本文提出了一種預(yù)測外匯匯率序列的新方法。使用機器學(xué)習(xí)算法,尤其是深度Q學(xué)習(xí),通過從技術(shù)指標(biāo)中提取信息,發(fā)現(xiàn)外匯市場匯率變化的趨勢。通過使用諸如技術(shù)指標(biāo)之類的財務(wù)分析方法,與以前的工作相比,我們設(shè)法大大減少了神經(jīng)網(wǎng)絡(luò)中輸入特征的數(shù)量。這可以讓我們擁有更加輕松的網(wǎng)絡(luò),并具有相似的性能。為了優(yōu)化輸入信息,我們對技術(shù)指標(biāo)提供的信息進行了分析,并使用了聚類算法來消除不必要的信息.通過本文提出的策略,經(jīng)過驗證,預(yù)測出的結(jié)果準(zhǔn)確率優(yōu)于傳統(tǒng)的投資策略（例如“買入和持有”）,并可以獲得更好的回報。我們能夠預(yù)測市場的趨勢:我們的算法在一個下降的市場上占據(jù)了一個空頭,這意味著它成功預(yù)測了未來價格的下降趨勢。這里介紹的研究工作對如何有效預(yù)測市場價格的策略研究具有啟發(fā)性的意義,并且有助于解決構(gòu)建智能投資組合管理系統(tǒng)的問題。 

【文章來源】：清華大學(xué)北京市 211工程院校 985工程院校 教育部直屬院校

【文章頁數(shù)】：105 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
ABSTRACT
NOMENCLATURE
1.INTRODUCTION
    1.1 THE FOREIGN EXCHANGE MARKET
        1.1.1 A brief introduction to the Forex market
        1.1.2 Market mechanics
        1.1.3 Principal actors of the Forex market
        1.1.4 The Forex rates in global supply chain
    1.2 ALGORITHMIC TRADING
        1.2.1 The rise of electronic markets
        1.2.2 Trading system
        1.2.3 What is algorithmic trading?
        1.2.4 Different types of algorithmic trading
    1.3 MOTIVATIONS
    1.4 THESIS CONTENT
2.LITERATURE REVIEW
    2.1 MACHINE LEARNING
    2.2 MARKOV DECISION PROCESS
        2.2.1 Definition
        2.2.2 Bellman equation
        2.2.3 Solution
    2.3 REINFORCEMENT LEARNING
    2.4 Q-LEARNING
        2.4.1 Q-Learning implementation
        2.4.2 Deep Q-learning
    2.5 TECHNICAL ANALYSIS
        2.5.1 The“Moving Average Convergence Divergence”
        2.5.2 The Pivot Point
        2.5.3 Bollinger Bands
        2.5.4 Percent B
        2.5.5 Relative Strength Index
        2.5.6 On-Balance Volume
        2.5.7 Accumulation/ Distribution indicator
        2.5.8 Chaikin's Oscillator
        2.5.9 Sharpe Ratio
    2.6 NEW FEATURES
3.BUILDING THE FRAMEWORK
    3.1 CHOICE OF CURRENCIES
    3.2 DATA ACQUISITION
    3.3 STATES AND REWARDS
    3.4 TECHNICAL INDICATORS
        3.4.1 On-Balance Volume
        3.4.2 Accumulation/Distribution Index
        3.4.3 Chaikin's Oscillator
        3.4.4 Relative Strength Index
        3.4.5 Divergence
        3.4.6 Percentage Change
        3.4.7 Consecutive variation
    3.5 CLUSTERING
        3.5.1 Objective:discretizing the state
        3.5.2 Presentation
        3.5.3 Overview of clustering methods
        3.5.4 Performance metric
        3.5.5 Further clustering choice
        3.5.6 Relative Strength Index clustering
        3.5.7 Clustering example
        3.5.8 Results
        3.5.9 Turning Point Matrix
        3.5.10 Number of states
    3.6 VALUE FUNCTION SELECTION
    3.7 ACTIONS
        3.7.1 Short position
        3.7.2 Asset allocation
    3.8 LEARNING PROCEDURE
        3.8.1 Exploration
        3.8.2 Convergence
        3.8.3 Linear decrementation of ε
        3.8.4 Discontinuous decrementation of ε
        3.8.5 A faster way to reach convergence:dyna
        3.8.6 The role of α
        3.8.7 The role of γ
        3.8.8 Further hyperparameter fine-tuning
        3.8.9 Catastrophic forgetting
        3.8.10 Experience replay
    3.9 STEP BY STEP ALGORITHMIC EXPLANATION
        3.9.1 Initialization
        3.9.2 Selection of current state and action
        3.9.3 Taking action and getting the reward
        3.9.4 Experience replay
        3.9.5 Evaluation of the policy and ε update
4.EXPERIMENT
    4.1 INTRODUCTION
    4.2 BENCHMARK
        4.2.1 Profit and Loss
        4.2.2 Random pick
        4.2.3“Buy and Hold”
    4.3 HYPERPARAMETERS
        4.3.1 Technical analysis
        4.3.2 Reinforcement learning
        4.3.3 Neural network
    4.4 SANITY CHECK
    4.5 DEEP Q-LEARNING–FIRST EXPERIMENT
    4.6 DEEP Q-LEARNING–SECOND EXPERIMENT
        4.6.1 Training
        4.6.2 Testing
    4.7 BENCHMARK COMPARISON
    4.8 RESULTS DISCUSSION
5.CONCLUSIONS
    5.1 RESEARCH RESULTS
    5.2 FURTHER INVESTIGATION
        5.2.1 Fine-tuning difficulties
        5.2.2 Asset allocation
        5.2.3 Computational power
        5.2.4 Number of data points
        5.2.5 State representation
        5.2.6 Volume information
REFERENCES
ACKNOWLEDGEMENTS
APPENDIX A
APPENDIX B
APPENDIX C
APPENDIX D
RESUME



本文編號：3590623