A stock market trading framework based on deep learning architectures

Abstract

Market prediction has been a key interest for professionals around the world. Numerous modern technologies have been applied in addition to statistical models over the years. Among the modern technologies, machine learning and in general artificial intelligence have been at the core of numerous market prediction models. Deep learning techniques in particular have been successful in modeling the market movements. It is seen that automatic feature extraction models and time series forecasting techniques have been investigated separately however a stacked framework with a variety of inputs is not explored in detail. In the present article, we suggest a framework based on a convolutional neural network (CNN) paired with long-short term memory (LSTM) to predict the closing price of the Nifty 50 stock market index. A CNN-LSTM framework extracts features from a rich feature set and applies time series modeling with a look-up period of 20 trading days to predict the movement of the next day. Feature sets include raw price data of target index as well as foreign indices, technical indicators, currency exchange rates, commodities price data which are all chosen by similarities and well-known trade setups across the industry. The model is able to capture the information based on these features to predict the target variable i.e. closing price with a mean absolute percentage error of 2.54% across 10 years of data. The suggested framework shows a huge improvement on return than the traditional buy and hold method.