A Real-Time Trading Decision Support System Based On Adaptive Machine Learning and Parallel Computing Techniques

• Main Authors: Hsiao, Chih-Yen, 蕭之彥
• Other Authors: Chen, An-Pin
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/87410602741485368045

碩士 === 國立交通大學 === 資訊管理研究所 === 104 === In This paper, we propose a novel idea to build a financial module by looking for similar data with return on investment (ROI), which was calculated by Taiwan stock price index futures minutes data. And our target is to predict the direction of the price change after thirty minutes. It will be different from the usual method which only input all the data from a period of time into the module. In Methodology, we focus on the weak classifier of the traditional Adaboost algorithm, to enhance to become Joint-Adaboost algorithm, based on the concept of paired feature learning, to exert the potential of the data classifier, therefore, the final classifier can be able to get the accuracy from 53.8% into 61.68%. However, in the experiments, with OpenCL to parallelized Joint-Adaboost algorithm, using the high performance graphic card will be 83.02 times faster than only using CPU, which minimized the calculation time, which might cause the delay by the complex calculation algorithms, to achieve our real-time goal. In the result, the algorithm have the abilities to create over 60% more accuracy and also accelerate the calculation speed. With the support on this study we can prove the algorithm module to be working properly and looking for similar data to build the module is meaningful, so the investors will have faith to build a whole real-time trading decision support system with us.