论文标题
深入学习在算法交易中的应用
An Application of Deep Reinforcement Learning to Algorithmic Trading
论文作者
论文摘要
该科学研究论文介绍了一种基于深度强化学习（DRL）的创新方法，以解决算法交易问题，以确定股票市场交易活动期间任何时间点的最佳交易位置。它提出了一种新颖的DRL交易策略，以最大程度地提高股票市场上的Sharpe比率绩效指标。该新的交易策略以Q-Network算法（TDQN）表示为交易的Q-Network算法（TDQN），这是从流行的DQN算法中启发的，并显着适应了当前的特定算法交易问题。训练由此产生的加固学习（RL）代理完全基于从有限的股票市场历史数据中产生的人工轨迹。为了客观地评估交易策略的绩效，研究论文还提出了一种新颖，更严格的绩效评估方法。遵循这种新的绩效评估方法，报告了TDQN策略的有希望的结果。
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.
