Learning to trade in financial time series using high-frequency through wavelet transformation and deep reinforcement learning

Jimin Lee, Hayeong Koh, Hi Jun Choe

Research output: Contribution to journalArticlepeer-review

Abstract

Deep learning-based financial approaches have received attention from both investors and researchers. This study demonstrates how to optimize portfolios, asset allocation, and trading systems based on deep reinforcement learning using three frameworks. In the proposed deep learning structure, the input data are first decomposed through wavelet transformation (WT) to remove noise from stock price time-series data. Then, only the mother wavelet (high-frequency) data are used as input. Second, reinforcement learning is performed using the high-frequency data. The reinforcement learning network employs long short-term memory (LSTM). Actions are determined by the LSTM network or randomly. Third, it learns the optimal investment trading system using the actions of a given transaction and appropriate rewards. The structure of the optimal investment trading system obtained by the proposed deep reinforcement learning structure improves trading performance without requiring the construction of a predictive model. To investigate the performance of the proposed structure, we applied the S&P500, DJI, and KOSPI200 indices to the proposed structure (HW_LSTM_RL) and other reinforcement learning structures for comparison. We evaluated the difference in Sharpe ratio for various test periods (one to three years) and for different rewards. Using the decomposed high-frequency data as input, a portfolio of investment transactions was improved for highly volatile markets. In deep reinforcement learning, we found that network composition and appropriate rewards have significant influence on learning transactions in financial time-series data. Thus, the proposed HW_LSTM_RL structure demonstrates the importance of input data composition, learning network settings, and rewards.

Original languageEnglish
JournalApplied Intelligence
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Funding Information:
We thank an anonymous referee for their helpful comments, which improved the clarity of the paper. This research was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (Grant No. 2015R1A5A1009350, 2018R1A2A3074566, and 2020R1I1A1A01071731). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2019R1A6A1A11051177).

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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