{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-reinforcement-learning-for-time-series","title":"Deep reinforcement learning for time series: playing idealized trading games","arxiv_id":"1803.03916","date":"2018-03-11","proceeding":null,"authors":["Xiang Gao"],"abstract":"Deep Q-learning is investigated as an end-to-end solution to estimate the\noptimal strategies for acting on time series input. Experiments are conducted\non two idealized trading games. 1) Univariate: the only input is a wave-like\nprice time series, and 2) Bivariate: the input includes a random stepwise price\ntime series and a noisy signal time series, which is positively correlated with\nfuture price changes. The Univariate game tests whether the agent can capture\nthe underlying dynamics, and the Bivariate game tests whether the agent can\nutilize the hidden relation among the inputs. Stacked Gated Recurrent Unit\n(GRU), Long Short-Term Memory (LSTM) units, Convolutional Neural Network (CNN),\nand multi-layer perceptron (MLP) are used to model Q values. For both games,\nall agents successfully find a profitable strategy. The GRU-based agents show\nbest overall performance in the Univariate game, while the MLP-based agents\noutperform others in the Bivariate game.","url_abs":"http://arxiv.org/abs/1803.03916v1","url_pdf":"http://arxiv.org/pdf/1803.03916v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-reinforcement-learning-for-time-series","repo_url":"https://github.com/golsun/deep-RL-time-series","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-reinforcement-learning-for-time-series","repo_url":"https://github.com/siddharthnishtala/DQN-Trader","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}