Next Article in Journal
Numerical Study of Entropy Generation in Mixed MHD Convection in a Square Lid-Driven Cavity Filled with Darcy–Brinkman–Forchheimer Porous Medium
Next Article in Special Issue
Quadratic Mutual Information Feature Selection
Previous Article in Journal
Entropy and Stability Analysis of Delayed Energy Supply–Demand Model
Previous Article in Special Issue
Bounds on Rényi and Shannon Entropies for Finite Mixtures of Multivariate Skew-Normal Distributions: Application to Swordfish (Xiphias gladius Linnaeus)
Open AccessArticle

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

by Hongguang Liu 1,*, Ping Ji 1 and Jian Jin 2
1
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
2
School of Government, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio M. Scarfone
Entropy 2016, 18(12), 435; https://doi.org/10.3390/e18120435
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop