| Summary: | 碩士 === 淡江大學 === 資訊工程學系資訊網路與多媒體碩士班 === 106 === In stock markets, how to determine an appropriate trading time for buying or selling stocks to make the return and risk of them being maximized and minimized is always an important issue for investors. The common way to deal with this problem is using trading strategies formed by fundamental, technical or chip analysis. Since there is a direct correlation between technical indicators and stock prices and technical indicators are much easy to use, hence, this thesis focus on how to establish efficient trading strategies by technical indicators. Literatures showed that there are lots of research topics, for example, including how to form trading strategies, parameter optimization for trading strategies, and trading strategy portfolio optimization. Because the trading strategy portfolios provided by existing approaches have limitations, to increase the flexibility and effectiveness of them, firstly, this thesis defines the group trading strategy portfolio optimization problem. Then, two group trading strategy portfolio optimization approaches are proposed using the grouping genetic algorithm.
In the first approach, a group trading strategy portfolio is encoded into a chromosome using three parts, the grouping, trading strategy and weight parts. The fitness function composes of four factors that are profit, risk, group balance and weight balance is utilized to assess the quality of a chromosome. Experiments were conducted on the uptrend, sideway trend and downtrend datasets with two sets of trading strategies and stop-loss and take-profit points to evaluate the effectiveness of the proposed approach. The experimental results show that the proposed approach can provide useful group trading strategy portfolio. Because the results also indicated that the first approach with stop-loss and take-profit points can increase return and reduce risk and to set stop-loss and take-profit points is an optimization problem, the second approach thus use not only grouping, trading strategy and weight but also stop-loss and take-profit part to encode a group trading strategy portfolio. Then, the grouping genetic algorithm is employed to optimize a group trading strategy portfolio and get its appropriate stop-loss and take-profit points. Experimental results reveal that the return of the second approach is better than the first approach. At last, the proposed approaches are applied on a group stock portfolio. The results show that stock portfolio with the group trading strategy can actually increase its ability to reduce risk and get more stable profit.
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