用xLSTM增强强化学习，让自动炒股更稳更赚钱。
A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
- 用xLSTM替代LSTM，更好捕捉股市长期变化趋势。
- 在科技股数据上，收益和夏普比率均优于传统LSTM方法。
- 适合对量化交易和深度强化学习感兴趣的研究者。
传统LSTM网络在处理序列数据时表现良好，但存在梯度消失、难以捕捉长期依赖等问题，影响其在动态高风险股市环境中的表现。本文探索将新型扩展型长短期记忆网络（xLSTM）与深度强化学习（DRL）结合，用于自动股票交易。所提方法在策略网络（actor）和价值网络（critic）中均采用xLSTM，有效处理时间序列数据与动态市场环境。使用近端策略优化（PPO）算法，在主要科技公司金融数据上进行实验，结果表明，基于xLSTM的模型在累计收益、单笔平均盈利、最大盈利比率、最大回撤及夏普比率等关键指标上均优于基于LSTM的方法。这表明xLSTM在提升DRL驱动的股票交易系统性能方面具有潜力。
原文摘要 · Abstract (English)
Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short Term Memory (xLSTM) network in combination with a deep reinforcement learning (DRL) approach for automated stock trading. Our proposed method utilizes xLSTM networks in both actor and critic components, enabling effective handling of time series data and dynamic market environments. Proximal Policy Optimization (PPO), with its ability to balance exploration and exploitation, is employed to optimize the trading strategy. Experiments were conducted using financial data from major tech companies over a comprehensive timeline, demonstrating that the xLSTM-based model outperforms LSTM-based methods in key trading evaluation metrics, including cumulative return, average profitability per trade, maximum earning rate, maximum pullback, and Sharpe ratio. These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。