High-frequency trading strategy based on deep neural networks pdf

SH 33 SH 34 SH 35 SH 36 SH 37 SH 38 SH 39 SH 40 SH 41 SH 42 SH Table 1.

The sample stocks and their number of increasing directions and decreasing directions. Table 2. Figure 2. Rolling segmentation on training set and testing set. The green bar represents the entire dataset, the blue bar represents the training set for a round experiment, and the yellow bar represents the corresponding testing set.

Table 3.


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Network architecture of discriminative model. Table 4. These figures are the average values over the 42 stocks. Table 5.

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

Table 6. Table 7.

Computational Intelligence in Data-Driven Modelling and Its Engineering Applications

Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9.

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Figure References R. Al-Hmouz, W. Pedrycz, and A. Barak and M. Booth, E. Gerding, and F. Bagheri, H. Mohammadi Peyhani, and M. Son, D. Noh, and J. Aldridge and S. De Oliveira, C. Nobre, and L. Li, X. Huang, X. Deng, and S. Wang, S. Bao, and J. Chong, C. Han, and F. Goodfellow, J.

Pouget-Abadie, M. Mirza et al. View at: Google Scholar S. Iizuka, E. Simo-Serra, and H. Luc, C. Couprie, S. Chintala, and J. Verbeek, Semantic segmentation using adversarial networks, arXiv preprint , arXiv, Mathieu, C. Couprie, and Y. LeCun, Deep multi-scale video prediction beyond mean square error, arXiv preprint , arXiv, Hamilton, Time Series Analysis , vol. View at: MathSciNet R. Shumway and D. Brockwell and R. Pellegrini, E. Ruiz, and A. Kara, M. Ghiassi, J.

Skinner, and D. Huang, Y. Nakamori, and S. Elsir, and H. Majhi, G. Panda, G. Sahoo, A. Panda, and A. Thavaneswaran, K. Thiagarajah, and S. Carlsson and R. Thavaneswaran, S. Appadoo, and A. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Rather, A. Agarwal, and V. Oliveira, and S. Chen, K. Xiao, J. Sun, and S. Yoshihara, K. Fujikawa, K. Our main contributions are summarized as follows: i We establish several high frequency technical indicators and investigate the statistically and trading significant in-sample and out-of-sample predictive power for each indicators.

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High-Frequency trading strategy based on deep neural networks

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    High-Frequency Trading Strategy Based on Deep Neural Networks

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