Created by W.Langdon from gp-bibliography.bib Revision:1.7975
HFT systems based on moving averages and a simple trend following system are developed to set benchmarks for the multi-frequency related systems. An experiment on the performance of two-frequency ARIMA model is also conducted to show the prediction power of the multi-frequency analysis, as time series in different resolutions may convey different information on its characteristics, the empirical results indicated that multi-frequency could improve the forecast performance. After that, an intra-day trading system is designed based on the Genetic Programming (GP) and technical analysis, wavelet de-noise is introduced to improve the performance of the GP based system, the system with wavelet de-noise showed best performance in the empirical test. To explore the nonlinear relationship, artificial neural network (ANN) is applied in the prediction of the financial time series. Both Nonlinear Auto-regressive with eXogenous (NARX) and wavelet based Multi-layer perceptron models are used in the forecasting of the intra-day high-frequency time series, based on which, HFT systems are developed. To test the performance of the HFT systems, the China index futures is selected as the experiment asset. Based on the experiments in this thesis, the HMA trading system shows the best performance among the tested moving averages trading systems; the two-frequency ARIMA beats the traditional single frequency models; the GP systems trained using the wavelet de-noised data outperforms the GP systems trained using the original data, and the hard-threshold denoise method provides the best out-of-sample trading performance; the WMLP based trading model outperforms the NARX model in the out-of-sample trading test.",
Genetic Programming entries for Hongguang Liu