Decision Support System for Real-Time Trading based on On-Line Learning and Parallel Computing Techniques

Abstract

A novel intraday algorithmic trading strategy is developed based on various machine learning techniques and paralleled computing architectures in this paper. The proposed binary classification framework can predict the price trends of Taiwan stock index futures after thirty minutes. Traditional learning-based approaches collect all samples during the training period as the learning materials. The major contribution of this paper is to collect a subset of similar historical financial data to train the real-time trading model. This goal can be achieved by an on-line learning technique which is required to calculate an accurate model with training time limitation. In addition, the proposed joint-AdaBoost algorithm is to improve the system performance based on the concept of paired feature learning and planar weak classifier design. The core execution components in this algorithm can be further accelerated with the aid of Open Computing Language (OpenCL) parallel computing platform. The experimental results show that the proposed learning algorithm can improve the prediction accuracy of final classifier from 53.8% to 61.68%. Compared to the pure CPU implementation, the OpenCL version which uses CPU and GPGPU simultaneously can reduce the calculation time around 83.02 times. The efficiency improvement can decrease the delay of investment opportunity which is a critical issue in real-time financial decision support system application. To sum up, this paper proposed a novel learning framework based on joint-AdaBoost algorithm with similar learning samples and OpenCL parallel computation. The extended financial decision support system is also proven to work effectively and efficiently in our simulation experiments to trade the Taiwan stock index futures.