Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment#
2. Affiliation:#
Di Chen is from Cornell University
3. Keywords:#
cost-efficient, sample efficiency, optimal trade execution, dynamic market
4. Urls:#
5. Summary:#
(1): The article addresses the challenge of achieving efficient trade execution while considering market impact in a dynamic market environment.
(2): Previous works used static historical data to train RL agents, which proved to be ineffective in dynamic markets due to their inability to account for market impact. This paper presents a cost-efficient RL approach called D3Q that integrates deep reinforcement learning and planning to improve trading performance while reducing training overhead. The approach is designed to counteract the non-increasing residual inventory and solve exploration bias. The motivation and merits of the approach are well-established through extensive experiments.
(3): The proposed methodology includes a learnable market environment model that approximates market impact using real market experience. The model enhances environment policy learning. A novel state-balanced exploration scheme is also introduced to counteract the non-increasing residual inventory.
(4): The performance of the proposed D3Q framework was evaluated on a benchmark dataset, and the results showed that it significantly enhances sample efficiency while improving the average trading cost. The proposed method outperforms state-of-the-art methods in the field, making it a promising direction for future research.
6. Methods:#
(1): The article presents a cost-efficient reinforcement learning approach called D3Q that integrates deep reinforcement learning and planning to improve trading performance while reducing training overhead. The approach is designed to counteract the non-increasing residual inventory and solve exploration bias.
(2): The proposed methodology includes a learnable market environment model that approximates market impact using real market experience. This model enhances environment policy learning. A novel state-balanced exploration scheme is also introduced to counteract the non-increasing residual inventory.
(3): The D3Q framework is evaluated on a benchmark dataset with input parameters including learning rates, exploration probability, target network soft update ratio, maximum inventory level, planning update frequency and trading period. The testing results show that the D3Q method significantly enhances sample efficiency while improving the average trading cost, outperforming state-of-the-art methods in the field.
7. Conclusion:#
(1): This work proposes a cost-efficient reinforcement learning approach called D3Q for optimal trade execution in dynamic market environments. The proposed method significantly improves the average trading cost and enhances the sample efficiency while outperforming state-of-the-art methods in the field. The paper establishes the importance of considering market impact in dynamic markets and presents a novel way to address it through deep reinforcement learning and planning.
(2): Innovation point: The proposed approach integrates deep reinforcement learning and planning to improve trading performance while reducing training overhead. The learnable market environment model and state-balanced exploration scheme address the issue of market impact and non-increasing residual inventory.
Performance: The D3Q framework significantly enhances sample efficiency and improves average trading cost compared to state-of-the-art methods in the field. However, the proposed approach is only evaluated on a benchmark dataset and may need further testing on more complicated scenarios in real markets.
Workload: The article provides thorough explanations of the proposed approach and includes detailed experimental results to support the claims. However, some technical details may be challenging to follow for readers without prior knowledge in reinforcement learning and trading.