群組交易策略組合最佳化技術之研究

A Study on Group Trading Strategy Portfolio Optimization Techniques

陳諭萱(Yu-Hsuan Chen)

指導教授 ： 陳俊豪

淡江大學/工學院/資訊工程學系資訊網路與多媒體碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00196

摘要

在股票市場中，如何找出合適的買賣時機使得投資標的可獲利並降低風險是投資人重視的議題。為解決此問題，透過基本面、技術面或籌碼面指標組合而成的交易策略常用於決定標的買賣時機。由於技術面指標直接與股價相關且容易使用，故本論文主要著重於如何利用技術指標建立有效之交易策略。回顧文獻，相關研究議題包含；交易策略的制定、技術指標的參數最佳化、交易策略組合等。因現有技術所提供的交易策略組合有其限制，為了提昇交易策略組合的使用彈性與有效性，本論文首先定義群組交易策略組合最佳化問題，進而利用群組遺傳演算法設計兩群組交易策略組合最佳化方法。
在方法一中，每個群組交易策略組合透過群組、策略與資金權重三部分進行染色體編碼。每一染色體則透過利潤、風險、群組平衡與權重平衡四因子進行適合度評估。實驗透過上漲趨勢、盤整趨勢及下跌趨勢的資料集搭配兩交易策略集合與停損停利點進行有效性評估，實驗結果證實所提的方法可提供有效的群組交易策略組合。接著，因方法一實驗顯示使用停損停利點可提升利潤與降低風險，又設定合適的停損停利點是最佳化問題，故方法二在編碼上，除了使用群組、策略與資金權重，額外增加停損停利部分進行染色體編碼，之後透過群組遺傳演算法找出最佳之群組交易策略組合與其合適的停損停利點。實驗結果亦指出利用方法二所得的群組交易策略組合報酬明顯優於方法一。最後，本論文將所提的方法應用至群組股票投資組合上，實驗數據也顯示加入群組交易策略組合能有效降低股票投資組合風險且能的到更穩定的報酬。

關鍵字

群組策略交易組合 ； 群組遺傳演算法 ； 投資組合優化 ； 交易策略 ； 交易策略組合 ； 停損停利點

並列摘要

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.

並列關鍵字

Group trading strategy portfolio ； grouping genetic algorithm ； portfolio optimization ； trading strategy ； trading strategy portfolio ； stop-loss and take-profit points.

參考文獻

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