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.

翻译：尽管有效市场假说存在，但许多研究表明股票市场存在非效率性，这推动了获取超额市场回报技术的发展。近几十年来，系统化交易取得了显著进展，深度学习方案作为分析和预测市场行为的强大工具崭露头角。本文提出一种受专业技术分析师交易方式启发的方法。该方案通过分析过去600天的股票价格，预测未来D天内股价将上涨或下跌10%或20%。此外，所提方法利用ResNet（一种深度学习模型）的跳跃连接和logits来提高预测概率。模型使用韩国和美国股市的历史数据进行训练和测试。研究表明，使用周期标签5可获得最佳结果。在韩国市场，该方法实现了高于市场回报率39%以上的利润；在美国市场，则实现了高于市场回报率40%以上的利润。