博碩士論文 110225024 詳細資訊




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姓名 李濬紘(Chun-Hung Lee)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 基於Q-learning與非監督式學習之交易策略
(A Trading Strategy Based on Q-learning and Unsupervised Learning)
相關論文
★ Q學習結合監督式學習在股票市場的應用
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2026-7-1以後開放)
摘要(中) 在股票交易中,根據不同情況設計一個盈利的交易策略是一個重大挑戰。近年來,人工智能的發展為股票市場帶來了新的投資方法。Q-learning,一種強化學習演算法,可以幫助投資者學習市場趨勢並提供更合理的投資決策。在Q-learning中,狀態的制定尤其重要,因為不同的制定方法會影響其表現。本文提出了一種基於非監督式學習的數據驅動方法來設置Q-learning所需的狀態,將多維度的股票市場資料作為特徵,並藉由動態時間校正(DTW) 與 t-SNE 來找尋所需狀態。本文以台灣股市為例,建構單一資產的Q-learning投資決策,並相應地提出了一個由多個資產組成的適當投資組合。實證結果顯示,所提出的方法提供了不錯的投資表現。
摘要(英) Designing a profitable trading strategy based on different situations is a major challenge in
stock trading. In recent years, the development of artificial intelligence has brought new investment methods to the stock market. Q-learning, a reinforcement learning algorithm, can
help investors to learn market trends and recommend more reasonable investment decisions.
In Q-learning, the formulation of states is particularly important since different formulation methods can affect its performance. We propose a data-driven approach based on a
non-supervised learning method to set the states required in Q-learning. By utilizing multidimensional stock market data as features and leveraging Dynamic Time Warping (DTW) and t-SNE, the proposed approach efficiently identifies the desired states for Q-learning. In this work, using the Taiwan stock market as an example, we obtain the Q-learning investment decision of a single asset and propose an appropriate investment portfolio consisting of multiple assets accordingly. The empirical results reveal that the proposed method provides a satisfactory investment performance.
關鍵字(中) ★ 動態時間校正
★ 非監督式學習
★ 投資組合選擇
★ Q-learning
★ t-SNE
關鍵字(英)
論文目次 1 Introduction 1
2 Review 3
2.1 Markov Decisions Process 3
2.2 Q-learning 4
2.3 t-Distributed Stochastic Neighbor Embedding 5
2.4 Dynamic Time Warping-based t-SNE 7
3 Methods 9
3.1 Data Pretreatment 10
3.2 Cluster to represent the state for Q-learning 11
3.3 Portfolio based on Q-learning 14
4 Experiment and results 17
4.1 Datasets 17
4.2 Experiment setting 18
4.3 Experimental results 19
5 Conclusion and discussion 28
Reference 30
A Preliminary clustering for other 16 stocks 32
B Q-learning performance for other 16 stocks 35
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指導教授 黃士峰 王紹宣 審核日期 2023-7-26
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