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.

基于深度学习的财务方法已受到投资者和研究人员的关注。这项研究演示了如何使用三个框架基于深度强化学习来优化投资组合，资产分配和交易系统。在提出的深度学习结构中，首先通过小波变换（WT）分解输入数据，以消除股价时间序列数据中的噪声。然后，仅将母小波（高频）数据用作输入。其次，使用高频数据进行强化学习。强化学习网络采用长短期记忆（LSTM）。动作由LSTM网络确定或随机确定。第三，它利用给定交易的行为和适当的奖励来学习最优投资交易系统。所提出的深度强化学习结构所获得的最佳投资交易系统的结构可以提高交易性能，而无需构建预测模型。为了研究拟议结构的性能，我们将S＆P500，DJI和KOSPI200指数应用于拟议结构（HW_LSTM_RL）和其他强化学习结构进行比较。我们评估了不同测试时期（一到三年）和不同奖励的夏普比率的差异。使用分解后的高频数据作为输入，针对高度波动的市场改善了投资交易组合。在深度强化学习中，我们发现网络组成和适当的奖励对金融时间序列数据中的学习交易有重大影响。从而，