{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:46:56Z","timestamp":1780318016925,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,3]]},"DOI":"10.1145\/3490354.3494366","type":"proceedings-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T03:50:06Z","timestamp":1651722606000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":70,"title":["FinRL"],"prefix":"10.1145","author":[{"given":"Xiao-Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyang","family":"Yang","sequence":"additional","affiliation":[{"name":"Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiechao","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"New York University Shanghai"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Joshua Achiam. 2018. Spinning up in deep reinforcement learning. https:\/\/spinningup.openai.com  Joshua Achiam. 2018. Spinning up in deep reinforcement learning. https:\/\/spinningup.openai.com"},{"key":"e_1_3_2_1_2_1","volume-title":"Mean-variance investing. Columbia Business School Research Paper No. 12\/49. (August 10","author":"Ang Andrew","year":"2012","unstructured":"Andrew Ang . August 10, 2012. Mean-variance investing. Columbia Business School Research Paper No. 12\/49. (August 10 , 2012 ). Andrew Ang. August 10, 2012. Mean-variance investing. Columbia Business School Research Paper No. 12\/49. (August 10, 2012)."},{"key":"e_1_3_2_1_3_1","volume-title":"ICML Workshop on Applications and Infrastructure for Multi-Agent Learning","author":"Bao Wenhang","year":"2019","unstructured":"Wenhang Bao and Xiao-Yang Liu . 2019 . Multi-agent deep reinforcement learning for liquidation strategy analysis . ICML Workshop on Applications and Infrastructure for Multi-Agent Learning (2019). Wenhang Bao and Xiao-Yang Liu. 2019. Multi-agent deep reinforcement learning for liquidation strategy analysis. ICML Workshop on Applications and Infrastructure for Multi-Agent Learning (2019)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2009.04.015"},{"key":"e_1_3_2_1_5_1","volume-title":"OpenAI gym. arXiv preprint arXiv:1606.01540","author":"Brockman Greg","year":"2016","unstructured":"Greg Brockman , Vicki Cheung , Ludwig Pettersson , Jonas Schneider , John Schulman , Jie Tang , and Wojciech Zaremba . 2016. OpenAI gym. arXiv preprint arXiv:1606.01540 ( 2016 ). Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. OpenAI gym. arXiv preprint arXiv:1606.01540 (2016)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2019.1571683"},{"key":"e_1_3_2_1_7_1","volume-title":"Bellemare","author":"Castro Pablo Samuel","year":"2018","unstructured":"Pablo Samuel Castro , Subhodeep Moitra , Carles Gelada , Saurabh Kumar , and Marc G . Bellemare . 2018 . Dopamine : A research framework for deep reinforcement learning. http:\/\/arxiv.org\/abs\/1812.06110 (2018). Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare. 2018. Dopamine: A research framework for deep reinforcement learning. http:\/\/arxiv.org\/abs\/1812.06110 (2018)."},{"key":"e_1_3_2_1_8_1","unstructured":"Ltd China Securities Index Co. 2017. CSI 300. http:\/\/www.csindex.com.cn\/uploads\/indices\/detail\/files\/en\/145_000300_Fact_Sheet_en.pdf  Ltd China Securities Index Co. 2017. CSI 300. http:\/\/www.csindex.com.cn\/uploads\/indices\/detail\/files\/en\/145_000300_Fact_Sheet_en.pdf"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2522401"},{"key":"e_1_3_2_1_10_1","unstructured":"Prafulla Dhariwal Christopher Hesse Oleg Klimov Alex Nichol Matthias Plappert Alec Radford John Schulman Szymon Sidor Yuhuai Wu and Peter Zhokhov. 2017. OpenAI baselines. https:\/\/github.com\/openai\/baselines.  Prafulla Dhariwal Christopher Hesse Oleg Klimov Alex Nichol Matthias Plappert Alec Radford John Schulman Szymon Sidor Yuhuai Wu and Peter Zhokhov. 2017. OpenAI baselines. https:\/\/github.com\/openai\/baselines."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3129391"},{"key":"e_1_3_2_1_12_1","unstructured":"Shanghai Stock Exchange. 2018. SSE 180 Index Methodology. http:\/\/www.sse.com.cn\/market\/sseindex\/indexlist\/indexdetails\/indexmethods\/c\/IndexHandbook_EN_SSE180.pdf  Shanghai Stock Exchange. 2018. SSE 180 Index Methodology. http:\/\/www.sse.com.cn\/market\/sseindex\/indexlist\/indexdetails\/indexmethods\/c\/IndexHandbook_EN_SSE180.pdf"},{"key":"e_1_3_2_1_13_1","volume-title":"Reinforcement learning in financial markets-asurvey. FAU Discussion Papers in Economics","author":"Fischer Thomas G.","unstructured":"Thomas G. Fischer . 2018. Reinforcement learning in financial markets-asurvey. FAU Discussion Papers in Economics . Friedrich-Alexander University Erlangen-Nuremberg , Institute for Economics. Thomas G. Fischer. 2018. Reinforcement learning in financial markets-asurvey. FAU Discussion Papers in Economics. Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics."},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Machine Learning","author":"Fujimoto Scott","year":"2018","unstructured":"Scott Fujimoto , Herke Van Hoof , and David Meger . 2018 . Addressing function approximation error in actor-critic methods . International Conference on Machine Learning (2018). Scott Fujimoto, Herke Van Hoof, and David Meger. 2018. Addressing function approximation error in actor-critic methods. International Conference on Machine Learning (2018)."},{"key":"e_1_3_2_1_15_1","volume-title":"Deep reinforcement learning in high frequency trading. ArXiv abs\/1809.01506","author":"Ganesh Prakhar","year":"2018","unstructured":"Prakhar Ganesh and Puneet Rakheja . 2018. Deep reinforcement learning in high frequency trading. ArXiv abs\/1809.01506 ( 2018 ). Prakhar Ganesh and Puneet Rakheja. 2018. Deep reinforcement learning in high frequency trading. ArXiv abs\/1809.01506 (2018)."},{"key":"e_1_3_2_1_16_1","volume-title":"NeurIPS'19 Workshop on Robust AI in Financial Services.","author":"Ganesh Sumitra","year":"2019","unstructured":"Sumitra Ganesh , Nelson Vadori , Mengda Xu , Hua Zheng , Prashant Reddy , and Manuela Veloso . 2019 . Reinforcement learning for market making in a multi-agent dealer market . NeurIPS'19 Workshop on Robust AI in Financial Services. Sumitra Ganesh, Nelson Vadori, Mengda Xu, Hua Zheng, Prashant Reddy, and Manuela Veloso. 2019. Reinforcement learning for market making in a multi-agent dealer market. NeurIPS'19 Workshop on Robust AI in Financial Services."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490354.3494415"},{"key":"e_1_3_2_1_18_1","volume-title":"Financial trading as a game: A deep reinforcement learning approach. arXiv preprint arXiv:1807.02787","author":"Huang Chien Yi","year":"2018","unstructured":"Chien Yi Huang . 2018. Financial trading as a game: A deep reinforcement learning approach. arXiv preprint arXiv:1807.02787 ( 2018 ). Chien Yi Huang. 2018. Financial trading as a game: A deep reinforcement learning approach. arXiv preprint arXiv:1807.02787 (2018)."},{"key":"e_1_3_2_1_19_1","unstructured":"Hang Seng Index. 2020. Hang Seng index and sub-indexes. https:\/\/www.hsi.com.hk\/eng\/indexes\/all-indexes\/hsi  Hang Seng Index. 2020. Hang Seng index and sub-indexes. https:\/\/www.hsi.com.hk\/eng\/indexes\/all-indexes\/hsi"},{"key":"e_1_3_2_1_20_1","volume-title":"Intelligent Systems Conference (IntelliSys)","author":"Jiang Zhengyao","year":"2017","unstructured":"Zhengyao Jiang and J. Liang . 2017. Cryptocurrency portfolio management with deep reinforcement learning . Intelligent Systems Conference (IntelliSys) ( 2017 ), 905--913. Zhengyao Jiang and J. Liang. 2017. Cryptocurrency portfolio management with deep reinforcement learning. Intelligent Systems Conference (IntelliSys) (2017), 905--913."},{"key":"e_1_3_2_1_21_1","unstructured":"Zhengyao Jiang Dixing Xu and J. Liang. 2017. A deep reinforcement learning framework for the financial portfolio management problem. ArXiv abs\/1706.10059.  Zhengyao Jiang Dixing Xu and J. Liang. 2017. A deep reinforcement learning framework for the financial portfolio management problem. ArXiv abs\/1706.10059."},{"key":"e_1_3_2_1_22_1","volume-title":"Sanjeevi","author":"Koratamaddi Prahlad","year":"2021","unstructured":"Prahlad Koratamaddi , Karan Wadhwani , Mridul Gupta , and Sriram G . Sanjeevi . 2021 . Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Engineering Science and Technology, an International Journal . Prahlad Koratamaddi, Karan Wadhwani, Mridul Gupta, and Sriram G. Sanjeevi. 2021. Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Engineering Science and Technology, an International Journal."},{"key":"e_1_3_2_1_23_1","volume-title":"financial turbulence, and risk management. Financial Analysts Journal 66 (10","author":"Kritzman Mark","year":"2010","unstructured":"Mark Kritzman and Yuanzhen Li. 2010. Skulls , financial turbulence, and risk management. Financial Analysts Journal 66 (10 2010 ). Mark Kritzman and Yuanzhen Li. 2010. Skulls, financial turbulence, and risk management. Financial Analysts Journal 66 (10 2010)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490354.3494413"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Liang Eric","year":"2018","unstructured":"Eric Liang , Richard Liaw , Robert Nishihara , Philipp Moritz , Roy Fox , Ken Goldberg , Joseph E. Gonzalez , Michael I. Jordan , and Ion Stoica . 2018 . RLlib: Abstractions for distributed reinforcement learning . In International Conference on Machine Learning (ICML). Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, and Ion Stoica. 2018. RLlib: Abstractions for distributed reinforcement learning. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_26_1","volume-title":"Continuous control with deep reinforcement learning. ICLR","author":"Lillicrap Timothy P","year":"2016","unstructured":"Timothy P Lillicrap , Jonathan J Hunt , Alexander Pritzel , Nicolas Heess , Tom Erez , Yuval Tassa , David Silver , and Daan Wierstra . 2016. Continuous control with deep reinforcement learning. ICLR ( 2016 ). Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2016. Continuous control with deep reinforcement learning. ICLR (2016)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Siyu Lin and P. Beling. 2020. A deep reinforcement learning framework for optimal trade execution. In ECML\/PKDD.  Siyu Lin and P. Beling. 2020. A deep reinforcement learning framework for optimal trade execution. In ECML\/PKDD.","DOI":"10.1007\/978-3-030-67670-4_14"},{"key":"e_1_3_2_1_28_1","unstructured":"Xiao-Yang Liu Zechu Li Zhaoran Wang and Jiahao Zheng. 2021. ElegantRL: A scalable and elastic deep reinforcement learning library. https:\/\/github.com\/AI4Finance-Foundation\/ElegantRL.  Xiao-Yang Liu Zechu Li Zhaoran Wang and Jiahao Zheng. 2021. ElegantRL: A scalable and elastic deep reinforcement learning library. https:\/\/github.com\/AI4Finance-Foundation\/ElegantRL."},{"key":"e_1_3_2_1_29_1","volume-title":"Deep RL Workshop, NeurIPS 2021","author":"Liu Xiao-Yang","year":"2021","unstructured":"Xiao-Yang Liu , Zechu Li , Zhuoran Yang , Jiahao Zheng , Zhaoran Wang , Anwar Walid , Jian Guo , and Michael Jordan . 2021 . ElegantRL-Podracer: Scalable and elastic library for cloud-native deep reinforcement learning . Deep RL Workshop, NeurIPS 2021 (2021). Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, and Michael Jordan. 2021. ElegantRL-Podracer: Scalable and elastic library for cloud-native deep reinforcement learning. Deep RL Workshop, NeurIPS 2021 (2021)."},{"key":"e_1_3_2_1_30_1","volume-title":"Data-Centric AI Workshop, NeurIPS.","author":"Liu Xiao-Yang","year":"2021","unstructured":"Xiao-Yang Liu , Jingyang Rui , Jiechao Gao , Liuqing Yang , Hongyang Yang , Zhaoran Wang , Christina Dan Wang , and Guo Jian . 2021 . Data-driven deep reinforcement learning in quantitative finance . Data-Centric AI Workshop, NeurIPS. Xiao-Yang Liu, Jingyang Rui, Jiechao Gao, Liuqing Yang, Hongyang Yang, Zhaoran Wang, Christina Dan Wang, and Guo Jian. 2021. Data-driven deep reinforcement learning in quantitative finance. Data-Centric AI Workshop, NeurIPS."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/1468-036X.00205"},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Machine Learning. 1928--1937","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih , Adria Puigdomenech Badia , Mehdi Mirza , Alex Graves , Timothy Lillicrap , Tim Harley , David Silver , and Koray Kavukcuoglu . 2016 . Asynchronous methods for deep reinforcement learning . In International Conference on Machine Learning. 1928--1937 . Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning. 1928--1937."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.935097"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-131X(1998090)17:5\/6<441::AID-FOR707>3.0.CO;2-#"},{"key":"e_1_3_2_1_35_1","volume-title":"Sentiment and knowledge based algorithmic trading with deep reinforcement learning. ArXiv abs\/2001.09403","author":"Nan Abhishek","year":"2020","unstructured":"Abhishek Nan , Anandh Perumal , and Osmar R Zaiane . 2020. Sentiment and knowledge based algorithmic trading with deep reinforcement learning. ArXiv abs\/2001.09403 ( 2020 ). Abhishek Nan, Anandh Perumal, and Osmar R Zaiane. 2020. Sentiment and knowledge based algorithmic trading with deep reinforcement learning. ArXiv abs\/2001.09403 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Pyfolio: A toolkit for portfolio and risk analytics in Python. https:\/\/github.com\/quantopian\/pyfolio.","year":"2019","unstructured":"Quantopian. 2019 . Pyfolio: A toolkit for portfolio and risk analytics in Python. https:\/\/github.com\/quantopian\/pyfolio. Quantopian. 2019. Pyfolio: A toolkit for portfolio and risk analytics in Python. https:\/\/github.com\/quantopian\/pyfolio."},{"key":"e_1_3_2_1_37_1","unstructured":"Antonin Raffin Ashley Hill Maximilian Ernestus Adam Gleave Anssi Kanervisto and Noah Dormann. 2019. Stable Baselines3. https:\/\/github.com\/DLR-RM\/stable-baselines3.  Antonin Raffin Ashley Hill Maximilian Ernestus Adam Gleave Anssi Kanervisto and Noah Dormann. 2019. Stable Baselines3. https:\/\/github.com\/DLR-RM\/stable-baselines3."},{"key":"e_1_3_2_1_38_1","volume-title":"Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Applied Sciences 9 (10","author":"Rundo Francesco","year":"2019","unstructured":"Francesco Rundo . 2019. Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Applied Sciences 9 (10 2019 ), 1--18. Francesco Rundo. 2019. Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Applied Sciences 9 (10 2019), 1--18."},{"key":"e_1_3_2_1_39_1","volume-title":"Deep reinforcement learning in cryptocurrency market making. arXiv: Trading and Market Microstructure","author":"Sadighian Jonathan","year":"2019","unstructured":"Jonathan Sadighian . 2019. Deep reinforcement learning in cryptocurrency market making. arXiv: Trading and Market Microstructure ( 2019 ). Jonathan Sadighian. 2019. Deep reinforcement learning in cryptocurrency market making. arXiv: Trading and Market Microstructure (2019)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Svetlana Sapuric and A. Kokkinaki. 2014. Bitcoin is volatile! Isn't that right?. In BIS.  Svetlana Sapuric and A. Kokkinaki. 2014. Bitcoin is volatile! Isn't that right?. In BIS.","DOI":"10.1007\/978-3-319-11460-6_22"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Otabek Sattarov Azamjon Muminov Cheol Lee Hyun Kang Ryumduck Oh Junho Ahn Hyung Oh and Heung Jeon. 2020. Recommending cryptocurrency trading points with deep reinforcement learning approach. Applied Sciences 10.  Otabek Sattarov Azamjon Muminov Cheol Lee Hyun Kang Ryumduck Oh Junho Ahn Hyung Oh and Heung Jeon. 2020. Recommending cryptocurrency trading points with deep reinforcement learning approach. Applied Sciences 10.","DOI":"10.3390\/app10041506"},{"key":"e_1_3_2_1_42_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , and Oleg Klimov . 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 ( 2017 ). John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_1_43_1","volume-title":"Portfolio theory and capital markets","author":"Sharpe William F","unstructured":"William F Sharpe . 1970. Portfolio theory and capital markets . McGraw-Hill College . William F Sharpe. 1970. Portfolio theory and capital markets. McGraw-Hill College."},{"key":"e_1_3_2_1_44_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al.","author":"Silver David","year":"2016","unstructured":"David Silver , Aja Huang , Chris J Maddison , Arthur Guez , Laurent Sifre , George Van Den Driessche , Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016 . Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484. David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484."},{"key":"e_1_3_2_1_45_1","volume-title":"Advances in Neural Information Processing Systems 12","author":"Sutton Richard S.","unstructured":"Richard S. Sutton , David Mcallester , Satinder Singh , and Yishay Mansour . 2000. Policy gradient methods for reinforcement learning with function approximation . In Advances in Neural Information Processing Systems 12 . MIT Press , 1057--1063. Richard S. Sutton, David Mcallester, Satinder Singh, and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems 12. MIT Press, 1057--1063."},{"key":"e_1_3_2_1_46_1","volume-title":"Exchange traded funds (ETF): History, mechanism, academic literature review and research perspectives. Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","author":"Tarassov Evgeni B.","year":"2016","unstructured":"Evgeni B. Tarassov . 2016. Exchange traded funds (ETF): History, mechanism, academic literature review and research perspectives. Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal ( 2016 ). Evgeni B. Tarassov. 2016. Exchange traded funds (ETF): History, mechanism, academic literature review and research perspectives. Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal (2016)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422519"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422561"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"e_1_3_2_1_50_1","volume-title":"NeurIPS Workshop","author":"Xiong Zhuoran","year":"2018","unstructured":"Zhuoran Xiong , Xiao-Yang Liu , Shan Zhong , Hongyang Yang , and Anwar Walid . 2018 . Practical deep reinforcement learning approach for stock trading . NeurIPS Workshop (2018). Zhuoran Xiong, Xiao-Yang Liu, Shan Zhong, Hongyang Yang, and Anwar Walid. 2018. Practical deep reinforcement learning approach for stock trading. NeurIPS Workshop (2018)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422540"},{"key":"e_1_3_2_1_52_1","volume-title":"International Joint Conference on Artificial Intelligence (IJCAI).","author":"Zha Daochen","unstructured":"Daochen Zha , Kwei-Herng Lai , Kaixiong Zhou , and X. X. Hu . 2019. Experience replay optimization . International Joint Conference on Artificial Intelligence (IJCAI). Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, and X. X. Hu. 2019. Experience replay optimization. International Joint Conference on Artificial Intelligence (IJCAI)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-016-9585-0"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.3905\/jfds.2020.1.030"}],"event":{"name":"ICAIF'21: 2nd ACM International Conference on AI in Finance","location":"Virtual Event","acronym":"ICAIF'21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the Second ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490354.3494366","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3490354.3494366","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:42Z","timestamp":1750188642000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490354.3494366"}},"subtitle":["deep reinforcement learning framework to automate trading in quantitative finance"],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":54,"alternative-id":["10.1145\/3490354.3494366","10.1145\/3490354"],"URL":"https:\/\/doi.org\/10.1145\/3490354.3494366","relation":{},"subject":[],"published":{"date-parts":[[2021,11,3]]},"assertion":[{"value":"2022-05-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}