Sparse Coding-Inspired Optimal Trading System for HFT Industry

IEEE Transactions on Industrial Informatics

Volumn 11, Issue 2, 2015, Pages 467-475

  Deng, Yue ^a   Kong, Youyong ^b   Bao, Feng ^c   Dai, Qionghai ^c  

^a NANJING UNIVERSITY   (China)

^b SOUTHEAST UNIVERSITY   (China)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Financial industry; financial signal processing; high frequency trading (HFt); reinforcement learning (RL); sparse coding (SC)

Indexed keywords

AUTOMATION; CODES (SYMBOLS); FINANCE; FINANCIAL MARKETS; KNOWLEDGE BASED SYSTEMS; REINFORCEMENT LEARNING; SIGNAL PROCESSING;

BI-LEVEL OPTIMIZATION; FEATURE REPRESENTATION; FINANCIAL INDUSTRY; FINANCIAL SIGNAL; HIGH-FREQUENCY TRADING; INFORMATION PROCESSING TECHNIQUE; JOINT OPTIMIZATION; SPARSE CODING;

COMMERCE;

EID: 84926479580     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2015.2404299     Document Type: Article

References (36)

1. T. Hendershott, C. M. Jones, and A. J. Menkveld, "Does algorithmic trading improve liquidity?" J. Finance, vol. 66, no. 1, pp. 1-33, 2011.
2. V. Behbood, J. Lu, and G. Zhang, "Fuzzy refinement domain adaptation for long term prediction in banking ecosystem," IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1637-1646, May 2014.
3. Y. Deng, Q. Dai, R. Liu, Z. Zhang, and S. Hu, "Low-rank structure learning via nonconvex heuristic recovery," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 3, pp. 383-396, Jan. 2013.
4. J. Moody and M. Saffell, "Learning to trade via direct reinforcement,"IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 875-889, Jul. 2001.
5. J. Moody, L. Wu, Y. Liao, and M. Saffell, "Performance functions and reinforcement learning for trading systems and portfolios," J. Forecast., vol. 17, no. 56, pp. 441-470, 1998.
6. M. A. Dempster and V. Leemans, "An automated fx trading system using adaptive reinforcement learning," Expert Syst. Appl., vol. 30, no. 3, pp. 543-552, 2006.
7. M. J. Pring, Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points. New York, NY, USA: McGraw-Hill, 1991, vol. 4.
8. G. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, "Machine learning for predictive maintenance: A multiple classifiers approach,"IEEE Trans. Ind. Informat., Aug. 2014.
9. W. E. Vinje and J. L. Gallant, "Sparse coding and decorrelation in primary visual cortex during natural vision," Science, vol. 287, no. 5456, pp. 1273-1276, 2000.
10. B. A. Olshausen and D. J. Field, "Sparse coding with an overcomplete basis set: A strategy employed by v1?" Vis. Res., vol. 37, no. 23, pp. 3311-3325, 1997.
11. T. Bai and Y. Li, "Robust visual tracking using flexible structured sparse representation," IEEE Trans. Ind. Informat., vol. 10, no. 1, pp. 538-547, Feb. 2014.
12. M. Elad, M. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," Proc. IEEE, vol. 98, no. 6, pp. 972-982, Jun. 2010.
13. Y. Deng, Q. Dai, and Z. Zhang, "Graph laplace for occluded face completion and recognition," IEEE Trans. Image Process., vol. 20, no. 8, pp. 2329-2338, Aug. 2011.
14. J. Suo, Y. Deng, L. Bian, and Q. Dai, "Joint non-Gaussian denoising and superresolving of raw high frame rate videos," IEEE Trans. Image Process., vol. 23, no. 3, pp. 1154-1168, Mar. 2014.
15. S. Li, L. D. Xu, and X. Wang, "A continuous biomedical signal acquisition system based on compressed sensing in body sensor networks," IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1764-1771, Aug. 2013.
16. Y. Deng, Y. Liu, Q. Dai, Z. Zhang, and Y. Wang, "Noisy depth maps fusion for multiview stereo via matrix completion," IEEE J. Sel. Topics Signal Process., vol. 6, no. 5, pp. 566-582, Sep. 2012.
17. X. Hu et al., "Robust and accurate transient light transport decomposition via convolutional sparse coding," Opt. Lett., vol. 39, no. 11, pp. 3177-3180, 2014.
18. J. Mairal, F. Bach, and J. Ponce, "Task-driven dictionary learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 791-804, Aug. 2012.
19. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Supervised dictionary learning," in Proc. Adv. Neural Inf. Process. Syst. Conf., 2008, pp. 1033-1040.
20. J. Yang, K. Yu, Y. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR'09), 2009, pp. 1794-1801.
21. G. S. Sivaram, S. K. Nemala, M. Elhilali, T. D. Tran, and H. Hermansky, "Sparse coding for speech recognition," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2010, pp. 4346-4349.
22. J. F. Bard, Practical Bilevel Optimization: Algorithms and Applications. New York, NY, USA: Springer, 1998, vol. 30.
23. L. Ponta, E. Scalas, M. Raberto, and S. Cincotti, "Statistical analysis and agent-based microstructure modeling of high-frequency financial trading,"IEEE J. Sel. Topics Signal Process., vol. 6, no. 4, pp. 381-387, Aug. 2012.
24. R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning. Cambridge, MA, USA: MIT Press, 1998.
25. I. Grondman, L. Busoniu, G. A. Lopes, and R. Babuska, "A survey of actor-critic reinforcement learning: Standard and natural policy gradients,"IEEE Trans. Syst. Man Cybern. C, Appl. Rev., vol. 42, no. 6, pp. 1291-1307, Nov. 2012.
26. G. Tesauro, "Td-gammon, a self-teaching backgammon program, achieves master-level play," Neural Comput., vol. 6, no. 2, pp. 215-219, 1994.
27. th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2002, pp. 259-268.
28. Y. Deng, Y. Li, Y. Qian, X. Ji, and Q. Dai, "Visual words assignment via information-theoretic manifold embedding," IEEE Trans. Cybern., vol. 44, no. 10, pp. 1924-1937, Oct. 2014.
29. B. A. Olshausen et al., "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, no. 6583, pp. 607-609, 1996.
30. H. Lee, A. Battle, R. Raina, and A. Ng, "Efficient sparse coding algorithms,"Adv. Neural Inf. Process. Syst., vol. 19, p. 801, 2007.
31. J. Yang, Z. Wang, Z. Lin, S. Cohen, and T. Huang, "Coupled dictionary training for image super-resolution," IEEE Trans. Image Process., vol. 21, no. 8, pp. 3467-3478, Aug. 2012.
32. P. J. Werbos, "Backpropagation through time: what it does and how to do it," Proc. IEEE, vol. 78, no. 10, pp. 1550-1560, Oct. 1990.
33. M. P. Taylor and H. Allen, "The use of technical analysis in the foreign exchange market," J. Int. Money Finance, vol. 11, no. 3, pp. 304-314, 1992.
34. M. Dempster, T. W. Payne, Y. Romahi, and G. Thompson, "Computational learning techniques for intraday fx trading using popular technical indicators," IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 744-754, Jul. 2001.
35. M. Kearns and Y. Nevmyvaka, High frequency trading-New realities for traders, markets and regulators, in Machine Learning for Market Microstructure and High Frequency Trading. Risk Books, 2013.
36. H. Bessembinder and K. Chan, "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance J., vol. 3, no. 2, pp. 257-284, 1995.