Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming
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- @Article{journals/entropy/LiuJJ16,
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author = "Hongguang Liu and Ping Ji and Jian Jin2",
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title = "Intra-Day Trading System Design Based on the
Integrated Model of Wavelet De-Noise and Genetic
Programming",
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journal = "Entropy",
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year = "2016",
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number = "12",
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volume = "18",
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pages = "435",
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keywords = "genetic algorithms, genetic programming, intra-day
trading, wavelet de-noise, technical analysis, CSI 300
index",
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bibdate = "2017-05-26",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/entropy/entropy18.html#LiuJJ16",
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DOI = "doi:10.3390/e18120435",
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size = "16 pages",
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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",
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notes = "Department of Industrial and Systems Engineering, The
Hong Kong Polytechnic University, Hong Kong 999077,
China",
- }
Genetic Programming entries for
Hongguang Liu
Ping Ji
Jian Jin2
Citations