用强化学习判断买股票的最佳时机，可扩展至卖点决策。
Reinforcement Learning for Stock Transactions
- 构建马尔可夫决策过程，以真实数据训练买点策略。
- 对比多种强化学习算法，发现深度Q-learning收敛性更好。
- 适合对量化交易和RL应用感兴趣的开发者或研究者。
大量研究致力于分析股票市场。若能从交易的混乱中识别出规律，便可能从中获利。本项目旨在应用强化学习（RL）确定给定时间范围内买入股票的最佳时机，稍作调整即可用于识别卖出时机。为利用公开的真实世界数据训练模型，我们自定义了马尔可夫决策过程（MDP）问题，参考文献[5][6]设计状态空间与奖励机制。我们训练了多组代理，包括Q-learning、带线性函数逼近的Q-learning以及深度Q-learning。此外，还尝试使用机器学习回归与分类模型预测股价。通过比较各代理的策略收敛情况，评估其在股票市场中最大化利润的能力。
原文摘要 · Abstract (English)
Much research has been done to analyze the stock market. After all, if one can determine a pattern in the chaotic frenzy of transactions, then they could make a hefty profit from capitalizing on these insights. As such, the goal of our project was to apply reinforcement learning (RL) to determine the best time to buy a stock within a given time frame. With only a few adjustments, our model can be extended to identify the best time to sell a stock as well. In order to use the format of free, real-world data to train the model, we define our own Markov Decision Process (MDP) problem. These two papers [5] [6] helped us in formulating the state space and the reward system of our MDP problem. We train a series of agents using Q-Learning, Q-Learning with linear function approximation, and deep Q-Learning. In addition, we try to predict the stock prices using machine learning regression and classification models. We then compare our agents to see if they converge on a policy, and if so, which one learned the best policy to maximize profit on the stock market.
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