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Nevertheless, due to the high complexity of massive, nonlinear, and nonindependent data and the difficulties and time consumption of complicated training models of deep learning, the performance of online trading decisions is still inadequate for practical application. This paper proposes the Integrated Framework of Forecasting Based Online Trading Strategy (IFF-BOTS) to satisfy better prediction performance and dynamic decisions for real-world online trading systems. Our method adopts a novel isomorphic convolutional neural network (CNN)-based forecaster-classifier-executor architecture to exploit CNN-based price and trend integrated prediction and direct-reinforcement-learning-based trading decision-making. IFF-BOTS can also achieve better real-time performance for online trading. We empirically compare the proposed approach with state-of-the-art prediction and trading methods on real-world S&amp;P and DJI datasets. The results show that the IFF-BOTS outperforms its competitors in predicting metrics, trading profits, and real-time performance.<\/jats:p>","DOI":"10.3233\/ida-216142","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T11:43:50Z","timestamp":1657626230000},"page":"893-909","source":"Crossref","is-referenced-by-count":12,"title":["An efficient isomorphic CNN-based prediction and decision framework for financial time series"],"prefix":"10.1177","volume":"26","author":[{"given":"Zhongming","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Economics, Xihua University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qibin","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Shan","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-216142_ref2","doi-asserted-by":"crossref","unstructured":"N. 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Saad, D.V. Prokhorov and D.C. Wunsch, II, Comparative Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks, IEEE Transactions on Neural Networks 9(6) (1998), 1456\u20131470.","DOI":"10.1109\/72.728395"},{"key":"10.3233\/IDA-216142_ref25","doi-asserted-by":"crossref","unstructured":"S. Selvin, R. Vinayakumar, E.A. Gopalakrishnan, V.K. Menon and K.P. 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