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研究生:史杜華
研究生(外文):TuWorld Waqaar Slader
論文名稱:外匯趨勢預測
論文名稱(外文):A Moving Average Approach to Predicting Forex Market Price Trends with Machine Learning
指導教授:江政欽江政欽引用關係
指導教授(外文):Cheng-Chin Chiang
口試委員:黃雅軒林信鋒
口試委員(外文):Ya-Xuan HuangXin-Feng Lin
口試日期:2020-07-31
學位類別:碩士
校院名稱:國立東華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:58
中文關鍵詞:移動平均線外匯交易外匯資金價格趨勢預測機器學習
外文關鍵詞:Moving AverageForexForeign ExchangePrice trend predictionMachine Learning
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  • 被引用被引用:0
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  • 下載下載:38
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股票和外匯價格的預測是一個具有挑戰性的問題,吸引了研究人員和投資者的廣泛興趣。儘管專家進行了許多研究來應對這一挑戰,但是仍然存在著應對股票和外匯市場價格波動的問題。本文旨在通過使用移動平均線和機器學習技術來減輕外匯市場的波動性對預測的影響。與第二天或下一小時的預測相反,這種方法還可以對整個價格序列產生整體預測。這項研究驗證了這種新穎方法在實際應用中的有效性,從而使最佳性能達到了86.8%的準確度。與其他方法相比,該方法還顯示出最佳的利潤收益。
Stock and Foreign Exchange price prediction is a challenging problem that attracts broad interests from researchers and investors. Despite many studies to tackle this challenge, issues in dealing with the volatility of stock and Forex market prices still remain. This thesis aims to lessen the effect that the volatile nature of the Forex market imposed on predictions by using Moving Averages accompanied by machine learning techniques. This approach also produces an overall prediction for an entire sequence of prices as opposed to next day or next hour prediction. The study verifies the effectiveness of this novel approach on a real-world application, resulting in the best performance reaching 86.8% accuracy. The proposed method also shows the best profit gains compared to other approaches.
Chapter 1. Introduction ------------------ 1
Chapter 2. Related Work ------------------ 3
Chapter 3.Financial Technical Terms ------ 7
3.1 Financial Indicators ----------------- 7
3.1.1 Relative Strength Index ------------ 9
3.1.2 Moving Averages -------------------- 10
3.1.3 Commodity Channel Index ------------ 12
3.1.4 Momentum --------------------------- 13
3.1.5 Mean Deviation --------------------- 14
3.2 Foreign Exchange technical terms ----- 14
Chapter 4. Recurrent Neural Network ----- 17
4.1 LSTM --------------------------------- 19
4.2 GRU ---------------------------------- 22
Chapter 5. Proposed Method --------------- 25
5.1 Future-Past Moving Averages ---------- 26
5.2 Neural Network Architecture ---------- 29
5.3 Performance Index -------------------- 32
Chapter 6. Dataset ----------------------- 35
6.1 Data selection and collection -------- 35
6.2 Data Split --------------------------- 37
Chapter 7. Performance Evaluation -------- 39
7.1 Training ----------------------------- 39
7.2 Accuracy Evaluation ------------------ 39
7.3 Profitability Evaluation ------------- 41
7.4 Comparison --------------------------- 48
Chapter 8. Conclusion/Future works ------- 55
References ------------------------------- 57
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https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
[2] Bradfield, D. (2019) Forex Vs Stocks: Top Differences and How to Trade Them
https://www.dailyfx.com/education/beginner/forex-vs-stocks.html
[3] Viraf, How (NOT) to Predict Stock Prices with LSTM
https://towardsdatascience.com/how-not-to-predict-stock-prices-with-lstms-a51f564ccbca
[4] Which is Harder to Trade Forex or Stock
https://rockfortmarkets.com/en/which-is-harder-to-trade-forex-or-stocks/
[5] Gregory M., Morris (2006). Candlestick Charting Explained: Timeless Techniques for Trading Stocks and Futures. McGraw-Hill. ISBN 9780071461542.
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https://www.cmcmarkets.com/en/trading-guides/what-are-candlestick-charts
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[9] Mudinas, A., Zhang, D., & Levene, M. (2019) Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward. arXiv preprint arXiv:1903.05440. University of London
[10] Bowling, J. (2019) What Happened When I Tried Market Prediction With Machine Learning
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[11] Nison, S, 2001. Japanese candlestick charting techniques, second edition, Upper Saddle River: Prentice Hall Press,.
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[13] Britz, D. (2015) Recurrent Neural Networks Tutorial, Part 3 – Backpropagation Through Time and Vanishing Gradients
http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
[14] Hochreiter, S. (1998) The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems , 6(02), 107-116.
[15] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555
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