Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65858
Title: Intra-day trading system design based on the integrated model of wavelet de-noise and genetic programming
Authors: Liu, H
Ji, P
Jin, J
Keywords: CSI 300 index
Genetic programming
Intra-day trading
Technical analysis
Wavelet de-noise
Issue Date: 2016
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Entropy, 2016, v. 18, no. 12, 435 How to cite?
Journal: Entropy 
Abstract: 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 theWavelet 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.
URI: http://hdl.handle.net/10397/65858
EISSN: 1099-4300
DOI: 10.3390/e18120435
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