Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning
This work addresses automated trading for financial markets by improving returns and reducing risk, though it appears incremental as it builds on existing DRL methods with feature extraction enhancements.
The paper tackled the problem of generating asset-specific trading rules from historical data using deep reinforcement learning, achieving a total return of 262% over two years on a specific asset compared to 78% from the best existing model.
Generating asset-specific trading signals based on the financial conditions of the assets is one of the challenging problems in automated trading. Various asset trading rules are proposed experimentally based on different technical analysis techniques. However, these kind of trading strategies are profitable, extracting new asset-specific trading rules from vast historical data to increase total return and decrease the risk of portfolios is difficult for human experts. Recently, various deep reinforcement learning (DRL) methods are employed to learn the new trading rules for each asset. In this paper, a novel DRL model with various feature extraction modules is proposed. The effect of different input representations on the performance of the models is investigated and the performance of DRL-based models in different markets and asset situations is studied. The proposed model in this work outperformed the other state-of-the-art models in learning single asset-specific trading rules and obtained a total return of almost 262% in two years on a specific asset while the best state-of-the-art model get 78% on the same asset in the same time period.