臺灣博碩士論文加值系統

台指期貨的報酬結構是非線性，有文獻支持台指期貨呈現弱勢效率市場假說。投資人期待在不同的風險擔承與期望報酬下，找出獲利穩定、風險小的最適交易策略。本研究採用樞軸點指標(Pivot Point)進行台指期貨交易研究。交易模式以當沖、隔日沖操作進行比較，藉由Pivot Point的壓力線與支撐線來避開盤整的股市，運用順勢策略進行交易，以突破壓力線、跌破支撐線來判斷強勁的趨勢，追隨趨勢並希望從中賺取淨利。交易週期是否可以適用不同週期1、15、30、45、60、120分K以及移動窗格則以一年測一年、三年測一年、五年測一年，進行穩定度分析。運用遺傳演算法最佳化停損點、停利點，加上建立程式交易下單環境，有紀律的依停損、停利進行交易進出的準則，希望能降低人為不理性及交易時間帶來的風險。研究結果顯示樞軸點指標(Pivot Point)能有穩定的獲利能力，交易週期以30分K作為進場判斷、移動窗格以三年測一年表現最佳。交易模式由當沖變成隔日沖，能有提高報酬的效果，但相對的風險也跟著提高，所以用遺傳演算法設定停損點、停利點，觸及設定價格立馬平倉，加入停損、停利能在報酬稍微減少的情形下有效的減少風險。

The return structure of TXF is nonlinear, and some literatures support that TXF presents Weak Form Efficient Market Hypothesis. Investors expect to find out the most proper trade strategy with stable profits and small risks under different risk undertaking and the expected return. Pivot Point index is used in this research for the research of TXF trade. The trade mode is to compare day trading with overnight trading. Resistance line and support line of Pivot Point are used to avoid the consolidated stock market, and trend trading strategy is used for trading so as to break through the resistance line and fall below the support line, in order to judge the strong trend and follow behind the trend to earn net profit. The trade cycle can be applicable to different cycles of 1, 15, 30, 45, 60 and 120 Point K and moving pane is to test one year with one year, test one year with three years and test one year with five years for the analysis of stability. Genetic algorithm is used to set optimized the stop-loss point and lock-in-gain point, program trade order environment is set up. The guideline of carrying out the trade based on the stop-loss point and lock-in-gain point is strictly followed, hoping to reduce the risks brought about by irrational trading and trading time. The research results suggest that Pivot Point can have a stable profitability, and the entrance should be judged by the trade cycle of 30 Point K. For Walk Forward Analysis, the testing of one year with three years has the best performance. The trading mode is changed from day trading to overnight trading, which can improve the effect of return. However, the relative risk is also increased. Thus, genetic algorithm is used to set the stop-loss point and lock-in-gain point. Close position is taken when reaching the set price, and the stop-loss point and lock-in-gain point are added. This can effectively reduce risks under the situations where return is reduced slightly.

目錄
摘要 I
Abstract II
目錄 IV
圖目錄 V
表目錄 VI
第一章 緒論 1
第一節 研究背景 2
第二節 研究動機 3
第三節 研究目的 4
第二章 文獻回顧 5
第一節 遺傳演算法 5
第二節 趨勢交易 11
第三節 技術指標 13
第四節 程式交易 16
第三章 研究方法 18
第一節 研究架構 18
第二節 交易策略Pivot Point 20
第三節 移動窗格 24
第四節 停損、停利參數設定 25
第五節 績效評估 30
第四章 資料分析結果 33
第一節 研究資料 33
第二節 交易相關費用 34
第三節 實驗環境 37
第四節 實驗設計 40
第五章 結論 52
附錄 53
參考文獻 54

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