A dynamic stock trading system based on a Multi-objective Quantum-Inspired Tabu Search algorithm

Recently evolutionary algorithms, such as the Genetic Algorithm (GA), Genetic Programming (GP) and Particle Swarm Optimization (PSO), have become common approaches used in financial applications to address stock trading problems. In this paper, we propose a novel method called the Multi-objective Quantum-inspired Tabu Search (MOQTS) algorithm, which can be applied in a stock trading system. Determining the best time to buy and sell in the stock market and maximizing profits while incurring fewer risks are important issues in financial research. In order to identify ideal trading points, the proposed trading system uses various kinds of technical indicators as trading rules in order to cope with different stock situations. The proposed algorithm is used to identify the optimal combination of trading rules as our trading strategy. Moreover, it makes use of a sliding window in order to avoid the major problem of over-fitting. In the experiment, the algorithm uses both profit earned and other aspects, such as successful transaction rate and standard deviation, to analyze this system. The experimental results, in relation to profit earned and successful transaction rates in the U.S.A stock market, outperform both the traditional method and the Buy & Hold method which are common benchmarks in the field.