Optimization of Trading Strategies with Genetic Algorithms and Wavelet Transform in the Hong Kong Stock Option

V. Ng and K.L. Lee (PRC)


Stock Option Trading, Genetic Algorithms, Wavelet Transformation


One important class of financial information is financial time series. Financial time series are time-varying, non linear in the data distribution and often exhibit irregularity or spikes. This paper aims at developing a stock option trading data mining system to provide optimal trading strategies. It applies genetic algorithms to explore optimality over a set of option trading rules and wavelet transform to capture high frequencies behavior in financial time series. The resulted trading strategy is searched with the goal of maximizing profit and minimizing risk. We have applied our system to the Hong Kong stock market with the option and stock daily closing trade data which are available in the Internet.

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