用强化学习优化股市交易策略，考虑手续费影响。
Dueling Deep Reinforcement Learning for Financial Time Series
- 采用双网络和分离价值架构提升决策能力。
- 在有无佣金条件下均优于随机策略。
- 适合研究金融交易的AI算法开发者。
强化学习（RL）已成为解决动态环境中决策问题的强大范式。本研究探索了双深度Q网络（DDQN）与分层网络架构在使用历史标普500指数数据进行金融交易任务中的应用。重点在于训练能够优化交易策略并考虑实际约束（如交易成本）的智能体。模型在有无佣金的场景下均进行了评估，突显了成本敏感环境对奖励机制的影响。尽管存在计算限制和金融时间序列数据固有的复杂性，智能体仍成功学习到有意义的交易策略。研究结果表明，即使在小规模数据集上训练，结合DDQN与分层网络的强化学习智能体仍可超越随机策略。然而，由于数据源的复杂性，政策表现仍有待优化。
原文摘要 · Abstract (English)
Reinforcement learning (RL) has emerged as a powerful paradigm for solving decision-making problems in dynamic environments. In this research, we explore the application of Double DQN (DDQN) and Dueling Network Architectures, to financial trading tasks using historical SP500 index data. Our focus is training agents capable of optimizing trading strategies while accounting for practical constraints such as transaction costs. The study evaluates the model performance across scenarios with and without commissions, highlighting the impact of cost-sensitive environments on reward dynamics. Despite computational limitations and the inherent complexity of financial time series data, the agent successfully learned meaningful trading policies. The findings confirm that RL agents, even when trained on limited datasets, can outperform random strategies by leveraging advanced architectures such as DDQN and Dueling Networks. However, significant challenges persist, particularly with a sub-optimal policy due to the complexity of data source.
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