Using Data Augmentation Based Reinforcement Learning for Daily Stock Trading

Clicks: 261
ID: 111147
2020
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Abstract
In algorithmic trading, adequate training data set is key to making profits. However, stock trading data in units of a day can not meet the great demand for reinforcement learning. To address this problem, we proposed a framework named data augmentation based reinforcement learning (DARL) which uses minute-candle data (open, high, low, close) to train the agent. The agent is then used to guide daily stock trading. In this way, we can increase the instances of data available for training in hundreds of folds, which can substantially improve the reinforcement learning effect. But not all stocks are suitable for this kind of trading. Therefore, we propose an access mechanism based on skewness and kurtosis to select stocks that can be traded properly using this algorithm. In our experiment, we find proximal policy optimization (PPO) is the most stable algorithm to achieve high risk-adjusted returns. Deep Q-learning (DQN) and soft actor critic (SAC) can beat the market in Sharp Ratio.
Reference Key
yuan2020electronicsusing Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuyu Yuan;Wen Wen;Jincui Yang;Yuan, Yuyu;Wen, Wen;Yang, Jincui;
Journal Electronics
Year 2020
DOI
10.3390/electronics9091384
URL
https://www.mdpi.com/2079-9292/9/9/1384/htm
Keywords
data augmentation deep reinforcement learning algorithmic trading access mechanism

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