论文标题
股票市场交易自动化的深入加强学习方法
Deep Reinforcement Learning Approach for Trading Automation in The Stock Market
论文作者
论文摘要
深度加强学习（DRL）算法可以扩展到以前棘手的问题。通过将财务资产价格“预测”步骤和投资组合的“分配”步骤相结合，可以在一个统一的过程中使用一个统一的过程，以生成能够与环境互动以通过试用和错误做出最佳决策，从而可以使用DRL自动化股票市场自动化。这项工作代表了DRL模型，以在股票市场产生盈利交易，从而有效克服了监督学习方法的局限性。我们将交易问题作为部分观察到的马尔可夫决策过程（POMDP）模型，考虑了股票市场施加的限制，例如流动性和交易成本。然后，我们使用双延迟的深层确定性策略梯度（TD3）算法解决了公式的POMDP问题，该算法报告了看不见的数据集（测试数据）的2.68 Sharpe比率。从股票市场预测和智能决策机制的角度来看，本文证明了DRL在金融市场上比其他类型的机器学习的优越性，并证明了其信誉和战略决策的优势。
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.
