Decision Making Process of Stock Trading Implementing DRQN And ARIMA

The approach to collect realistic trading signals throughout the transaction process to broaden advantages is a long-studied issue. The rapid expansion and dynamic character on stock markets is a major issue for the financial sector, as conventional trade tactics developed by experienced financial professionals do not generate sufficient performance under all market situations. In most previous studies, Machine Learning and Deep Learning have been based on price estimation techniques, yet few studies have shown decisions based on stock trading. To solve this difficulty, adaptive stock trading strategies are suggested with profound techniques of deep reinforcement learning. This study exhibits the implementation of Deep Recurrent Q-Learning (DRQN) and Autoregressive Integrating Moving Average (ARIMA) on stock trading with predicting closing value of stock that helps for strategic decision-making from a stock market by acknowledging risk to buy, hold and sell with profit calculation. This method was applied on 15 Nasdaq stock datasets and overcomes the limitation of recent developed reinforcement learning methods. The proposed fusion of DRQN and ARIMA based strategy displays robust result which helps to take better decision for stock trading with visualizing experimental outcomes.