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Entropy 2016, 18(12), 435; doi:10.3390/e18120435

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
School of Government, Beijing Normal University, Beijing 100875, China
Author to whom correspondence should be addressed.
Academic Editor: Antonio M. Scarfone
Received: 13 October 2016 / Revised: 24 November 2016 / Accepted: 1 December 2016 / Published: 6 December 2016
(This article belongs to the Collection Advances in Applied Statistical Mechanics)
View Full-Text   |   Download PDF [536 KB, uploaded 6 December 2016]   |  


Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP) was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT) method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI) 300 futures in the emerging China Financial Futures Exchange (CFFEX) market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models. View Full-Text
Keywords: genetic programming; intra-day trading; wavelet de-noise; technical analysis; CSI 300 index genetic programming; intra-day trading; wavelet de-noise; technical analysis; CSI 300 index

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, H.; Ji, P.; Jin, J. Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming. Entropy 2016, 18, 435.

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