Market Profile with Convolutional Neural Networks: Learning the Structure of Price Activities

Many studies tried to apply machine learning (ML) methods to forecast the financial time series during the past decade. Moreover, the emergence of deep learning has led to another researches with results that significantly outperform previous models. However, most of the deep learning researches used the raw financial time series price data which consists of open, close, high and low (OCHL) as learning features. According to statistics, Long Short Term Memory networks (LSTM) is the first choice to deal with the forecasting problems with OCHL datasets due to its feedback connection networks, resulting higher performances for price series prediction. Meanwhile, Convolutional Neural Networks (CNN) has increased its popularity since it outperforms traditional ML models in classification problems. In this paper, there are three types of future trend that are the ultimate targets to be discovered. Nevertheless, OCHL features may be too sensitive to learn the large future trend in financial time series. This study proposes a novel approach: Convolutional Neural Networks with Market Profiles (CNN-MPs) which includes (1) adapting Market Profile to covert financial time series data to grey-scale image method, (2) generating two types of learning images: stacked and sequential profile that can keep the interaction between continuous profiles, and (3) learning the structure of price activities with CNN. Market Profile is a concept that has been widely used in the financial decision-making by comparing the current price with the market fair value. In addition, the trend is well established at the accepting movement of fair value which can be confirmed from the structure of profiles. In experiments, one of the popular commodities, corn was selected to evaluate the proposed method. And the experimental results show that proposed sequential profile method obtained 17% higher accuracy and more profitability than LSTM networks and other methods. Therefore, the proposed CNN-MPs method can effectively discover the trend of corn providing those who need import corn with a reference.