Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming
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...
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doaj-2b9c8850b5124b0b8e31a7cbcefa94d22020-11-24T22:56:51ZengMDPI AGEntropy1099-43002016-12-01181243510.3390/e18120435e18120435Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic ProgrammingHongguang Liu0Ping Ji1Jian Jin2Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaSchool of Government, Beijing Normal University, Beijing 100875, ChinaTechnical 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.http://www.mdpi.com/1099-4300/18/12/435genetic programmingintra-day tradingwavelet de-noisetechnical analysisCSI 300 index |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongguang Liu Ping Ji Jian Jin |
spellingShingle |
Hongguang Liu Ping Ji Jian Jin Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming Entropy genetic programming intra-day trading wavelet de-noise technical analysis CSI 300 index |
author_facet |
Hongguang Liu Ping Ji Jian Jin |
author_sort |
Hongguang Liu |
title |
Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming |
title_short |
Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming |
title_full |
Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming |
title_fullStr |
Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming |
title_full_unstemmed |
Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming |
title_sort |
intra-day trading system design based on the integrated model of wavelet de-noise and genetic programming |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2016-12-01 |
description |
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. |
topic |
genetic programming intra-day trading wavelet de-noise technical analysis CSI 300 index |
url |
http://www.mdpi.com/1099-4300/18/12/435 |
work_keys_str_mv |
AT hongguangliu intradaytradingsystemdesignbasedontheintegratedmodelofwaveletdenoiseandgeneticprogramming AT pingji intradaytradingsystemdesignbasedontheintegratedmodelofwaveletdenoiseandgeneticprogramming AT jianjin intradaytradingsystemdesignbasedontheintegratedmodelofwaveletdenoiseandgeneticprogramming |
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1725653021878124544 |