Hamidreza Haddadian - Ph.D. Candidate, Department of Financial Management, Faculty of Management, Central Branch, Islamic Azad University, Tehran, Iran.
Morteza Baky Haskuee - Visiting Professor and Research Fellow, York University, School of Liberal Art and Professional Studies, ON, Canada.
Gholamreza Zomorodian - Assistant Prof., Department of Business Management, Central Branch, Islamic Azad University, Tehran, Iran.

The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These ۴ steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.

portfolio optimization, Artificial intelligence, Algorithmic Trading, trading systems, Genetic Algorithm, Technical Analysis, Neural Network