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 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
... idered 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. Entropy 2016, 18, 435 2 of 16 to extract the real characteristics of the time series. To overcome this problem, in this paper, all the models are trained using the intra-day data. Information that has a dramatic impact on the market is expected to be digested during the market closed periods, therefore, jumping points barely existed in the intra-day data. True trends of the time series can be recognized with GP models trained using the intra-day data. Another innovation point of this paper is the noise reduction of the intra-day data. High frequency intra-day financial time series are highly correlated and unstationary. Wavelet de-noise is applied in this paper to handle the noises. Wavelet transformation leads to a sparse representation for many real-world signals, and many features in different scales of the data can be localized. Wavelet transformation concentrates the signal in a few large-magnitude wavelet coefficients. Wavelet coefficients which are small in value are typically noise and can be removed while preserving important signal features. Both the soft-thresholding and the hard-thresholding methods are used in this paper to de-noise data. Optimized GP models based on the original data and the proposed wavelet de-noised data are compared with each other using the out-of-sample performance. In this paper, the CSI 300 index future is selected as the target training and testing asset. The index future market of China is one of the most actively traded emerging markets. Past studies on index futures usually use the frequency of a day, a week or even a month, some of which indeed showed outstanding performance in the out-of-sample experiments. However, these kinds of theoretical profits are highly unrealistic in the real markets; that is because most of the trades in the future markets are margin trading and small movements of the stock index might lead to a large amount of gains or losses to the investors. For most of the time, investors do not have enough money to wait for the market to go to the right price that their model expected. The proposed model in this paper is trained using the intra-day 1-minute interval data. For each trading day, the data is separated into two different parts: the first part is used to train the GP model and the second part is used to perform the out-of-sample performance. In this way, no open position is held overnight, which could greatly reduce the risk caused by overnight unfavorable price jumps. The remainder of this paper is organized as follows. Relevant studies are briefly reviewed in Section 2. In Section 3, the wavelet de-noise and genetic programming based approach are elaborated and the design of the experiment is explained. Section 4 presents the empirical experiment. This paper concludes in Section 5.