Title :
Mining Better Technical Trading Strategies with Genetic Algorithms
Author :
Ni, Jiarui ; Zhang, Chengqi
Author_Institution :
Univ. of Technol., Sydney, NSW
Abstract :
Technical analysis is one of the two main schools of thought in the analysis of security prices. It is widely believed and applied by many professional and amateur traders. However, it is often criticized for lacking scientific rigour or worse, for lacking any basis whatsoever. We propose to explore the feasibility and/or limitation of technical analysis by the optimization of technical trading strategies over historical stock data with genetic algorithms. This paper presents the optimization problem in detail and discusses the potential problems to be tackled during the optimization. Preliminary experiments show that it can identify the limitations quickly
Keywords :
data mining; genetic algorithms; pricing; data mining; genetic algorithm; historical stock data; optimization problem; security prices; technical analysis; technical trading strategy; Algorithm design and analysis; Australia; Economic forecasting; Educational institutions; Genetic algorithms; History; Pattern analysis; Profitability; Security; Stock markets;
Conference_Titel :
Integrating AI and Data Mining, 2006. AIDM '06. International Workshop on
Conference_Location :
Hobart, Tas.
Print_ISBN :
0-7695-2730-2
DOI :
10.1109/AIDM.2006.12
