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

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{journals/entropy/LiuJJ16,
  author =       "Hongguang Liu and Ping Ji and Jian Jin2",
  title =        "Intra-Day Trading System Design Based on the
                 Integrated Model of Wavelet De-Noise and Genetic
                 Programming",
  journal =      "Entropy",
  year =         "2016",
  number =       "12",
  volume =       "18",
  pages =        "435",
  keywords =     "genetic algorithms, genetic programming, intra-day
                 trading, wavelet de-noise, technical analysis, CSI 300
                 index",
  bibdate =      "2017-05-26",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.3390/e18120435;
                 DBLP,
                 http://dblp.uni-trier.de/db/journals/entropy/entropy18.html#LiuJJ16",
  DOI =          "doi:10.3390/e18120435",
  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 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",
}

Genetic Programming entries for Hongguang Liu Ping Ji Jian Jin2

Citations