Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis

Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market leading to the development of techniques to gain above-market returns. Systematic trading has undergone significant advances in recent decades with deep learning schemes emerging as a powerful tool for analyzing and predicting market behavior. In this paper, a method is proposed that is inspired by how professional technical analysts trade. This scheme looks at stock prices of the previous 600 days and predicts whether the stock price will rise or fall 10% or 20% within the next D days. Plus, the proposed method uses the Resnet's (a deep learning model) skip connections and logits to increase the probability of the prediction. The model was trained and tested using historical data from both the Korean and US stock markets. We show that using the period label of 5 gives the best result. On Korea market it achieved a profit more than 39% above the market return, and a profit more than 40% above the market return on the US market.