Prophet 알고리즘을 활용한 가상화폐의 자동 매매 프로그램 개발
Cryptocurrency Auto-trading Program Development Using Prophet Algorithm
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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.
Abstract
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.
참고문헌 (15)
- Ammer, M. A., "Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices:Increasing Investment Awareness", ELECTRONICS, 11(15), pp.2349~ (2022)
- "Binance"[URL]
- Hochreiter, S., "Long short-term memory", Neural Computation, 9(8), pp.1735~1780 (1997)
- "HYPERCONNECT"[URL]
- Ilonen, J., "Differ-ential evolution training algorithm for feed-forward neural networks", Neural Processing Letters, 17, pp.93~105 (2003)
- 진영훈, "주성분 분석 기법을 활용한 시계열 데이터 분석 및 예측 시스템", 한국융합학회논문지, 12(11), pp.99~107 (2021), 한국융합학회[DOI]
- Kim, H. Y., "Forecasting the volatility of stock price index : A hybrid model integrating LSTM with multiple GARCH-type models", Expert Systems with Applications, 103, pp.25~37 (2018)
- 김선웅, "딥러닝을 이용한 비트코인 투자전략의 성과 분석", 한국융합학회논문지, 12(4), pp.249~258 (2021), 한국융합학회[DOI]
- 김선웅, "기계학습 알고리즘을 이용한 알트코인의 가격 예측 성과", 디지털콘텐츠학회논문지, 24(1), pp.141~151 (2023), 한국디지털콘텐츠학회
- Lee, K. H., "A news event driven approach for LSTM-based stock index volatility forecasting", master’s, pp.2020
- Livieris, I. E., "A CNN–LSTM model for gold price time-series forecasting", Neural Computing and Applications, 32, pp.17351~17360 (2020)
- "Naver blog"[URL]
- "Understanding LSTM networks" (2015)[URL]
- Sean, J., Taylor, "Forecasting at scale", The American Statistician, 72(1), pp.37~45 (2018)
- 송한진, "AI의 LSTM기법을 이용한 금융시계열 데이터 변동성 예측방법 연구", 한국지식정보기술학회 논문지, 14(6), pp.665~673 (2019), 한국지식정보기술학회[DOI]