Agent Performing Autonomous Stock Trading under Good and Bad Situations
Yunfei Luo Zhangqi Duan
Stock trading is one of the popular ways for financial management. However,
the market and the environment of economy is unstable and usually not
predictable. Furthermore, engaging in stock trading requires time and effort to
analyze, create strategies, and make decisions. It would be convenient and
effective if an agent could assist or even do the task of analyzing and
modeling the past data and then generate a strategy for autonomous trading.
Recently, reinforcement learning has been shown to be robust in various tasks
that involve achieving a goal with a decision making strategy based on
time-series data. In this project, we have developed a pipeline that simulates
the stock trading environment and have trained an agent to automate the stock
trading process with deep reinforcement learning methods, including deep
Q-learning, deep SARSA, and the policy gradient method. We evaluate our
platform during relatively good (before 2021) and bad (2021 - 2022) situations.
The stocks we've evaluated on including Google, Apple, Tesla, Meta, Microsoft,
and IBM. These stocks are among the popular ones, and the changes in trends are
representative in terms of having good and bad situations. We showed that
before 2021, the three reinforcement methods we have tried always provide
promising profit returns with total annual rates around $70\%$ to $90\%$, while
maintain a positive profit return after 2021 with total annual rates around 2%
to 7%.