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, known as alpha. Systematic trading has undergone significant advances in recent decades, with deep learning emerging as a powerful tool for analyzing and predicting market behavior. In this paper, we propose a model inspired by professional traders that look at stock prices of the previous 600 days and predicts whether the stock price rises or falls by a certain percentage within the next D days. Our model, called DeepStock, uses Resnet's skip connections and logits to increase the probability of a model in a trading scheme. We test our model on both the Korean and US stock markets and achieve a profit of N\% on Korea market, which is M\% above the market return, and profit of A\% on US market, which is B\% above the market return.

翻译：尽管有效市场假说存在，但许多研究指出股票市场存在非有效性，从而催生了获取超额收益（即alpha）的技术。系统化交易在近几十年取得了显著进展，深度学习作为分析和预测市场行为的强大工具逐渐崭露头角。本文提出了一种受专业交易员启发的模型，该模型通过观察过去600天的股价数据，预测未来D天内股价是否会上涨或下跌特定百分比。我们的模型DeepStock采用ResNet的跳跃连接（skip connections）与logits机制，以提高交易策略中模型的成功率。我们分别在韩国和美国股票市场上测试该模型，在韩国市场获得N%的利润（高于市场回报率M%），在美国市场获得A%的利润（高于市场回报率B%）。