Author:
Wang Zhengyan,Zhao Yisong
Publisher
Springer Nature Singapore
Reference11 articles.
1. Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M.A., Mahmoodi, A., Jam, P.A.: New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the support vector machine and heuristic algorithms of imperialist competition and genetic. Expert Syst. Appl. 94, 21–31 (2018)
2. Cagliero, L., Garza, P., Attanasio, G., Baralis, E.: Training ensembles of faceted classification models for quantitative stock trading. Computing 102, 1213–1225 (2020)
3. Huang, B., Huan, Y., Xu, L., Zou, Z.: Automated trading systems statistical and machine learning methods and hardware implementation: a survey. Enterprise Information Systems 13, 132–144 (2019)
4. Abel, D., Dabney, W., Harutyunyan, A., Ho, M.K., Littman, M.L., et al.: On the expressivity of markov reward. Adv. Neural. Inf. Process. Syst. 34, 7799–7812 (2021)
5. An, B., Sun, S., Wang, R.: Deep reinforcement learning for quantitative trading: challenges and opportunities. IEEE Intell. Syst. 37(2), 23–26 (2022)