Quantitative trading strategy based on Fuzzy control model regulation

Abstract

Quantitative trading is considered as a high complex and challenging area of research, a significant amount of works have been done in this field; generally most of them can be divided into two categories. One is to predict the future trend or price; the other is to construct decision support system which can give certain buy or sell signals. This paper illustrates concretely how to make use of the information emanating from the markets and to transform it to mathematical equations for automatic decision making, and to build and implement systematic computer-based models for quantitative trading; hence, we formulate the market constraints and the global Profit and Loss formula. In this work, we propose a new adaptive prediction strategy using Fuzzy control system regulation combined with standard regression methods of predictability. In order to check the good quality and efficiency of the proposed strategy, calculations of returns, risk, sharpe ratio, and performance are made and proved. This fact is clearly demonstrated in the proposed system. The trading strategy based on the proposed fuzzy prediction model is defined including risk management in addition to market constraints in order to maximize the global Profit and Loss. Finally, the US market is chosen to apply and simulate the proposed method.