick@haw-hamburg.de"}]},{"given":"Phuc","family":"Tran","sequence":"additional","affiliation":[{"name":"thienphuc.tran@haw-hamburg.de"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Modelling and Knowledge Bases XXXIV"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220494","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T13:55:34Z","timestamp":1674741334000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220494"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,23]]},"ISBN":["9781643683706","9781643683713"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220494","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,23]]}}}