Reinforcement Learning for Stock Transactions

• URL: http://arxiv.org/abs/2505.16099v2
• Date: Sat, 24 May 2025 01:44:46 GMT
• Title: Reinforcement Learning for Stock Transactions
• Authors: Ziyi Zhou, Nicholas Stern, Julien Laasri,
• Abstract summary: We train a series of agents using Q-Learning, Q-Learning with linear function approximation, and deep Q-Learning.<n>We try to predict the stock prices using machine learning regression and classification models.
• Score: 1.9578448731837585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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