Building a trade system by genetic algorithm and technical analysis for thai stock index

Abstract

Recent studies in financial markets suggest that technical analysis can be a very useful tool in predicting the trend. Trading systems are widely used for market assessment. This paper employs a genetic algorithm to evolve an optimized stock market trading system. Our proposed system can decide a trading strategy for each day and produce a high profit for each stock. Our decision-making model is used to capture the knowledge in technical indicators for making decisions such as buy, hold and sell. The system consists of two stages: elimination of unacceptable stocks and stock trading construction. The proposed expert system is validated by using the data of 5 stocks that publicly traded in the Thai Stock Exchange-100 Index from the year 2010 through 2013. The experimental results have shown higher profits than "Buy & Hold" models for each stock index, and those models that included a volume indicator have profit better than other models. The results are very encouraging and can be implemented in a Decision- Trading System during the trading day. © 2014 Springer International Publishing Switzerland.