  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. 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 Korea and US stock markets. The backtest is
done using the data from 2020 to 2022. Using the proposed method for the Korea
market it gave return of 75.36% having Sharpe ratio of 1.57, which far exceeds
the market return by 36% and 0.61, respectively. On the US market it gives
total return of 27.17% with Sharpe ratio of 0.61, which outperforms other
benchmarks such as NASDAQ, S&P500, DOW JONES index by 17.69% and 0.27,
respectively.
