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| Title: | 隨機森林及深度強化學習在台指期交易策略之應用
The application of random forest and Deep reinforcement learning in TAIEX future trading strategies |
| Authors: | 葉峰銘
Yeh, Fong-Ming |
| Contributors: | 陳威光
蔡瑞煌
葉峰銘
Yeh, Fong-Ming |
| Keywords: | 台灣加權股價指數期貨
隨機森林
深度強化學習
TAIEX futures
Random forest
Deep reinforcement learning |
| Date: | 2019 |
| Issue Date: | 2019-08-07 16:11:51 (UTC+8) |
| Abstract: | 近年來有許多論文嘗試以不同的機器學習演算法建立模型應用在交易上，並且取得不錯的成效。本次研究採用了機器學習二大分支中的兩種演算法，分別為監督式學習中的隨機森林及深度強化學習來建構台灣加權股價指數期貨交易策略。
本次研究將2007年7月2日至2019年3月31日的期貨價格、技術面及籌碼面等相關資料分為訓練、驗證及測試區間，以訓練區間資料作為訓練樣本，訓練隨機森林及深度強化學習模型，並以樣本外的驗證及測試區間驗證其績效。
在隨機森林模型之中我們輸入了62項特徵進行訓練，結果在驗證區間年化報酬率為13.06%、在測試區間年化報酬率則為12.32%。此外，我們透過隨機森林模型篩選特徵重要性，發現外資籌碼面指標占重要性前10名當中的4名，自營籌碼面指標則占前10名當中的5名，其中外資台指期貨多空交易淨額、自營金融期貨多空交易淨額及自營台指期貨多空交易淨額特徵重要性排名分別為第1、第2及第3。在深度強化學習模型之中，我們額外輸入了隨機森林篩選過的特徵進行訓練。結果在驗證區間年化報酬率為0.50%、在測試區間年化報酬率則為5.03%。
In recent years, there are many researches have tried to build trading strategies by different machine learning algorithms and achieved good results. In this paper, we use random forest and deep reinforcement learning which belong two branch of machine learning to construct our models for TAIEX future trading strategies.
In this paper, we use the relevant data of TAIEX future including price, technical indicators and chip factors from July 2, 2007 to March 31, 2019 and divide the data into training, validation and test data set. The training data set is used to train the models. The validation and test data set are used to verify the performance.
In this paper, we input 62 features to train the random forest model, as a result, the annualized rate of return is in validation data set is 13.06%, and the annualized return rate in the test data set is 12.32%. In addition, we use the random forest model to rank the importance of features and find that factors of foreign investors chip account for 4 of the top 10 and factors of dealer chip account for 5 of the top 10. The feature importance of foreign investors TAIEX future volume net, dealer finance future volume net and dealer TAIEX future volume net is ranked first, second and third respectively. Beside, we additionally input top 10 important feature to train the deep reinforcement learning model, as a result, the annualized rate of return is in validation data set is 0.5%, and the annualized return rate in the test data set is 5.03%. |
| Reference: | Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046-7056.
Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47
Bengio, Y. O. S. H. U. A. (1997). Training a neural network with a financial criterion rather than a prediction criterion. In Decision Technologies for Financial Engineering: Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (NNCM’96), World Scientific Publishing (pp. 36-48).LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Brooks, C., Rew, A. G., & Ritson, S. (2001). A trading strategy based on the lead–lag relationship between the spot index and futures contract for the FTSE 100. International Journal of Forecasting, 17(1), 31-44
Blume, L., Easley, D., & O`hara, M. (1994). Market statistics and technical analysis: The role of volume. The Journal of Finance, 49(1), 153-181.
Deng, Yue, et al. "Deep direct reinforcement learning for financial signal representation and trading." IEEE transactions on neural networks and learning systems 28.3 (2017): 653-664.
Díaz-Uriarte, R., & De Andres, S. A. (2006). Gene selection and classification of microarray data using random forest. BMC bioinformatics, 7(1), 3.
Khaidem, L., Saha, S., & Dey, S. R. (2016). Predicting the direction of stock market prices using random forest. arXiv preprint arXiv1605.00003.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The review of financial studies, 1(1), 41-66.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The journal of finance, 55(4), 1705-1765.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Petersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529.
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE transactions on neural Networks, 12(4), 875-889.
Moody, J., Wu, L., Liao, Y., & Saffell, M. (1998). Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting, 17(5‐6), 441-470.
Neely, C., Weller, P., & Dittmar, R. (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of financial and Quantitative Analysis, 32(4), 405-426.
Neuneier, R. (1998). Enhancing Q-learning for optimal asset allocation. In Advances in neural information processing systems(pp. 936-942).
Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012, July). How many trees in a random forest?. In International workshop on machine learning and data mining in pattern recognition (pp. 154-168). Springer, Berlin, Heidelberg.
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), e0155133.
Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258-273.
Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. |
| Description: | 碩士
國立政治大學
金融學系
106352032 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106352032 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900217 |
| Appears in Collections: | [金融學系] 學位論文
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