Reinforcement learning with teacher-student framework in future market

Sihang Chen; Weiqi Luo; Chao Yu

doi:10.1117/12.2626423

21 December 2021 Reinforcement learning with teacher-student framework in future market

Sihang Chen, Weiqi Luo, Chao Yu

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560A (2021) https://doi.org/10.1117/12.2626423
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Based on computer science, mathematics, and statistics, quantitative trading models, especially the artificial intelligence-based models, are widely used in the financial field. These models show their profitability in securities markets. Many researchers use supervised learning methods to predict the price trend and generate long (short) signals. However, the accuracy that supervised learning methods focus on is at odds with the main purpose of the quantitative models to achieve excess returns. To solve this problem, we construct a reinforcement learning-based approach and introduce the Markov decision process to facilitate the market’s operating mechanism. Moreover, we calculate more than 100 high-frequency factors to enhance the perception ability of the model. To shorten the exploration process in the training stage, we design a teacher-student framework by utilizing the prior experiences and then use both teacher-environment and agent-environment interacting samples to calculate the temporal difference error (TD-error). This error is used to optimize the model. To measure the practicality of the proposed model, we use the back-test method and build up simulated CSI 300 and CSI 500 markets. Three commonly used technical analysis-based methods and two reinforcement learning-based methods are compared with the proposed one.

Citation Download Citation

Sihang Chen, Weiqi Luo, and Chao Yu "Reinforcement learning with teacher-student framework in future market", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560A (21 December 2021); https://doi.org/10.1117/12.2626423

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