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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16138
Title: | 使用遞迴式增強學習法建立股價指數期貨交易策略 Using Recurrent Reinforcement Learning To Set up Stock Index Futures Trading Strategies |
Authors: | Chun-Jie Yang 楊鈞傑 |
Advisor: | 呂育道 |
Keyword: | 臺股期貨,機器學習,遞迴式增強學習法,梯度陡升法, Taiwan stock index futures,machine learning,recurrent reinforcement learning,gradient ascent, |
Publication Year : | 2012 |
Degree: | 碩士 |
Abstract: | 本研究利用Moody and Wu提出的遞迴式增強學習法(RRL)來建立一個臺灣股價指數期貨的交易策略。RRL在資料訓練期極大化夏普指數,並在資料檢測期評估系統的交易績效。在系統設計上,我們設計了4種實驗組合,包含了兩段不同期間的訓練資料,以及是否設置停損點以及停利點的分析。
為檢測此學習法的可行性,我們採用2001年1月2日至2008年12月31日之臺股期貨歷史資料進行學習訓練及資料檢測。 This thesis adopts recurrent reinforcement learning (RRL) proposed by Moody and Wu to establish trading strategies for Taiwan Stock Index Futures. RRL system evaluates the performance in terms of cumulative profit by maximizing Sharpe’s ratio during the training period. We design 4 training window-trading strategy combinations, which consist of 2 sets of historical stock data from different periods. We also discuss the differences when both maximum acceptable loss and minimum acceptable profit are given. To verify our RRL algorithm, we use real historical stock data for backtesting and examine the performance of our trading strategies. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16138 |
Fulltext Rights: | 未授權 |
Appears in Collections: | 資訊工程學系 |
Files in This Item:
File | Size | Format | |
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ntu-101-1.pdf Restricted Access | 711.29 kB | Adobe PDF |
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