Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

1. 서 론
2. 이론적 배경
    2.1 RNN(Recurrent Neural Network)
    2.2 LSTM(Long Short-Term Memory)
    2.3 Prophet
3. 실험 및 결과 분석
    3.1 예측 정확도 비교 분석
    3.2 예측 값을 활용한 가상화폐의 자동매매 프로세스설계
    3.3 자동매매 실험 결과
4. 결 론
References

• Hyun-Sun Kim(Department of Investment Information Engineering, Yonsei University) | 김현선 (연세대학교 투자정보공학 협동과정)
• Jae Joon Ahn(Division of Data Science, Yonsei University) | 안재준 (연세대학교 데이터사이언스학부) Corresponding Author