클라우드 컴퓨팅과 기계학습 기법을 이용한 주식의 기술적 분석 지표 최적화 및 주가 추세 변동 예측
The Optimization of Technical Analysis Indicators and Stock Trend Prediction Using Machine Learning and Cloud Computing
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초록
국내 주식 시장에서 트렌드 예측을 위한 기계학습 모델의 활용 사례가 점점 증가하고 있다. 특히, 주가 데이터와같은 복잡한 시계열 데이터를 분석하고 예측하기 위해서는 기계학습을 활용하는 것이 필수적이다. 본 연구에서는 클라우드 컴퓨팅 서비스를 활용한 금융 데이터 수집 및 금융 시계열 추세 예측을 위한 기계학습 시스템을 제안한다. 먼저, 데이터 수집을 위해 Amazon Web Services(AWS)의 서버리스 서비스를 활용하였으며, 기술적 분석 지표(Relative Strength Index(RSI), Simple Moving Average(SMA), 볼린저 밴드, Rate Of Change(ROC), Golden Cross and Dead Cross(GDC), Stochastic Oscillator(STOCH), Moving Average Convergence Divergence(MACD), Detrended Price Oscillator(DPO))의 임계치를 유전 알고리즘을 통해 최적화 하였다. 이후 최적화된 지표들을 Echo State Network(ESN), Recurrent Neural Network(RNN), 그리고 다양한 기계학습 분류 모델의 학습 데이터로 사용하여 각 종목의 추세를 예측하였다. 예측된 추세를 바탕으로 백테스트를 진행한 결과, 평균 수익률은 ESN이 334%, RNN이 175%, 그리고 분류 모델이 199%를 기록하였다. 따라서 본 연구는 국내 주식 투자에서도 기계학습이 높은 예측력을 보이며 다양한 활용 가능성을 지니고 있음을 시사 하였다.
Abstract
The application of machine learning models for trend prediction in the domestic stock market is increasing. In particular, utilizing machine learning is essential for analyzing and predicting complex time-series data, such as stock price data. This study proposes a machine learning system for financial time-series trend prediction, utilizing cloud computing services. First, for data collection, the serverless service of Amazon Web Services was employed, and the thresholds of technical analysis indicators were optimized through a genetic algorithm. The optimized indicators were then used as training data for Echo State Network, Recurrent Neural Network (RNN), and various machine learning classification models to predict the trend of each stock. Based on the predicted trends, backtesting was conducted, and the results showed that the average returns were 334% for ESN, 175% for RNN, and 199% for classification models. Therefore, this study suggests that machine learning exhibits high predictive power in domestic stock investment and holds various potential applications.
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