Classifying Sequences of Market Profile using Deep Learning - Dave, Pranshu Rupesh; Sajja, Priti Srinivas (International Journal of Computer Sciences and Engineering, 2018)
Since its inception, market profile has been used by traders as a way to assess the market value of a stock. By reading market profile charts, it is possible for traders to assess who is driving the market (buyers or sellers) and make trades accordingly. The spatiotemporal feature of market profile can be used to train a deep learning model for classifying sequences of market profile. This is a novel idea and one that needs to be examined and experimented upon. LSTM networks are structures capable of remembering long term dependencies in time series data. Convolutional Neural Networks, on the other hand help in figuring out patterns in multidimensional data. A python library is built to generate market profile from time series data. Leveraging the power of LSTMs and CNNs, two models are proposed for the classification: FC-LSTM and ConvLSTM. The results show that the proposed models are able to catch patterns amongst profiles and FC-LSTM performs better than ConvLSTM on this task.