Searching Optimal Trading Strategy based on Quantum-inspired Tabu Search Algorithm

• Main Authors: Jian-Wei Lin, 林建瑋
• Other Authors: Yao-Hsin Chou
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/23954351663179000110

碩士 === 國立暨南國際大學 === 資訊工程學系 === 103 === The investment is very important, because of inflation, low interest rates lead to reduced value of money. Owing to the return of stock investment is more than other kinds of investment. It became the financial market become more popular in recent years. However, the stock market is uncertain, complicated and difficult to predict. Hence, how to find the proper trading times for stock markets is a difficult problem. In this study, we use technical analysis in the trading price and trading volume, and proposes a new method to calculate the moving average. Owing to Quantum-inspired Tabu Search (QTS) algorithm can search and find the optimal efficiencies, so QTS is used to find the optimal trading time to establish a system of trading decisions. In addition, to avoid the over-fitting problem, we adopt the sliding window to dynamically adjust trading strategies with training data over time. The results show that the proposed method to overcome some situations, such as Taiwan stock market, US stock market. Consequently, the proposed approach having much more returns of investment (ROI) than other schemes.